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SanYu Research Engine

by com.wentangkeji.sanyu
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106 deterministic medical research engines: ROC/AUC, survival, Table 1, DEG, pathway enrichment, primer design, literature search. Every response carries engine_status — auditable, not LLM guessing.

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Status
Healthy
Uptime
100.0% over the last 24 hours
Last Tested
Transport
Streamable HTTP · MCP 2025-06-18
URL
ChatGPT
–
Claude
–

TDQS

B3.1/5.0

Scored across 108 tools

Disambiguation3/5

Tools span many domains and most have detailed USE WHEN guidance and aliases, but several clusters overlap: kaplan_meier vs survival_cox vs animal_survival_km, pk_nca vs pk_pd_assess vs pkpd_simulate, toxicity_predict vs toxicology_assess, and arrive_checklist vs reporting_checklist. These overlaps are usually resolvable from descriptions, but selection still requires careful reading.

Naming Consistency4/5

Names are consistently snake_case and often domain-prefixed (animal_, metabolite_, scrna_, protein_, qpcr_), which makes the set predictable. There is some verb-first vs noun-first variation and a few special meta-tools (sanyu_*), but no chaotic camelCase or naming styles.

Tool Count1/5

With 108 tools, the surface is far beyond a well-scoped size and creates a very large selection burden for an agent. Even with sanyu_list_actions as a catalogue, the count is excessive for practical tool choice.

Completeness4/5

The surface covers a broad research lifecycle: literature search, evidence synthesis, study design, statistics, omics, animal/pharma workflows, reporting checklists, and manuscript helpers. Minor gaps remain in generic data manipulation/plotting and reference-management workflows, but major workflows are represented.

Available Tools

108 tools
animal_dose_volumeAnimal dose & injection volume / 动物给药量计算B
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Animal dose & injection volume / 动物给药量计算 | USE WHEN: compute the dose (mg) and injection volume (mL) for an animal from body weight and mg/kg | ALIASES: 动物给药量, 给药体积, 按体重给药, dose volume, mg/kg, 注射体积, 灌胃体积 | RETURNS: 给药量与体积

NameRequiredDescriptionDefault
dose_mg_per_kgYes剂量(mg/kg)
animal_weight_gYes动物体重(g)
animal_randomizationAnimal randomization / 动物随机分组A
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Animal randomization / 动物随机分组 | USE WHEN: randomly assign animals to groups as required by ARRIVE | ALIASES: 动物随机分组, animal randomization, 分组表, 动物分组 | RETURNS: 分组表

NameRequiredDescriptionDefault
seedNo种子
groupsNo分组名
methodNo随机化方法
n_totalYes动物总数
animal_sample_sizeAnimal sample size (3R) / 动物实验样本量A
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Animal sample size (3R) / 动物实验样本量 | USE WHEN: how many animals per group are needed (power-based, ARRIVE/3R wording) | ALIASES: 动物样本量, 动物要多少只, 每组几只, animal sample size, 3R, power animals | RETURNS: 每组动物数

NameRequiredDescriptionDefault
alphaNoα
powerNo把握度
groupsNo组数
effect_sizeYes效应量
animal_survival_kmAnimal survival analysis / 动物生存分析A
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Animal survival analysis / 动物生存分析 | USE WHEN: Kaplan-Meier and log-rank on animal death/survival times | ALIASES: 动物生存分析, 生存曲线, log-rank, animal survival, KM, 死亡时间 | RETURNS: KM 曲线与 log-rank

NameRequiredDescriptionDefault
ctrl_timesYes对照组时间
ctrl_eventsYes对照组事件
treat_timesYes给药组时间
treat_eventsYes给药组事件
arrive_checklistARRIVE 2.0 checklist / ARRIVE 2.0 检查A
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ARRIVE 2.0 checklist / ARRIVE 2.0 检查 | USE WHEN: check an animal experiment against the ARRIVE 2.0 reporting guideline | ALIASES: ARRIVE, 动物实验规范, 动物伦理报告, animal reporting, ARRIVE 2.0 | RETURNS: ARRIVE 条目核对

NameRequiredDescriptionDefault

No parameters

atac_diffATAC-seq differential accessibility / ATAC 差异可及性A
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ATAC-seq differential accessibility / ATAC 差异可及性 | USE WHEN: differential open regions from ATAC-seq counts | ALIASES: ATAC, 差异可及性, 染色质开放, differential accessibility, peak差异 | RETURNS: 差异 peak

NameRequiredDescriptionDefault
data_jsonYes必填。数据对象（不是字符串）。矩阵形式：{"matrix": [[行1各列数值...], [行2...]], "rows": ["基因/特征名"], "cols": ["样本名"]}；组学结果另可带 {"groups": ["组A","组A","组B"...]}（与 cols 一一对应）、{"p_values": [...], "log2fc": [...]}（若已有差异结果）。实测缺此字段会被拒：Field required: data_json
bioequivalence_compareBioequivalence (BE) / 生物等效性评价B
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Bioequivalence (BE) / 生物等效性评价 | USE WHEN: compare test vs reference AUC/Cmax and judge 80-125% bioequivalence with 90% CI | ALIASES: 生物等效性, BE, AUC Cmax, 90%CI, 等效性判定, bioequivalence | RETURNS: 90%CI 与等效性判定

NameRequiredDescriptionDefault
test_auc_listYes受试制剂 AUC
reference_auc_listYes参比制剂 AUC
calibration_curveCalibration curve (prediction model) / 校准曲线A
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Calibration curve (prediction model) / 校准曲线 | USE WHEN: assess calibration of a risk model and get the curve plus Brier score | ALIASES: 校准曲线, 校准度, Brier, calibration, 预测模型校准, 可靠性曲线 | RETURNS: 校准曲线与 Brier 分数

NameRequiredDescriptionDefault
labelsYes真实标签
n_binsNo分箱数
scoresYes预测概率（0~1）
cell_assay_calcCell assay calculator / 细胞实验通用计算B
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Cell assay calculator / 细胞实验通用计算 | USE WHEN: CCK8 / wound healing / Transwell / colony formation calculations (pick tool_id: cck8, wound-healing, transwell, colony) | ALIASES: CCK8, 划痕实验, Transwell, 克隆形成, 细胞增殖, 迁移实验, cell viability, wound healing | RETURNS: 计算结果

NameRequiredDescriptionDefault
paramsNo该工具所需参数
tool_idYes工具 id，如 cck8/wound-healing/transwell/colony
chip_peak_callingChIP-seq peak calling / ChIP-seq Peak callingA
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ChIP-seq peak calling / ChIP-seq Peak calling | USE WHEN: call peaks from ChIP-seq reads | ALIASES: ChIP-seq, peak calling, 峰识别, 转录因子结合, ChIP | RETURNS: peak 清单

NameRequiredDescriptionDefault
data_jsonYes必填。数据对象（不是字符串）。矩阵形式：{"matrix": [[行1各列数值...], [行2...]], "rows": ["基因/特征名"], "cols": ["样本名"]}；组学结果另可带 {"groups": ["组A","组A","组B"...]}（与 cols 一一对应）、{"p_values": [...], "log2fc": [...]}（若已有差异结果）。实测缺此字段会被拒：Field required: data_json
citation_formatCitation formatting / 引文格式化A
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Citation formatting / 引文格式化 | USE WHEN: reformat references to Vancouver / APA / GB-T 7714 | ALIASES: 参考文献格式, 引文格式, Vancouver, APA, GB/T 7714, citation, 文献格式 | RETURNS: 格式化后的引文

NameRequiredDescriptionDefault
styleNovancouver/apa/mla/chicago/gbt7714
referencesYes参考文献列表
citation_verifyVerify citation authenticity / 引文真实性核验A
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Verify citation authenticity / 引文真实性核验 | USE WHEN: check whether references are fabricated (existence check against the local corpus) | ALIASES: 核验参考文献, 引用是真的吗, 假文献, citation verify, 参考文献真实性, hallucinated reference | RETURNS: 逐条核验结果

NameRequiredDescriptionDefault
textYes含引用的正文
topicNo主题
classification_metricsClassification metrics / 分类性能报告A
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Classification metrics / 分类性能报告 | USE WHEN: full diagnostic performance set: accuracy, sensitivity, specificity, PPV, NPV, F1 | ALIASES: 分类性能, 准确率, 敏感度, 特异度, PPV, NPV, confusion matrix, 诊断指标 | RETURNS: 分类指标全套

NameRequiredDescriptionDefault
labelsYes真实标签
thresholdNo判定阈值
predictionsYes预测结果（0/1）
clinical_basic_bridgeClinical ↔ basic translation bridge / 临床↔基础转化配对A
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Clinical ↔ basic translation bridge / 临床↔基础转化配对 | USE WHEN: from a clinical finding propose mechanistic hypotheses and experiments (or the reverse) | ALIASES: 转化研究, 临床到基础, 机制假说, bench to bedside, Bridge, 实验方案建议 | RETURNS: 转化研究假说与实验方案

NameRequiredDescriptionDefault
findingYes临床发现/基础发现
directionNoclinical2basic 或 basic2clinical
cloning_designMolecular cloning design / 克隆方案设计A
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Molecular cloning design / 克隆方案设计 | USE WHEN: design a plasmid construct: homology arms, primers, assembly strategy | ALIASES: 分子克隆, 构建质粒, 同源臂, cloning, Gibson, 酶切连接, 引物设计克隆 | RETURNS: 克隆方案与引物

NameRequiredDescriptionDefault
methodNoGibson / Golden Gate / 酶切连接
insert_seqYes插入片段序列（≥40bp）
vector_seqYes载体序列
consort_flowchartCONSORT flow diagram / CONSORT 流程图A
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CONSORT flow diagram / CONSORT 流程图 | USE WHEN: draw a CONSORT participant flow diagram from screening/randomization/loss counts | ALIASES: CONSORT流程图, 流程图, 入组流程, flow diagram, 筛选失访 | RETURNS: CONSORT 流程图

NameRequiredDescriptionDefault
titleNo研究题目
screenedNo筛选人数
randomizedNo随机化人数
allocated_controlNo对照组分配数
excluded_screeningNo筛除人数
allocated_interventionNo干预组分配数
lost_to_followup_controlNo对照组失访数
lost_to_followup_interventionNo干预组失访数
crf_sdtm_validateCRF / SDTM validation / CRF 数据校验（SDTM）A
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CRF / SDTM validation / CRF 数据校验（SDTM） | USE WHEN: check whether CRF data meets CDISC SDTM conventions before export | ALIASES: CRF校验, SDTM, CDISC, 数据合规, 导出前检查, CRF validate, data standards | RETURNS: 校验结果

NameRequiredDescriptionDefault
form_idNo表单 id
dataframe_parseParse pasted data / infer column types / 数据解析与列类型识别A
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Parse pasted data / infer column types / 数据解析与列类型识别 | USE WHEN: the user pasted a CSV/table blob: get shape, column types, missingness before analysis | ALIASES: 解析数据, 列类型, 粘贴表格, CSV解析, parse data, column types, 数据形状 | RETURNS: 行列数、列类型、缺失情况

NameRequiredDescriptionDefault
csv_dataNoCSV 文本
dataset_columnsList data columns / 读取数据列名A
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List data columns / 读取数据列名 | USE WHEN: list the variables in an uploaded data file (do this before Table 1 / modeling so you use real column names) | ALIASES: 列名, 有哪些变量, 字段名, columns, variables, 变量清单, 表头 | RETURNS: 列名与类型

NameRequiredDescriptionDefault
file_idYes数据文件 id
dataset_profileDataset profile / 数据快速画像A
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Dataset profile / 数据快速画像 | USE WHEN: first look at a data file: distributions, missingness, outliers, data quality overview | ALIASES: 数据长什么样, 描述统计, 缺失情况, 数据质量, data profile, descriptive, summary stats, EDA | RETURNS: 描述统计与质量概览

NameRequiredDescriptionDefault
file_idYes数据文件 id
study_designNo研究设计
delong_auc_compareDeLong test (compare two AUCs) / 两条 ROC 的 AUC 比较（DeLong）A
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DeLong test (compare two AUCs) / 两条 ROC 的 AUC 比较（DeLong） | USE WHEN: compare two ROC AUCs from the same subjects and test whether the difference is significant | ALIASES: DeLong, 两个AUC比较, 哪个指标更好, AUC差异, compare AUC, 曲线下面积比较 | RETURNS: 两 AUC 及差值 P 值

NameRequiredDescriptionDefault
labelsYes真实标签 0/1
scores1Yes指标 1 的分数
scores2Yes指标 2 的分数
design_gate_rulesDesign gate rules / 方案设计闸门规则A
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Design gate rules / 方案设计闸门规则 | USE WHEN: explain why a design step is blocked, or what the platform requires for each step (hard gates G1/G2/G3) | ALIASES: 方案设计闸门, 为什么不让过, 硬性要求, gate rules, validation rules, why blocked | RETURNS: 每步是否有硬闸门、三道跨步闸门 G1/G2/G3 的原文规则

NameRequiredDescriptionDefault

No parameters

design_step_contextDesign step context / 取某一步的上下文A
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Design step context / 取某一步的上下文 | USE WHEN: load what the user already answered for a design step (PICO / design type / population) before drafting the next step | ALIASES: 方案设计上下文, 上一步内容, 已经填过的信息, design context, previous step, project context | RETURNS: 该步骤可用的前序上下文（pico / design_type / population / topic）

NameRequiredDescriptionDefault
stepYes要看哪一步的上下文（数据编号 1-11）
project_idYes课题 id
differential_expressionDifferential expression (limma-style) / 差异表达分析B
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Differential expression (limma-style) / 差异表达分析 | USE WHEN: differential genes from an expression matrix (with volcano plot input) | ALIASES: 差异表达, 差异基因, DEG, differential expression, 火山图, 表达矩阵, limma | RETURNS: 差异基因清单

NameRequiredDescriptionDefault
group_aNoA 组样本表达（CSV）
group_bNoB 组样本表达（CSV）
gene_namesNo基因名列表
pval_thresholdNoP 阈值
log2fc_thresholdNolog2FC 阈值
dna_translateDNA → protein translation / 序列翻译A
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DNA → protein translation / 序列翻译 | USE WHEN: translate a nucleotide sequence into amino acids in all three frames | ALIASES: 序列翻译, 核酸转蛋白, 翻译, translate, DNA to protein, 氨基酸序列, ORF翻译 | RETURNS: 氨基酸序列

NameRequiredDescriptionDefault
sequenceYes核酸序列
drug_interaction_checkDrug-drug interaction check / 药物相互作用检查A
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Drug-drug interaction check / 药物相互作用检查 | USE WHEN: can these two drugs be used together? interaction severity and mechanism | ALIASES: 药物相互作用, 能不能一起用, 配伍禁忌, DDI, drug interaction, 相互作用 | RETURNS: 相互作用等级与机制

NameRequiredDescriptionDefault
drug_aYes药物 A
drug_bYes药物 B
dunn_posthocDunn post-hoc test / 多组非参数两两比较（Dunn）A
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Dunn post-hoc test / 多组非参数两两比较（Dunn） | USE WHEN: pairwise comparisons after Kruskal-Wallis (nonparametric, multiple groups) | ALIASES: Dunn, 非参数两两比较, Kruskal-Wallis后续, post hoc, 多重比较 | RETURNS: 两两比较 P 值

NameRequiredDescriptionDefault
groupsYes{组名: [数值...]}
egger_bias_testEgger's test (publication bias) / Meta 发表偏倚（Egger）A
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Egger's test (publication bias) / Meta 发表偏倚（Egger） | USE WHEN: test publication bias in a meta-analysis and get funnel-plot inputs | ALIASES: Egger, 发表偏倚, 漏斗图, publication bias, meta回归, funnel plot | RETURNS: Egger 检验 P 与截距

NameRequiredDescriptionDefault
log_orYes各研究 logOR
se_log_orYes各研究 SE
evidence_followupEvidence follow-up Q&A / 证据追问（多轮）B
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Evidence follow-up Q&A / 证据追问（多轮） | USE WHEN: multi-turn follow-up on an evidence card (causal judgement) | ALIASES: 证据追问, 继续追问, 证据卡, evidence followup, 因果判断 | RETURNS: 追问答复

NameRequiredDescriptionDefault
roundNo轮次
titleYes标题
abstractNo摘要
questionYes追问问题
evidence_intakeStructured evidence intake / 文献证据分级录入B
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Structured evidence intake / 文献证据分级录入 | USE WHEN: decompose a paper into a structured evidence card by evidence level (Decision OS) | ALIASES: 证据分级, 证据卡, 文献拆解, evidence level, GRADE, 证据录入 | RETURNS: 结构化证据卡

NameRequiredDescriptionDefault
doiNoDOI
titleYes标题
abstractNo摘要
literature_typeNo文献类型
evidence_reportEvidence report narrative / 证据报告叙述B
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Evidence report narrative / 证据报告叙述 | USE WHEN: turn evidence cards into a readable report | ALIASES: 证据报告, 证据综述, evidence report, 证据叙述 | RETURNS: 证据报告正文

NameRequiredDescriptionDefault
langNo可选。报告语言：zh（默认，中文）或 en（英文）。其他值按 zh 处理。
resultYes必填。上一次 act_decision_intake / act_decision_followup 返回的 result 对象，原样传入即可（内含 causality / drug_stage / evidence_chain 等字段）。实测缺此字段返回：缺少决策结果 result
fibrosis_stagingLiver fibrosis staging / 纤维化分期A
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Liver fibrosis staging / 纤维化分期 | USE WHEN: map fibrosis scores to Ishak / METAVIR stage | ALIASES: 纤维化分期, Ishak, METAVIR, 肝纤维化, fibrosis stage, 病理分期 | RETURNS: 分期结论与解读

NameRequiredDescriptionDefault
stageNo分期数
methodNo分期体系：ishak/metavir/nas
figure_legendFigure legend writer / 图注生成A
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Figure legend writer / 图注生成 | USE WHEN: write a figure legend for a given chart type | ALIASES: 图注, Figure legend, 图片说明, 图例, 写图注 | RETURNS: 图注文本

NameRequiredDescriptionDefault
contextNo图的背景与数据
chart_typeYes图类型
flow_clusterFlow cytometry clustering / 流式细胞聚类A
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Flow cytometry clustering / 流式细胞聚类 | USE WHEN: unsupervised clustering of flow cytometry data into cell populations | ALIASES: 流式聚类, 无监督分群, flow clustering, 细胞群, FlowSOM | RETURNS: 细胞群与比例

NameRequiredDescriptionDefault
eventsNo事件数据
grid_sizeNo网格大小
n_clustersNo聚类数
flow_gateFlow cytometry gating / 流式门控分析A
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Flow cytometry gating / 流式门控分析 | USE WHEN: gating / population percentages from flow cytometry data | ALIASES: 流式门控, 圈门, gating, 阳性比例, flow cytometry | RETURNS: 门控比例

NameRequiredDescriptionDefault
eventsNo事件数据
methodNo门控方法
paramsNo门控参数
forest_plotForest plot / 森林图B
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Forest plot / 森林图 | USE WHEN: forest plot from HR/OR/RR with confidence intervals | ALIASES: 森林图, forest plot, HR OR RR, 置信区间图, meta森林图 | RETURNS: 森林图（矢量）

NameRequiredDescriptionDefault
dataYes每项 {name, estimate, ci_lower, ci_upper}
titleNo图标题
x_labelNoX 轴标签
gene_info_lookupGene information lookup / 基因信息检索B
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Gene information lookup / 基因信息检索 | USE WHEN: what a gene does: full name, aliases, function, location | ALIASES: 基因信息, 这个基因是干什么的, gene function, 别名, gene info, 基因全称 | RETURNS: 基因信息

NameRequiredDescriptionDefault
queryYes基因名/关键词
gene_pathway_diseaseGene → pathway → disease network / 基因-通路-疾病网络A
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Gene → pathway → disease network / 基因-通路-疾病网络 | USE WHEN: from a gene list, find pathway / disease / drug associations and build a mechanism network | ALIASES: 基因通路, 机制网络, gene pathway, 疾病关联, 药物靶点, network, 通路疾病 | RETURNS: 通路/疾病/药物关联

NameRequiredDescriptionDefault
genesYes基因列表
geo_dataset_pipelineGEO dataset pipeline / GEO 数据全流程A
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GEO dataset pipeline / GEO 数据全流程 | USE WHEN: end-to-end on a GEO accession: DEG → enrichment → immune infiltration | ALIASES: GEO, GSE, 数据集分析, GEO编号, geo dataset, public data reanalysis | RETURNS: 差异基因、富集结果

NameRequiredDescriptionDefault
geo_idYesGEO 数据集编号，如 GSE12345
speciesNo物种：human/mouse
pval_cutoffNoP 阈值
logfc_cutoffNologFC 阈值
gleason_grade_groupGleason grade group / Gleason 分级分组A
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Gleason grade group / Gleason 分级分组 | USE WHEN: Gleason score → Grade Group and risk stratum (prostate cancer) | ALIASES: Gleason, 前列腺癌分级, Grade Group, 格里森评分, 前列腺病理 | RETURNS: Grade Group 与风险分层

NameRequiredDescriptionDefault
primary_gradeYes主要分级 1-5
secondary_gradeYes次要分级 1-5
go_kegg_enrichmentGO / KEGG enrichment / 通路富集（GO/KEGG）A
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GO / KEGG enrichment / 通路富集（GO/KEGG） | USE WHEN: pathway enrichment for a gene list (GO biological process, KEGG) | ALIASES: 富集分析, GO, KEGG, 通路富集, enrichment, 通路, 功能注释 | RETURNS: 富集通路与 P 值

NameRequiredDescriptionDefault
db_filterNo数据库：GO_BP/GO_CC/GO_MF/KEGG
gene_listYes基因列表
project_idNo项目 id
ic50_fitIC50 curve fitting / IC50 曲线拟合A
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IC50 curve fitting / IC50 曲线拟合 | USE WHEN: fit a dose-response curve and report IC50 with 95% CI | ALIASES: IC50, 半数抑制浓度, 剂量效应曲线, dose response, 抑制率拟合, GI50 | RETURNS: IC50 值与 95%CI

NameRequiredDescriptionDefault
modelNo拟合模型：4PL/3PL
directionNoinhibition 或 activation
responsesYes响应/抑制率
concentrationsYes浓度序列
ihc_hscoreIHC scoring (H-score / Allred) / IHC 免疫组化评分A
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IHC scoring (H-score / Allred) / IHC 免疫组化评分 | USE WHEN: immunohistochemistry scoring from staining intensity percentages (H-score, Allred, positive rate) | ALIASES: 免疫组化评分, H-score, Allred, IHC, 阳性率, 染色评分, ER PR HER2 | RETURNS: H-score / Allred / 阳性率

NameRequiredDescriptionDefault
stain_typeYes染色类型
intensity_0_pctNo0 级占比(%)
intensity_1_pctNo1+ 占比(%)
intensity_2_pctNo2+ 占比(%)
intensity_3_pctNo3+ 占比(%)
iptw_weightingIPTW inverse probability weighting / IPTW 逆概率加权B
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IPTW inverse probability weighting / IPTW 逆概率加权 | USE WHEN: propensity-score weighting (ATE/ATT) for observational comparisons | ALIASES: IPTW, 逆概率加权, 倾向评分加权, propensity weighting, 加权后效应, ATE | RETURNS: 权重与加权后效应

NameRequiredDescriptionDefault
treatmentYes处理分组 0/1
propensity_scoresYes倾向评分
journal_recommendJournal recommendation / 智能选刊 V3A
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Journal recommendation / 智能选刊 V3 | USE WHEN: recommend target journals from title/abstract, in reach / match / safe tiers | ALIASES: 选刊, 投哪个期刊, 期刊推荐, journal, submission target, 冲稳保, 投稿建议 | RETURNS: 候选期刊与三档策略

NameRequiredDescriptionDefault
titleYes论文标题
designNo研究设计
abstractNo摘要
keywordsNo关键词
sample_sizeNo样本量
target_tierNo目标档次
kaplan_meierKaplan-Meier curve / Kaplan-Meier 曲线A
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Kaplan-Meier curve / Kaplan-Meier 曲线 | USE WHEN: KM curve and median survival only (no multivariable model) | ALIASES: KM曲线, Kaplan-Meier, 中位生存, 生存曲线, 生存率, log-rank | RETURNS: KM 曲线与中位生存

NameRequiredDescriptionDefault
timesYes随访时间
eventsYes事件 0/1
groupsNo分组
linear_mixed_modelLinear mixed model (repeated measures) / 线性混合模型（纵向/重复测量，公式模式）A
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Linear mixed model (repeated measures) / 线性混合模型（纵向/重复测量，公式模式） | USE WHEN: same subject measured repeatedly (pre/post, multiple follow-ups): compare trajectories over time, group×time interaction; modern replacement for repeated-measures ANOVA (handles missing, no sphericity assumption) | ALIASES: 混合模型, 重复测量, 纵向数据, LMM, mixed model, 交互作用, 多次随访, 每人测多次, repeated measures | RETURNS: 固定效应系数/SE/Wald z/P/95%CI（按真实变量名，不是 X1/X2）、各项整体检验 omnibus、交互项检验、primary_test、ICC、方差成分、n_subjects/n_obs、组间例数、缺失与收敛告警

NameRequiredDescriptionDefault
dataYes长格式数据：每行一次观测 [{受试者ID, 分组, 时点, 结局}, ...]；也接受列式 {列名: [值, ...]}。注意必须是**长表**（每行一次测量），不是每人一行多列
remlNo默认 true（REML）
formulaYesR 风格公式，如 '收缩压 ~ 分组 * 时点'（组×时间交互）；左端=结局
subjectYes受试者/病案号列名（随机截距的分组依据）
referenceNo参照水平，如 {"分组": "对照组"} —— 报告表里「与对照组相比」必须是它
re_formulaNo随机效应，默认 '1'（随机截距）；随机斜率用 '1 + 时点'
categoricalNo显式声明哪些列按分类处理（如 ["分组"]）；数值编码的 0/1 分组必须声明，否则会被当连续变量
time_valuesNo把字符串时点映射成数值以估线性趋势，如 {"时点": {"基线":0,"4周":1,"12周":2}}
literature_search_pmcLiterature search (1.53M PMC full texts) / 文献检索（站内 153 万篇 PMC）A
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Literature search (1.53M PMC full texts) / 文献检索（站内 153 万篇 PMC） | USE WHEN: search the in-house PMC corpus for literature / evidence / state of the art | ALIASES: 找文献, 文献检索, 检索文献, literature search, PMC, 证据, 研究现状, PubMed替代 | RETURNS: 文献列表（含 PMCID）

NameRequiredDescriptionDefault
qYes检索词（英文效果更好）
limitNo返回条数
metabolite_heatmapMetabolite heatmap / 代谢组热图C
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Metabolite heatmap / 代谢组热图 | USE WHEN: clustered heatmap of metabolites | ALIASES: 代谢热图, 聚类热图, heatmap, metabolite heatmap | RETURNS: 聚类热图

NameRequiredDescriptionDefault
data_jsonYes必填。数据对象（不是字符串）。矩阵形式：{"matrix": [[行1各列数值...], [行2...]], "rows": ["基因/特征名"], "cols": ["样本名"]}；组学结果另可带 {"groups": ["组A","组A","组B"...]}（与 cols 一一对应）、{"p_values": [...], "log2fc": [...]}（若已有差异结果）。实测缺此字段会被拒：Field required: data_json
metabolite_pathwayMetabolite pathway enrichment / 代谢物通路富集B
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Metabolite pathway enrichment / 代谢物通路富集 | USE WHEN: pathway enrichment for differential metabolites | ALIASES: 代谢物通路, 代谢富集, metabolite pathway, KEGG代谢, 差异代谢物 | RETURNS: 代谢通路富集

NameRequiredDescriptionDefault
metabolitesYes代谢物列表
fdr_thresholdNoFDR 阈值
metabolite_pcaMetabolite PCA / 代谢组 PCAA
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Metabolite PCA / 代谢组 PCA | USE WHEN: sample separation / outliers in metabolomics (unsupervised) | ALIASES: 代谢组PCA, PCA, 样本分组趋势, 离群, unsupervised, 得分图 | RETURNS: PCA 得分与解释度

NameRequiredDescriptionDefault
groupsNo分组标签
data_jsonYes代谢物矩阵
metabolite_plsdaMetabolite PLS-DA / 代谢组 PLS-DAA
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Metabolite PLS-DA / 代谢组 PLS-DA | USE WHEN: supervised discrimination and VIP for differential metabolites | ALIASES: PLS-DA, 有监督判别, VIP, 代谢组判别, 判别分析 | RETURNS: PLS-DA 模型与 VIP

NameRequiredDescriptionDefault
groupsNo分组标签
data_jsonYes代谢物矩阵
metabolite_volcanoMetabolite volcano plot / 代谢组火山图C
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Metabolite volcano plot / 代谢组火山图 | USE WHEN: volcano plot of differential metabolites | ALIASES: 代谢火山图, volcano, 差异代谢物火山图 | RETURNS: 火山图

NameRequiredDescriptionDefault
data_jsonYes必填。数据对象（不是字符串）。矩阵形式：{"matrix": [[行1各列数值...], [行2...]], "rows": ["基因/特征名"], "cols": ["样本名"]}；组学结果另可带 {"groups": ["组A","组A","组B"...]}（与 cols 一一对应）、{"p_values": [...], "log2fc": [...]}（若已有差异结果）。实测缺此字段会被拒：Field required: data_json
mif_phenotypeMultiplex IF phenotyping / 多重免疫荧光表型B
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Multiplex IF phenotyping / 多重免疫荧光表型 | USE WHEN: assign cell phenotypes and proportions from multiplex immunofluorescence markers | ALIASES: 多重免疫荧光, mIF, 细胞表型, phenotype, marker组合, 亚群比例 | RETURNS: 细胞亚群表型与比例

NameRequiredDescriptionDefault
cd3_posNoCD3+ 数
cd8_posNoCD8+ 数
foxp3_posNoFoxP3+ 数
pd_l1_posNoPD-L1+ 数
multi_omics_integrateMulti-omics integration / 多组学整合B
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Multi-omics integration / 多组学整合 | USE WHEN: integrate mRNA / protein / metabolite layers and find cross-layer associations | ALIASES: 多组学, 整合分析, multi-omics, 转录组蛋白组代谢组, cross-omics | RETURNS: 多组学关联结果

NameRequiredDescriptionDefault
mrnaNomRNA 列表
proteinNo蛋白列表
metaboliteNo代谢物列表
pathway_mapNo通路映射方式
nomogramNomogram / 列线图 NomogramA
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Nomogram / 列线图 Nomogram | USE WHEN: turn a logistic or Cox model into a nomogram figure for clinical use | ALIASES: 列线图, Nomogram, 评分图, nomogram, 风险评分图 | RETURNS: 列线图 SVG

NameRequiredDescriptionDefault
csv_dataNoCSV 文本
nuclei_countNuclei segmentation & counting / 细胞核分割与计数B
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Nuclei segmentation & counting / 细胞核分割与计数 | USE WHEN: cell density / positive rate from a tissue section (nuclei segmentation) | ALIASES: 细胞核分割, 细胞计数, 密度, nuclei count, segmentation, 阳性细胞率 | RETURNS: 分割统计结果

NameRequiredDescriptionDefault
stain_typeNo染色类型
cell_densityNo细胞密度
positive_rateNo阳性率
orf_findORF prediction / ORF 预测A
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ORF prediction / ORF 预测 | USE WHEN: find open reading frames in a nucleotide sequence | ALIASES: ORF, 开放阅读框, 找编码区, open reading frame, 翻译起始 | RETURNS: ORF 列表与位置

NameRequiredDescriptionDefault
sequenceYes核酸序列
organ_coefficientOrgan coefficient / 脏器系数B
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Organ coefficient / 脏器系数 | USE WHEN: organ weight / body weight ratio (脏器系数) | ALIASES: 脏器系数, 器官系数, organ coefficient, organ index, 脏器重量比 | RETURNS: 脏器系数

NameRequiredDescriptionDefault
labelNo脏器名
body_weightsNo体重
organ_weightsNo脏器重
pcr_primer_designPCR primer design / PCR 引物设计A
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PCR primer design / PCR 引物设计 | USE WHEN: design PCR primer pairs from a DNA sequence or gene (Tm, GC, product size) | ALIASES: 引物设计, PCR引物, primer, 设计引物, 扩增引物, primer pair | RETURNS: 候选引物对与 Tm/GC

NameRequiredDescriptionDefault
geneNo基因名（无序列时用）
sequenceNo模板 DNA 序列
permutation_testPermutation test / 置换检验A
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Permutation test / 置换检验 | USE WHEN: exact/permutation test when t-test assumptions fail or n is tiny | ALIASES: 置换检验, 随机化检验, permutation, 精确检验, 小样本非参数, resampling | RETURNS: 置换 P 值

NameRequiredDescriptionDefault
group1Yes第一组数值
group2Yes第二组数值
n_permNo置换次数，默认 10000
pico_refinePICO refinement / PICO 精炼A
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PICO refinement / PICO 精炼 | USE WHEN: turn a vague clinical question into a structured PICO (Population / Intervention / Comparator / Outcome) | ALIASES: PICO, 把问题拆开, 研究问题怎么提, refine question, 结构化问题, PICO拆解 | RETURNS: 结构化 PICO

NameRequiredDescriptionDefault
picoYes结构化 PICO，如 {"P":"ICU 脓毒症患者","I":"乳酸清除率","C":"常规","O":"28天病死率"}
pk_ncaNon-compartmental PK (NCA) / NCA 非房室药代分析A
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Non-compartmental PK (NCA) / NCA 非房室药代分析 | USE WHEN: non-compartmental analysis of concentration-time data: AUC, Cmax, Tmax, t1/2, CL, Vd | ALIASES: NCA, 非房室分析, 药代参数, AUC Cmax T1/2, PK parameters, 血药浓度 | RETURNS: AUC/Cmax/Tmax/T1/2/CL/Vd

NameRequiredDescriptionDefault
doseNo给药剂量
dose_unitNo剂量单位，如 mg/kg
time_pointsYes时间点
concentrationsYes浓度
pk_pd_assessPK/PD assessment / PK/PD 评估C
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PK/PD assessment / PK/PD 评估 | USE WHEN: pharmacokinetic/pharmacodynamic assessment and dose projection | ALIASES: PK/PD评估, 药代药效, 剂量推算, pkpd assess, 暴露量 | RETURNS: PK/PD 评估结果

NameRequiredDescriptionDefault
drug_nameYes药物名称
pk_paramsNo药代参数
pkpd_simulatePK/PD simulation / PK/PD 模拟B
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PK/PD simulation / PK/PD 模拟 | USE WHEN: simulate concentration-effect time course for a dosing regimen | ALIASES: PK/PD模拟, 给药方案模拟, 浓度时间曲线, simulation, 剂量方案 | RETURNS: 模拟曲线与参数

NameRequiredDescriptionDefault
drug_nameYes药物名
pd_paramsNo药效参数
pk_paramsNo药代参数
plasmid_mapPlasmid map / 质粒图谱B
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Plasmid map / 质粒图谱 | USE WHEN: draw/describe a plasmid map from a sequence (features, orientation) | ALIASES: 质粒图谱, 载体结构, plasmid map, vector, 质粒构建图 | RETURNS: 质粒图谱

NameRequiredDescriptionDefault
sequenceYes质粒序列
ppi_networkProtein-protein interaction network / 蛋白互作网络A
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Protein-protein interaction network / 蛋白互作网络 | USE WHEN: PPI network from a protein list, hub identification (STRING) | ALIASES: 蛋白互作, PPI, STRING, hub基因, 互作网络, network | RETURNS: 互作网络与 hub 蛋白

NameRequiredDescriptionDefault
speciesNo物种：human/mouse
protein_listYes蛋白/基因列表
confidence_thresholdNo置信度阈值
primer_tmPrimer Tm & dimers / 引物 Tm 计算A
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Primer Tm & dimers / 引物 Tm 计算 | USE WHEN: Tm, GC% and self/cross dimer check for given primer sequences | ALIASES: Tm, 引物Tm, 退火温度, GC含量, 二聚体, primer dimer, melting temperature | RETURNS: Tm、GC、二聚体

NameRequiredDescriptionDefault
sequenceYes引物序列
prisma_flowchartPRISMA flow diagram / PRISMA 流程图B
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PRISMA flow diagram / PRISMA 流程图 | USE WHEN: PRISMA literature screening flow for systematic reviews / meta-analyses | ALIASES: PRISMA, 文献筛选流程, 系统评价流程图, flow diagram, 检索流程图 | RETURNS: PRISMA 流程图

NameRequiredDescriptionDefault
titleNo研究题目
records_screenedNo筛读记录数
studies_includedNo最终纳入研究数
records_identifiedNo检索记录数
propensity_score_matchingPropensity score matching (PSM) / 倾向性评分匹配 PSMA
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Propensity score matching (PSM) / 倾向性评分匹配 PSM | USE WHEN: match treated and control subjects on baseline covariates (1:1 nearest neighbour within caliper), then report SMD balance | ALIASES: PSM, 倾向性评分, 倾向评分匹配, propensity score, 1:1匹配, 基线可比, 混杂控制, 匹配后均衡 | RETURNS: 匹配后样本与均衡性

NameRequiredDescriptionDefault
dataNo样本表：每行一个受试者的字典，如 [{"treat":1,"age":62,"sex":0}, …]（也可改用 treatment/covariates 直接传数组）
ratioNo匹配比例，默认 1
treatmentNo处理变量（1=处理组，0=对照组）。传 data 时填列名 "treat"；也可直接传 0/1 数组
covariatesNo协变量列名数组，如 ["age","sex"]（传 data 时）或二维数值矩阵
protein_digestIn-silico protein digestion / 蛋白酶解模拟A
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In-silico protein digestion / 蛋白酶解模拟 | USE WHEN: choose protease and list peptides for mass-spec sample prep | ALIASES: 蛋白酶解, 质谱前处理, 酶解肽段, digest, trypsin, 肽段清单 | RETURNS: 肽段清单

NameRequiredDescriptionDefault
enzymesNo酶：Trypsin/Lys-C
sequenceYes蛋白序列
protein_enrichmentProtein enrichment / 蛋白富集分析B
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Protein enrichment / 蛋白富集分析 | USE WHEN: GO/KEGG enrichment for a protein list | ALIASES: 蛋白富集, protein enrichment, 蛋白GO, 蛋白通路 | RETURNS: 富集通路

NameRequiredDescriptionDefault
speciesNo物种
protein_listYes蛋白列表
enrichment_typeNoGO/KEGG/Reactome
protein_physchemProtein physicochemical properties / 蛋白理化性质A
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Protein physicochemical properties / 蛋白理化性质 | USE WHEN: molecular weight, pI, GRAVY, instability index etc. from a protein sequence | ALIASES: 蛋白理化性质, 分子量, 等电点, pI, GRAVY, 疏水性, MW, protein properties | RETURNS: MW/pI/GRAVY 等

NameRequiredDescriptionDefault
sequenceYes蛋白序列
protein_nameNo蛋白名
protein_ptmPost-translational modification prediction / 翻译后修饰分析A
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Post-translational modification prediction / 翻译后修饰分析 | USE WHEN: predict phosphorylation / glycosylation and other PTM sites | ALIASES: 翻译后修饰, 磷酸化位点, 糖基化, PTM, phosphorylation, modification sites | RETURNS: 修饰位点预测

NameRequiredDescriptionDefault
sequenceYes蛋白序列
modificationsNo修饰类型：phosphorylation/glycosylation
psi_stabilityPSI model stability / 模型稳定性 PSIA
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PSI model stability / 模型稳定性 PSI | USE WHEN: population stability index between expected and observed score distributions | ALIASES: PSI, 模型稳定性, 群体稳定性, population stability, 漂移 | RETURNS: PSI 值与稳定性判断

NameRequiredDescriptionDefault
n_binsNo分箱数
expected_probsYes开发集概率
observed_probsYes验证集概率
pubmed_searchPubMed search / PubMed 检索A
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PubMed search / PubMed 检索 | USE WHEN: search PubMed for the latest literature (live NCBI E-utilities) | ALIASES: PubMed, 查pubmed, 最新文献, pubmed检索, NCBI, 最新研究 | RETURNS: PubMed 文献列表

NameRequiredDescriptionDefault
qYes检索式（英文）
year_startNo起始年份
max_resultsNo条数
qpcr_bayesian_designqPCR Bayesian design / 贝叶斯自适应设计（qPCR）C
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qPCR Bayesian design / 贝叶斯自适应设计（qPCR） | USE WHEN: design a qPCR experiment / digital-twin PCR simulation for a target | ALIASES: qPCR设计, 贝叶斯自适应, digital PCR, 实验设计, target, 扩增效率 | RETURNS: 实验设计方案

NameRequiredDescriptionDefault
targetYes目标基因/模板
constraintsNo约束条件
qpcr_ddctqPCR 2^-ddCt relative expression / qPCR 相对表达量（2^-ΔΔCt）A
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qPCR 2^-ddCt relative expression / qPCR 相对表达量（2^-ΔΔCt） | USE WHEN: compute dCt, ddCt and 2^-ddCt fold change from target and reference Ct values, with a group test (calibrated on the mean control dCt) | ALIASES: qPCR, 2^-ddCt, 相对表达量, 倍数, dCt, ddCt, fold change, 内参, Ct值, 基因表达 | RETURNS: fc（2^-ΔΔCt 相对倍数，已按对照组 ΔCt 均值校准）、fold_values、p_value、stat_method（welch_ttest_on_dct / student_ttest_on_dct …

NameRequiredDescriptionDefault
geneNo目的基因名，如 NLRP3 / TREM2
exp_ctYes处理组目的基因 Ct，逗号分隔，如 20.1,20.4,20.2
ctrl_ctYes对照组目的基因 Ct，逗号分隔，如 22.5,22.8,22.6
ref_geneNo内参基因名，默认 GAPDH
exp_ref_ctYes处理组内参基因 Ct，逗号分隔，与 exp_ct 一一对应，如 18.2,18.5,18.3
ctrl_ref_ctYes对照组内参基因 Ct，逗号分隔，与 ctrl_ct 一一对应，如 18.3,18.1,18.4
randomization_allocateRandomization / allocation / 随机化分组A
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Randomization / allocation / 随机化分组 | USE WHEN: produce a reproducible randomization list: simple, block, stratified, or minimization, with a fixed seed | ALIASES: 随机分组, 随机数字表, 区组随机, 分层随机, 最小化法, randomization, random allocation, 分组名单, seed | RETURNS: 分组结果表

NameRequiredDescriptionDefault
seedNo随机种子（固定才可复现）
groupsYes分组名称数组，如 ["干预组","对照组"]（必须是数组，不是数字）
methodNosimple 简单 / block 区组 / stratified 分层 / minimization 最小化
block_sizeNo区组长度（block 方法用）
n_subjectsYes总样本量，如 60
renal_dose_adjustRenal function & dose adjustment / 肾功能剂量调整A
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Renal function & dose adjustment / 肾功能剂量调整 | USE WHEN: the user gives age / sex / weight / serum creatinine and asks for creatinine clearance, eGFR, CKD stage, or how to adjust a drug dose in renal impairment (vancomycin, meropenem, metformin, anticoagulants…) | ALIASES: 肾功能剂量, 肌酐清除率, CrCl, eGFR, 肾小球滤过率, 减量, 剂量调整, 肾功能不全, 万古霉素怎么减量, 按肌酐算剂量 | RETURNS: CrCl（Cockcroft-Gault）+ eGFR（CKD-EPI 2021）+ CKD 分期 + 该药的肾剂量分档（含依据与核对提示）

NameRequiredDescriptionDefault
ageYes年龄（岁）
sexNomale / female（也接受 男 / 女）
crrtNo是否 CRRT
drugNo药名，中英文均可，如 vancomycin / 万古霉素
dialysisNo是否血液透析
height_cmNo身高 cm（用于理想体重与 BSA，建议给）
scr_mg_dlNo血肌酐 mg/dL（与 scr_umol_l 二选一）
weight_kgYes实际体重 kg
scr_umol_lNo血肌酐 μmol/L（国内化验单常用单位，与 scr_mg_dl 二选一）
reporting_checklistReporting guideline checklist / 报告规范清单核对A
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Reporting guideline checklist / 报告规范清单核对 | USE WHEN: check a manuscript against CONSORT / STROBE / PRISMA / TRIPOD / ARRIVE / STARD | ALIASES: 报告规范, CONSORT, STROBE, PRISMA, TRIPOD, STARD, checklist, 投稿前检查, 规范核对 | RETURNS: 逐条核对结果

NameRequiredDescriptionDefault
textYes论文正文
design_typeNo研究设计类型
restriction_sitesRestriction sites / 酶切位点分析A
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Restriction sites / 酶切位点分析 | USE WHEN: find restriction enzyme sites in a DNA sequence (can it be cut, which enzyme) | ALIASES: 酶切位点, 限制性内切酶, restriction enzyme, 能不能切开, cloning sites | RETURNS: 酶切位点清单

NameRequiredDescriptionDefault
sequenceYes核酸序列
risk_ratio_orRisk ratio / odds ratio / 相对危险度 RRA
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Risk ratio / odds ratio / 相对危险度 RR | USE WHEN: RR, OR and 95% CI from a 2×2 table of counts | ALIASES: 相对危险度, RR, OR, 比值比, 四格表, risk ratio, odds ratio, 95%CI | RETURNS: RR、OR、95%CI

NameRequiredDescriptionDefault
exposed_totalYes暴露组总数
exposed_eventsYes暴露组事件数
unexposed_totalYes非暴露组总数
unexposed_eventsYes非暴露组事件数
rnaseq_degRNA-seq differential analysis / RNA-seq 差异分析B
Read-only
Inspect

RNA-seq differential analysis / RNA-seq 差异分析 | USE WHEN: differential genes from RNA-seq count matrix (negative binomial) | ALIASES: RNA-seq, 转录组差异, 差异基因, DESeq, count matrix, RNA测序 | RETURNS: 差异基因清单

NameRequiredDescriptionDefault
groupsNo分组
log2fcNolog2FC 阈值
data_jsonYes表达矩阵
fdr_thresholdNoFDR
roc_aucROC curve & AUC / ROC 曲线与 AUCA
Read-only
Inspect

ROC curve & AUC / ROC 曲线与 AUC | USE WHEN: the user has a binary gold standard and predicted scores/probabilities and wants AUC, ROC curve, discrimination, or the best cut-off | ALIASES: ROC, AUC, 曲线下面积, 诊断效能, 最佳阈值, cutoff, discrimination, 敏感度特异度, ROC曲线 | RETURNS: AUC、最优阈值、敏感度特异度

NameRequiredDescriptionDefault
labelsYes真实标签 0/1
scoresYes预测分数/概率
sample_size_calculatorSample size & power / 样本量计算（设计口径）A
Read-only
Inspect

Sample size & power / 样本量计算（设计口径） | USE WHEN: calculate how many subjects are needed (two means / two proportions / correlation / survival), or solve for power | ALIASES: 样本量, 要做多少例, 需要多少病人, sample size, power calculation, 把握度, 最少例数, n per group | RETURNS: 每组例数与合计

NameRequiredDescriptionDefault
sdNo标准差
alphaNo显著性水平，默认 0.05
powerNo把握度，默认 0.8
effectNo预期效应量/差值
dropoutNo脱落率
sample_typeNo计算类型：两组均数/两组率/相关/生存
sanyu_job_status异步任务进度与结果 / Job status
Read-only
Inspect

Get progress and result of an asynchronous job. 生成类动作（topic_generate / paper_draft_one_click / …）提交后返回 job_id（engine_status=queued），用这个工具轮询：每 3 秒查一次，最多等 300 秒；status=succeeded 取 result，failed 读 error（失败不扣分）。US English: poll a queued generation job until succeeded/failed.

NameRequiredDescriptionDefault
job_idYes提交生成类动作时返回的 job_id
sanyu_list_actions动作目录 / Tool catalogA
Read-only
Inspect

List every tool on this server as a compact catalogue (name / when-to-use / returns / required params / cost). 列出三域医研全部动作摘要。仅在不确定该用哪个工具时调用；已知工具名可直接调用对应工具。

NameRequiredDescriptionDefault

No parameters

sas_r_codeReproducible SAS/R/Python code / 生成可复现 SAS/R 代码A
Read-only
Inspect

Reproducible SAS/R/Python code / 生成可复现 SAS/R 代码 | USE WHEN: generate the SAS / R / Python code that reproduces a given analysis | ALIASES: SAS代码, R代码, 复现代码, reproducible code, Python代码, 统计代码 | RETURNS: 推荐统计方法与代码

NameRequiredDescriptionDefault
designYes研究设计
n_groupsNo组数
outcome_typeNo结局类型
scrna_annotateSingle-cell type annotation / 单细胞细胞类型注释B
Read-only
Inspect

Single-cell type annotation / 单细胞细胞类型注释 | USE WHEN: automatically annotate cell types in single-cell clusters | ALIASES: 细胞类型注释, annotation, 单细胞注释, cell type, 自动注释 | RETURNS: 细胞类型注释

NameRequiredDescriptionDefault
data_jsonYes必填。数据对象（不是字符串）。矩阵形式：{"matrix": [[行1各列数值...], [行2...]], "rows": ["基因/特征名"], "cols": ["样本名"]}；组学结果另可带 {"groups": ["组A","组A","组B"...]}（与 cols 一一对应）、{"p_values": [...], "log2fc": [...]}（若已有差异结果）。实测缺此字段会被拒：Field required: data_json
scrna_clusterSingle-cell clustering / 单细胞分析A
Read-only
Inspect

Single-cell clustering / 单细胞分析 | USE WHEN: cluster and embed single-cell data (UMAP) | ALIASES: 单细胞, scRNA, 聚类降维, UMAP, Seurat, single cell | RETURNS: 聚类结果与 UMAP

NameRequiredDescriptionDefault
n_pcsNo主成分数
data_jsonYes单细胞矩阵
min_genesNo最少基因数
max_mito_pctNo线粒体比例上限
scrna_markersSingle-cell marker genes / 单细胞 Marker 基因B
Read-only
Inspect

Single-cell marker genes / 单细胞 Marker 基因 | USE WHEN: find marker genes per cluster | ALIASES: marker基因, 各群marker, markers, 差异标记, cluster markers | RETURNS: 各群 marker

NameRequiredDescriptionDefault
data_jsonYes必填。数据对象（不是字符串）。矩阵形式：{"matrix": [[行1各列数值...], [行2...]], "rows": ["基因/特征名"], "cols": ["样本名"]}；组学结果另可带 {"groups": ["组A","组A","组B"...]}（与 cols 一一对应）、{"p_values": [...], "log2fc": [...]}（若已有差异结果）。实测缺此字段会被拒：Field required: data_json
shap_explainSHAP single-case explanation / SHAP 单样本解释B
Read-only
Inspect

SHAP single-case explanation / SHAP 单样本解释 | USE WHEN: explain why one patient was predicted high risk (feature contributions) | ALIASES: SHAP, 解释预测, 为什么高风险, 特征贡献, explain prediction, 可解释性 | RETURNS: SHAP 贡献图

NameRequiredDescriptionDefault
csv_dataNoCSV 文本
model_typeNo模型类型
sample_indexNo样本序号
smd_balanceSMD balance check / SMD 均衡性检查A
Read-only
Inspect

SMD balance check / SMD 均衡性检查 | USE WHEN: standardized mean differences before/after PSM or IPTW (covariate balance) | ALIASES: SMD, 标准化均数差, 均衡性, balance, 匹配后均衡, 协变量平衡, love plot | RETURNS: 各协变量 SMD 与均衡判断

NameRequiredDescriptionDefault
treatmentYes分组 0/1
covariatesYes协变量矩阵：每行一个样本、每列一个变量
species_dose_convertSpecies dose conversion / 种属剂量换算A
Read-only
Inspect

Species dose conversion / 种属剂量换算 | USE WHEN: convert an animal dose to a human equivalent dose (mg/kg ↔ mg/m², FDA guidance) | ALIASES: 种属换算, 动物剂量换人, 剂量换算, species conversion, HED, mg/m2, 体表面积法 | RETURNS: 换算后剂量与依据

NameRequiredDescriptionDefault
source_doseYes来源剂量
source_speciesYes来源物种
target_speciesYes目标物种
stats_toolboxStatistics toolbox (97 tests) / 统计中心（97 个统计工具统一入口）A
Read-only
Inspect

Statistics toolbox (97 tests) / 统计中心（97 个统计工具统一入口） | USE WHEN: any statistical test not covered by a dedicated tool: Kappa, ICC, Bland-Altman, VIF, PCA, cluster, Friedman, McNemar, runs test, sign test, trend chi-square, meta-analysis pieces — pass tool_id | ALIASES: 统计检验, Kappa, ICC, Bland-Altman, VIF, PCA, 聚类, Friedman, McNemar, 游程 | RETURNS: 该统计工具的完整结果（含统计量、P 值、95%CI、解读）

NameRequiredDescriptionDefault
payloadNo该工具需要的数据字段（不要把数据包成字符串）。常见字段：group1/group2（两组数值数组）、groups（{组名:[数值]}）、values、x/y、time/event、gold/score、es/se（效应量与标准误）、m1/m2、r1/r2、row_var/col_var、features/label
tool_idYes统计工具 id，必须是下列之一：比较差异[ttest,welch,mwu,wilcoxon,anova,welchAnova,kw,friedman,rmAnova,ancova,chi2,fisher,mcnemar,cochranQ,posthoc,multicorr,homogeneity,normality,brunner,signTest,runsTest,effectSize,pInverse]、找关系[pearson,spearman,kendall,linearReg,multiLinear,logistic,logisticModel,vif,regDiag,icc,blan
study_design_advisorStudy design advisor / 研究设计向导A
Read-only
Inspect

Study design advisor / 研究设计向导 | USE WHEN: the user has a research question but does not know which study design to use (RCT / cohort / case-control / cross-sectional / diagnostic accuracy) | ALIASES: 研究设计选哪个, 设计向导, 用什么研究设计, study design, which design, 研究类型, 队列还是病例对照 | RETURNS: 推荐研究设计与理由

NameRequiredDescriptionDefault
picoYes研究问题/PICO 描述
feasibilityNo可行性条件（病例量、周期、经费）
survival_coxSurvival analysis (KM + Cox) / 生存分析（KM/Cox 全流程）A
Read-only
Inspect

Survival analysis (KM + Cox) / 生存分析（KM/Cox 全流程） | USE WHEN: follow-up time and event data: survival curves, median survival, hazard ratios, log-rank, multivariable Cox | ALIASES: 生存分析, Cox回归, HR, 风险比, log-rank, 中位生存, 随访, 生存率, hazard ratio | RETURNS: 生存曲线、中位生存、HR、log-rank P

NameRequiredDescriptionDefault
dataYes逐行数据，每行含时间/事件/分组/协变量
time_colNo随访时间列名
event_colNo结局事件列名（1=事件）
group_colNo分组列名
analysis_typeNokm 或 cox
covariate_colsNoCox 协变量列名
table_one_baselineTable 1 (baseline characteristics) / 生成 Table 1（基线特征表）A
Read-only
Inspect

Table 1 (baseline characteristics) / 生成 Table 1（基线特征表） | USE WHEN: build the paper's first table: baseline characteristics by group with P values | ALIASES: Table 1, 基线表, 基线特征表, 人口学表, 第一张表, baseline table, characteristics table, 组间比较表 | RETURNS: Table 1 表格 + 可选段落

NameRequiredDescriptionDefault
file_idYes数据文件 id（可不填，助手会自动用你最近上传的数据）
test_methodNoauto/t/nonparametric/chi2
group_variableYes分组变量列名
generate_paragraphNo是否同时生成论文表述段落
variables_to_includeNo纳入的变量列名
table_one_subgroupSubgroup Table 1 / 分组 Table 1（亚组表）A
Read-only
Inspect

Subgroup Table 1 / 分组 Table 1（亚组表） | USE WHEN: baseline characteristics split by a subgroup variable (e.g. by sex, by centre) | ALIASES: 亚组基线表, 分层基线表, subgroup table, stratified baseline, 按某变量分层 | RETURNS: 亚组表

NameRequiredDescriptionDefault
file_idYes数据文件 id
group_variableYes分组变量
subgroup_variableYes分层变量
variables_to_includeNo纳入变量
text_overlap_checkText overlap self-check / 文字重合度自查A
Read-only
Inspect

Text overlap self-check / 文字重合度自查 | USE WHEN: compare a passage with the user's own previous papers (self-plagiarism check) | ALIASES: 查重, 文字重合度, 重复率, overlap, self plagiarism, 相似文献 | RETURNS: 重合度与相似文献

NameRequiredDescriptionDefault
textYes待查文本
top_nNo返回条数
toxicity_predictToxicity prediction (hERG / DILI) / 毒性预测（hERG/DILI）A
Read-only
Inspect

Toxicity prediction (hERG / DILI) / 毒性预测（hERG/DILI） | USE WHEN: cardiac (hERG) and liver (DILI) toxicity risk from a SMILES string | ALIASES: 毒性预测, hERG, DILI, 心脏毒性, 肝毒性, toxicity, SMILES | RETURNS: 毒性风险分级

NameRequiredDescriptionDefault
smilesYes化合物 SMILES
endpointsNo终点：hERG/DILI/Ames
toxicology_assessToxicology assessment / 毒理评估A
Read-only
Inspect

Toxicology assessment / 毒理评估 | USE WHEN: nonclinical toxicology risk: hERG / DILI / genotoxicity, overall risk level | ALIASES: 毒理评估, 毒性风险, 遗传毒, toxicology, nonclinical safety, 安全评价 | RETURNS: 毒性风险分级

NameRequiredDescriptionDefault
logPNologP
hERG_IC50_nMNohERG IC50 (nM)
daily_dose_mgNo日剂量 (mg)
expected_Cmax_nMNo预期 Cmax (nM)
tukey_hsdTukey HSD post-hoc / 多组两两比较（Tukey HSD）A
Read-only
Inspect

Tukey HSD post-hoc / 多组两两比较（Tukey HSD） | USE WHEN: pairwise comparisons after one-way ANOVA with family-wise error control | ALIASES: Tukey, HSD, ANOVA后续, 两两比较, post hoc, 多重比较 | RETURNS: 两两比较 P 值

NameRequiredDescriptionDefault
groupsYes{组名: [数值...]}
tumor_growth_inhibitionTumour growth inhibition (TGI) / 抑瘤率 TGIA
Read-only
Inspect

Tumour growth inhibition (TGI) / 抑瘤率 TGI | USE WHEN: compute TGI (%) from control vs treated tumour volumes, with a group comparison | ALIASES: 抑瘤率, TGI, 肿瘤抑制率, tumour growth inhibition, 抑瘤实验, 药效评价 | RETURNS: TGI 与统计检验

NameRequiredDescriptionDefault
ctrl_volumesYes对照组体积
treat_volumesYes给药组体积
tumor_microenvironmentTumour microenvironment / 肿瘤微环境分析A
Read-only
Inspect

Tumour microenvironment / 肿瘤微环境分析 | USE WHEN: assess tumour immune microenvironment from TILs / CD8 / PD-L1 data (typing + prognosis hint) | ALIASES: 肿瘤微环境, TME, 免疫浸润, TIL, PD-L1, CD8, immune microenvironment | RETURNS: 微环境分型与预后提示

NameRequiredDescriptionDefault
tils_pctNoTILs 占比
cd8_countNoCD8+ 细胞数
pd_l1_posNoPD-L1 阳性率
cancer_typeNo癌种
tumor_volume_curveTumour volume & growth curve / 肿瘤体积计算A
Read-only
Inspect

Tumour volume & growth curve / 肿瘤体积计算 | USE WHEN: tumour volume from caliper length/width and the growth curve over days | ALIASES: 肿瘤体积, 长短径算体积, 生长曲线, tumor volume, caliper, 生长抑制曲线 | RETURNS: 体积与生长曲线

NameRequiredDescriptionDefault
daysNo天数
widthsNo短径
lengthsNo长径
group_nameNo组名
two_way_anovaTwo-way ANOVA / 双因素方差分析B
Read-only
Inspect

Two-way ANOVA / 双因素方差分析 | USE WHEN: two factors: main effects and interaction | ALIASES: 双因素方差分析, two-way ANOVA, 交互作用, 主效应, 两个因素 | RETURNS: 主效应与交互

NameRequiredDescriptionDefault
valuesNo观测值
group_aYes因素 A 水平
group_bYes因素 B 水平

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 39 tool updates
    • Removedacademic_polish
    • Removedacceptance_probability
    • Removedcover_letter
    • Removeddecision_arbitration
    • Removeddeep_peer_review
    • Removeddesign_next_step
    • Removeddesign_protocol_generate
    • Removeddesign_type_decision
    • Removedexperiment_plan_generate
    • Removedidea_ignite
    • Removedliterature_compare
    • Removedliterature_digest_card
    • Removedliterature_review_outline
    • Removedlocal_literature_compare
    • Removedmermaid_diagram
    • Removednsfc_grant_analysis
    • Removedpaper_draft_one_click
    • Removedpaper_outline
    • Removedpaper_six_agent_review
    • Removedpdf_text_pico
    • Removedpico_structure
    • Removedpre_submission_review
    • Removedproposal_review
    • Removedprotocol_export
    • Removedprotocol_export_check
    • Removedresults_paragraph_writer
    • Removedrevision_response
    • Removedsection_writer
    • Removedsystematic_review_assist
    • Removedtechnical_roadmap
    • Removedtop_journal_mining
    • Removedtopic_feasibility
    • Removedtopic_gaps
    • Removedtopic_generate
    • Removedtopic_lit_review
    • Removedtopic_novelty
    • Removedtopic_ppt
    • Removedtopic_prominence
    • Removedtopic_research_plan
  2. 39 tool updates
    • Addedacademic_polish
    • Addedacceptance_probability
    • Addedcover_letter
    • Addeddecision_arbitration
    • Addeddeep_peer_review
    • Addeddesign_next_step
    • Addeddesign_protocol_generate
    • Addeddesign_type_decision
    • Addedexperiment_plan_generate
    • Addedidea_ignite
    • Addedliterature_compare
    • Addedliterature_digest_card
    • Addedliterature_review_outline
    • Addedlocal_literature_compare
    • Addedmermaid_diagram
    • Addednsfc_grant_analysis
    • Addedpaper_draft_one_click
    • Addedpaper_outline
    • Addedpaper_six_agent_review
    • Addedpdf_text_pico
    • Addedpico_structure
    • Addedpre_submission_review
    • Addedproposal_review
    • Addedprotocol_export
    • Addedprotocol_export_check
    • Addedresults_paragraph_writer
    • Addedrevision_response
    • Addedsection_writer
    • Addedsystematic_review_assist
    • Addedtechnical_roadmap
    • Addedtop_journal_mining
    • Addedtopic_feasibility
    • Addedtopic_gaps
    • Addedtopic_generate
    • Addedtopic_lit_review
    • Addedtopic_novelty
    • Addedtopic_ppt
    • Addedtopic_prominence
    • Addedtopic_research_plan
  3. 39 tool updates
    • Removedacademic_polish
    • Removedacceptance_probability
    • Removedcover_letter
    • Removeddecision_arbitration
    • Removeddeep_peer_review
    • Removeddesign_next_step
    • Removeddesign_protocol_generate
    • Removeddesign_type_decision
    • Removedexperiment_plan_generate
    • Removedidea_ignite
    • Removedliterature_compare
    • Removedliterature_digest_card
    • Removedliterature_review_outline
    • Removedlocal_literature_compare
    • Removedmermaid_diagram
    • Removednsfc_grant_analysis
    • Removedpaper_draft_one_click
    • Removedpaper_outline
    • Removedpaper_six_agent_review
    • Removedpdf_text_pico
    • Removedpico_structure
    • Removedpre_submission_review
    • Removedproposal_review
    • Removedprotocol_export
    • Removedprotocol_export_check
    • Removedresults_paragraph_writer
    • Removedrevision_response
    • Removedsection_writer
    • Removedsystematic_review_assist
    • Removedtechnical_roadmap
    • Removedtop_journal_mining
    • Removedtopic_feasibility
    • Removedtopic_gaps
    • Removedtopic_generate
    • Removedtopic_lit_review
    • Removedtopic_novelty
    • Removedtopic_ppt
    • Removedtopic_prominence
    • Removedtopic_research_plan
  4. 39 tool updates
    • Addedacademic_polish
    • Addedacceptance_probability
    • Addedcover_letter
    • Addeddecision_arbitration
    • Addeddeep_peer_review
    • Addeddesign_next_step
    • Addeddesign_protocol_generate
    • Addeddesign_type_decision
    • Addedexperiment_plan_generate
    • Addedidea_ignite
    • Addedliterature_compare
    • Addedliterature_digest_card
    • Addedliterature_review_outline
    • Addedlocal_literature_compare
    • Addedmermaid_diagram
    • Addednsfc_grant_analysis
    • Addedpaper_draft_one_click
    • Addedpaper_outline
    • Addedpaper_six_agent_review
    • Addedpdf_text_pico
    • Addedpico_structure
    • Addedpre_submission_review
    • Addedproposal_review
    • Addedprotocol_export
    • Addedprotocol_export_check
    • Addedresults_paragraph_writer
    • Addedrevision_response
    • Addedsection_writer
    • Addedsystematic_review_assist
    • Addedtechnical_roadmap
    • Addedtop_journal_mining
    • Addedtopic_feasibility
    • Addedtopic_gaps
    • Addedtopic_generate
    • Addedtopic_lit_review
    • Addedtopic_novelty
    • Addedtopic_ppt
    • Addedtopic_prominence
    • Addedtopic_research_plan
  5. 39 tool updates
    • Removedacademic_polish
    • Removedacceptance_probability
    • Removedcover_letter
    • Removeddecision_arbitration
    • Removeddeep_peer_review
    • Removeddesign_next_step
    • Removeddesign_protocol_generate
    • Removeddesign_type_decision
    • Removedexperiment_plan_generate
    • Removedidea_ignite
    • Removedliterature_compare
    • Removedliterature_digest_card
    • Removedliterature_review_outline
    • Removedlocal_literature_compare
    • Removedmermaid_diagram
    • Removednsfc_grant_analysis
    • Removedpaper_draft_one_click
    • Removedpaper_outline
    • Removedpaper_six_agent_review
    • Removedpdf_text_pico
    • Removedpico_structure
    • Removedpre_submission_review
    • Removedproposal_review
    • Removedprotocol_export
    • Removedprotocol_export_check
    • Removedresults_paragraph_writer
    • Removedrevision_response
    • Removedsection_writer
    • Removedsystematic_review_assist
    • Removedtechnical_roadmap
    • Removedtop_journal_mining
    • Removedtopic_feasibility
    • Removedtopic_gaps
    • Removedtopic_generate
    • Removedtopic_lit_review
    • Removedtopic_novelty
    • Removedtopic_ppt
    • Removedtopic_prominence
    • Removedtopic_research_plan
  6. 79 tool updates
    • Addedacademic_polish
    • Addedacceptance_probability
    • Removedact_cover_letter
    • Removedact_decision_arbitration
    • Removedact_deep_review
    • Removedact_design_decision
    • Removedact_design_export
    • Removedact_design_export_check
    • Removedact_design_generate
    • Removedact_design_next_step
    • Removedact_design_pico_refine
    • Removedact_experiment_plan
    • Removedact_idea_ignite
    • Removedact_lit_digest
    • Removedact_lit_systematic_review
    • Removedact_literature_compare
    • Removedact_material_mermaid
    • Removedact_nsfc_analysis
    • Removedact_paper_review
    • Removedact_pdf_local_compare
    • Removedact_pdf_pico
    • Removedact_pdf_review_outline
    • Removedact_polish
    • Removedact_pre_review
    • Removedact_predict_success
    • Removedact_proposal_review
    • Removedact_results_paragraph
    • Removedact_revision_track
    • Removedact_roadmap
    • Removedact_top_journal_mining
    • Removedact_topic_feasibility
    • Removedact_topic_gaps
    • Removedact_topic_generate
    • Removedact_topic_lit_review
    • Removedact_topic_novelty
    • Removedact_topic_ppt
    • Removedact_topic_prominence
    • Removedact_topic_research_plan
    • Removedact_write_chapter
    • Removedact_writing_one_click
    • Removedact_writing_outline
    • Addedcover_letter
    • Addeddecision_arbitration
    • Addeddeep_peer_review
    • Addeddesign_next_step
    • Addeddesign_protocol_generate
    • Addeddesign_type_decision
    • Addedexperiment_plan_generate
    • Addedidea_ignite
    • Addedliterature_compare
    • Addedliterature_digest_card
    • Addedliterature_review_outline
    • Addedlocal_literature_compare
    • Addedmermaid_diagram
    • Addednsfc_grant_analysis
    • Addedpaper_draft_one_click
    • Addedpaper_outline
    • Addedpaper_six_agent_review
    • Addedpdf_text_pico
    • Addedpico_structure
    • Addedpre_submission_review
    • Addedproposal_review
    • Addedprotocol_export
    • Addedprotocol_export_check
    • Addedresults_paragraph_writer
    • Addedrevision_response
    • Addedsanyu_job_status
    • Addedsection_writer
    • Addedsystematic_review_assist
    • Addedtechnical_roadmap
    • Addedtop_journal_mining
    • Addedtopic_feasibility
    • Addedtopic_gaps
    • Addedtopic_generate
    • Addedtopic_lit_review
    • Addedtopic_novelty
    • Addedtopic_ppt
    • Addedtopic_prominence
    • Addedtopic_research_plan
  7. 39 tool updates
    • Addedact_cover_letter
    • Addedact_decision_arbitration
    • Addedact_deep_review
    • Addedact_design_decision
    • Addedact_design_export
    • Addedact_design_export_check
    • Addedact_design_generate
    • Addedact_design_next_step
    • Addedact_design_pico_refine
    • Addedact_experiment_plan
    • Addedact_idea_ignite
    • Addedact_lit_digest
    • Addedact_lit_systematic_review
    • Addedact_literature_compare
    • Addedact_material_mermaid
    • Addedact_nsfc_analysis
    • Addedact_paper_review
    • Addedact_pdf_local_compare
    • Addedact_pdf_pico
    • Addedact_pdf_review_outline
    • Addedact_polish
    • Addedact_pre_review
    • Addedact_predict_success
    • Addedact_proposal_review
    • Addedact_results_paragraph
    • Addedact_revision_track
    • Addedact_roadmap
    • Addedact_top_journal_mining
    • Addedact_topic_feasibility
    • Addedact_topic_gaps
    • Addedact_topic_generate
    • Addedact_topic_lit_review
    • Addedact_topic_novelty
    • Addedact_topic_ppt
    • Addedact_topic_prominence
    • Addedact_topic_research_plan
    • Addedact_write_chapter
    • Addedact_writing_one_click
    • Addedact_writing_outline
  8. 211 tool updates
    • Removedact_animal_arrive_check
    • Removedact_animal_dosing
    • Removedact_animal_organ_coef
    • Removedact_animal_randomize
    • Removedact_animal_sample_size
    • Removedact_animal_survival
    • Removedact_animal_tgi
    • Removedact_animal_tumor_volume
    • Removedact_atac_diff
    • Removedact_bayesian_design
    • Removedact_be_compare
    • Removedact_bridge_convert
    • Removedact_calibration
    • Removedact_cell_tool
    • Removedact_checklist_check
    • Removedact_chip_peak
    • Removedact_citation_format
    • Removedact_classification_report
    • Removedact_cloning_design
    • Removedact_consort_flow
    • Removedact_crf_validate
    • Removedact_data_quick_stats
    • Removedact_ddi_check
    • Removedact_decision_followup
    • Removedact_decision_intake
    • Removedact_decision_report
    • Removedact_deg_analyze
    • Removedact_delong
    • Removedact_design_context
    • Removedact_design_gate_rules
    • Removedact_design_guide
    • Removedact_design_sample_size
    • Removedact_dose_convert_pharm
    • Removedact_dunn
    • Removedact_enrich
    • Removedact_fcs_cluster
    • Removedact_fcs_gate
    • Removedact_fibrosis_stage
    • Removedact_figure_legend
    • Removedact_file_columns
    • Removedact_forest_chart
    • Removedact_gene_pathway
    • Removedact_gene_search
    • Removedact_geo_pipeline
    • Removedact_gleason
    • Removedact_ic50_fit
    • Removedact_ihc_score
    • Removedact_iptw
    • Removedact_journal_recommend_v3
    • Removedact_km_curve
    • Removedact_lit_search
    • Removedact_lit_verify_citations
    • Removedact_literature_overlap
    • Removedact_lmm_formula
    • Removedact_meta_egger
    • Removedact_metabo_enrichment
    • Removedact_metabo_heatmap
    • Removedact_metabo_pca
    • Removedact_metabo_plsda
    • Removedact_metabo_volcano
    • Removedact_mif_phenotype
    • Removedact_ml_nomogram
    • Removedact_ml_parse
    • Removedact_ml_psi
    • Removedact_ml_shap
    • Removedact_nca_analyze
    • Removedact_nuclei_segment
    • Removedact_omics_integrate
    • Removedact_permutation_test
    • Removedact_pico_refine
    • Removedact_pkpd_eval
    • Removedact_pkpd_simulate
    • Removedact_ppi_network
    • Removedact_primer_design
    • Removedact_primer_tm
    • Removedact_prisma_flow
    • Removedact_protein_digest
    • Removedact_protein_enrichment
    • Removedact_protein_physchem
    • Removedact_protein_ptm
    • Removedact_psm_match
    • Removedact_pubmed_search
    • Removedact_qpcr_ddct
    • Removedact_randomize
    • Removedact_risk_ratio
    • Removedact_rnaseq_deg
    • Removedact_roc
    • Removedact_sas_r_code
    • Removedact_scrna_analyze
    • Removedact_scrna_celltype
    • Removedact_scrna_markers
    • Removedact_sequence_enzyme
    • Removedact_sequence_orf
    • Removedact_sequence_plasmid
    • Removedact_sequence_translate
    • Removedact_smd_balance
    • Removedact_stat_center
    • Removedact_survival_analyze
    • Removedact_table_one
    • Removedact_table_one_subgroup
    • Removedact_tme_analyze
    • Removedact_tox_eval
    • Removedact_tox_predict
    • Removedact_tukey_hsd
    • Removedact_two_way_anova
    • Addedanimal_dose_volume
    • Addedanimal_randomization
    • Addedanimal_sample_size
    • Addedanimal_survival_km
    • Addedarrive_checklist
    • Addedatac_diff
    • Addedbioequivalence_compare
    • Addedcalibration_curve
    • Addedcell_assay_calc
    • Addedchip_peak_calling
    • Addedcitation_format
    • Addedcitation_verify
    • Addedclassification_metrics
    • Addedclinical_basic_bridge
    • Addedcloning_design
    • Addedconsort_flowchart
    • Addedcrf_sdtm_validate
    • Addeddataframe_parse
    • Addeddataset_columns
    • Addeddataset_profile
    • Addeddelong_auc_compare
    • Addeddesign_gate_rules
    • Addeddesign_step_context
    • Addeddifferential_expression
    • Addeddna_translate
    • Addeddrug_interaction_check
    • Addeddunn_posthoc
    • Addedegger_bias_test
    • Addedevidence_followup
    • Addedevidence_intake
    • Addedevidence_report
    • Addedfibrosis_staging
    • Addedfigure_legend
    • Addedflow_cluster
    • Addedflow_gate
    • Addedforest_plot
    • Addedgene_info_lookup
    • Addedgene_pathway_disease
    • Addedgeo_dataset_pipeline
    • Addedgleason_grade_group
    • Addedgo_kegg_enrichment
    • Addedic50_fit
    • Addedihc_hscore
    • Addediptw_weighting
    • Addedjournal_recommend
    • Addedkaplan_meier
    • Addedlinear_mixed_model
    • Addedliterature_search_pmc
    • Addedmetabolite_heatmap
    • Addedmetabolite_pathway
    • Addedmetabolite_pca
    • Addedmetabolite_plsda
    • Addedmetabolite_volcano
    • Addedmif_phenotype
    • Addedmulti_omics_integrate
    • Addednomogram
    • Addednuclei_count
    • Addedorf_find
    • Addedorgan_coefficient
    • Addedpcr_primer_design
    • Addedpermutation_test
    • Addedpico_refine
    • Addedpk_nca
    • Addedpk_pd_assess
    • Addedpkpd_simulate
    • Addedplasmid_map
    • Addedppi_network
    • Addedprimer_tm
    • Addedprisma_flowchart
    • Addedpropensity_score_matching
    • Addedprotein_digest
    • Addedprotein_enrichment
    • Addedprotein_physchem
    • Addedprotein_ptm
    • Addedpsi_stability
    • Addedpubmed_search
    • Addedqpcr_bayesian_design
    • Addedqpcr_ddct
    • Addedrandomization_allocate
    • Addedrenal_dose_adjust
    • Addedreporting_checklist
    • Addedrestriction_sites
    • Addedrisk_ratio_or
    • Addedrnaseq_deg
    • Addedroc_auc
    • Addedsample_size_calculator
    • Addedsas_r_code
    • Addedscrna_annotate
    • Addedscrna_cluster
    • Addedscrna_markers
    • Addedshap_explain
    • Addedsmd_balance
    • Addedspecies_dose_convert
    • Addedstats_toolbox
    • Addedstudy_design_advisor
    • Addedsurvival_cox
    • Addedtable_one_baseline
    • Addedtable_one_subgroup
    • Addedtext_overlap_check
    • Addedtoxicity_predict
    • Addedtoxicology_assess
    • Addedtukey_hsd
    • Addedtumor_growth_inhibition
    • Addedtumor_microenvironment
    • Addedtumor_volume_curve
    • Addedtwo_way_anova
  9. 3 tool updates
    • Removedact_distribution_fit
    • Changedact_table_one1 field changed
      • changedInput schema / required
        Previous value: -[
-  "group_variable"
-]New value: +[
+  "file_id",
+  "group_variable"
+]
    • Changedact_table_one_subgroup1 field changed
      • changedInput schema / required
        Previous value: -[
-  "group_variable",
-  "subgroup_variable"
-]New value: +[
+  "file_id",
+  "group_variable",
+  "subgroup_variable"
+]
  10. 107 tool updates
    • First observedact_animal_arrive_check
    • First observedact_animal_dosing
    • First observedact_animal_organ_coef
    • First observedact_animal_randomize
    • First observedact_animal_sample_size
    • First observedact_animal_survival
    • First observedact_animal_tgi
    • First observedact_animal_tumor_volume
    • First observedact_atac_diff
    • First observedact_bayesian_design
    • First observedact_be_compare
    • First observedact_bridge_convert
    • First observedact_calibration
    • First observedact_cell_tool
    • First observedact_checklist_check
    • First observedact_chip_peak
    • First observedact_citation_format
    • First observedact_classification_report
    • First observedact_cloning_design
    • First observedact_consort_flow
    • First observedact_crf_validate
    • First observedact_data_quick_stats
    • First observedact_ddi_check
    • First observedact_decision_followup
    • First observedact_decision_intake
    • First observedact_decision_report
    • First observedact_deg_analyze
    • First observedact_delong
    • First observedact_design_context
    • First observedact_design_gate_rules
    • First observedact_design_guide
    • First observedact_design_sample_size
    • First observedact_distribution_fit
    • First observedact_dose_convert_pharm
    • First observedact_dunn
    • First observedact_enrich
    • First observedact_fcs_cluster
    • First observedact_fcs_gate
    • First observedact_fibrosis_stage
    • First observedact_figure_legend
    • First observedact_file_columns
    • First observedact_forest_chart
    • First observedact_gene_pathway
    • First observedact_gene_search
    • First observedact_geo_pipeline
    • First observedact_gleason
    • First observedact_ic50_fit
    • First observedact_ihc_score
    • First observedact_iptw
    • First observedact_journal_recommend_v3
    • First observedact_km_curve
    • First observedact_lit_search
    • First observedact_lit_verify_citations
    • First observedact_literature_overlap
    • First observedact_lmm_formula
    • First observedact_meta_egger
    • First observedact_metabo_enrichment
    • First observedact_metabo_heatmap
    • First observedact_metabo_pca
    • First observedact_metabo_plsda
    • First observedact_metabo_volcano
    • First observedact_mif_phenotype
    • First observedact_ml_nomogram
    • First observedact_ml_parse
    • First observedact_ml_psi
    • First observedact_ml_shap
    • First observedact_nca_analyze
    • First observedact_nuclei_segment
    • First observedact_omics_integrate
    • First observedact_permutation_test
    • First observedact_pico_refine
    • First observedact_pkpd_eval
    • First observedact_pkpd_simulate
    • First observedact_ppi_network
    • First observedact_primer_design
    • First observedact_primer_tm
    • First observedact_prisma_flow
    • First observedact_protein_digest
    • First observedact_protein_enrichment
    • First observedact_protein_physchem
    • First observedact_protein_ptm
    • First observedact_psm_match
    • First observedact_pubmed_search
    • First observedact_qpcr_ddct
    • First observedact_randomize
    • First observedact_risk_ratio
    • First observedact_rnaseq_deg
    • First observedact_roc
    • First observedact_sas_r_code
    • First observedact_scrna_analyze
    • First observedact_scrna_celltype
    • First observedact_scrna_markers
    • First observedact_sequence_enzyme
    • First observedact_sequence_orf
    • First observedact_sequence_plasmid
    • First observedact_sequence_translate
    • First observedact_smd_balance
    • First observedact_stat_center
    • First observedact_survival_analyze
    • First observedact_table_one
    • First observedact_table_one_subgroup
    • First observedact_tme_analyze
    • First observedact_tox_eval
    • First observedact_tox_predict
    • First observedact_tukey_hsd
    • First observedact_two_way_anova
    • First observedsanyu_list_actions

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