meta-funnel-plot
Generate Meta-analysis funnel plots and perform publication bias testing. Takes CSV file with Meta-analysis data as input, outputs funnel plot PNG, Egger test and Begg test results.
Generate Meta-analysis funnel plots and perform publication bias testing. Takes CSV file with Meta-analysis data as input, outputs funnel plot PNG, Egger test and Begg test results.
Generate forest plots for meta-analysis of survival data. Input is a CSV file containing study names, HR and 95% confidence intervals, output forest plot PNG and data table CSV. Supports both R and Python scripts.
Generate forest plots for meta-analysis of continuous data. Input a CSV file containing study names, means, standard deviations, and sample sizes for experimental and control groups. Output forest plot PNG and data table CSV.
Generate meta-analysis forest plots for binary classification data. Input is a CSV file containing study names, event counts and sample sizes for experimental and control groups. Output includes forest plot PNG and data table CSV.
Generate Baujat plots for heterogeneity analysis. Identify studies that contribute most to the overall meta-analysis results and heterogeneity, helping discover potential outlier studies. Input meta-analysis data CSV, output Baujat plot PNG and contribution data CSV.
Generates Mermaid flowchart code and visual diagrams for pathophysiological.
A low-level plotting library for comprehensive customization. Use when fine-grained control over every plot element is needed, creating new types of charts, or integrating into specific scientific workflows. Can export to PNG/PDF/SVG for publication. For quick statistical charts, use seaborn; for interactive charts, use plotly; for journal-style, publication-ready multi-panel charts, use scientific-visualization.
Professional beautification tool for gene expression heatmaps, automatically adds clustering trees, color annotation tracks, and intelligently optimizes label layout.
Analyze data with `forest-plot-styler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
Show journal impact factor and quartile trends over 5 years.
Generates detailed text descriptions of medical images and charts for.
Generates the "Results" section for meta-analysis sensitivity analysis based on statistical tables and titles. Use when the user wants to describe sensitivity analysis results or format sensitivity tables for a meta-analysis paper.
Generates the "Risk of Bias" results section for a meta-analysis based on assessment tables and statistics. Use when the user wants to draft the risk of bias analysis text from provided data tables.
Generates a Meta-analysis results section description for funnel plots, including statistical tables (Egger's, Begg's, Trim & Fill) and figure legends. Supports English and Chinese outputs. Use when user provides a funnel plot image and statistics and wants a formatted report.
Generates a meta-analysis baseline characteristics section (text + table) from raw data. Supports Chinese and English. Use when the user provides baseline data and wants a formatted results section.
Generate standardized figure legends for scientific charts and graphs.
Generate hospital discharge summaries from admission data, hospital course.
Kaplan-Meier survival analysis tool for clinical and biological research. Generates publication-ready survival curves with statistical tests.
Automated generation of baseline characteristics tables (Table 1) for clinical research papers.
Analyze data with `volcano-plot-labeler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
Summarize core safety information from Investigator's Brochures for clinical.
Therapeutics Data Commons (PyTDC) for AI-ready therapeutic ML datasets and benchmarks; use it when you need standardized dataset loading, meaningful splits (e.g., scaffold/cold-start), and consistent evaluation for ADME/Toxicity/DTI/DDI or molecular optimization.
Access the RCSB Protein Data Bank (PDB) to search, download, and programmatically retrieve 3D macromolecular structures and metadata; use when you need structure discovery (text/sequence/3D similarity) or automated structural data ingestion for structural biology and drug discovery workflows.