Data & Analytics

Convert complex Venn diagrams with more than 4 sets to clearer Upset.

Analyze data with `toxicity-structure-alert` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

Statistical modeling and regression analysis for biomedical data. Linear/logistic/ordinal regression, Cox proportional hazards, mixed-effects models, ANOVA, with odds ratios, hazard ratios, confidence intervals, and model diagnostics.

Automated generation of baseline characteristics tables (Table 1) for clinical research papers.

Generate publication-ready baseline characteristics tables (Table 1) for clinical research papers with automatic variable type detection, appropriate statistics (mean±SD, median[IQR, n(%)), group comparisons (t-test, chi-square), and APA formatting.

Analyze data with `survival-curve-risk-table` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

Kaplan-Meier survival analysis tool for clinical and biological research. Generates publication-ready survival curves with statistical tests.

Determines the appropriate Risk of Bias assessment scale for a medical study based on its design (RCT, Cohort, etc.), using PubMed metadata lookup or text analysis. Use when the user wants to know which quality assessment tool to use for a specific paper (given PMID or abstract).

Guided statistical analysis for test selection, assumption checks, power analysis, and APA-style reporting. Use when you need to choose an appropriate statistical test for your data and produce publication-ready results (including effect sizes and diagnostics).

Recommends appropriate statistical methods (T-test vs ANOVA, etc.) based.

Map spatial transcriptomics data from 10x Genomics Visium/Xenium onto.

Analyze data with `smiles-de-salter` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

Programmatically query public single-cell study metadata from the Broad Institute Single Cell Portal REST API when you need to search and filter datasets by organism, tissue, disease, or cell type without an API key.

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Feature importance, dependence plots, interaction effects, and fairness analysis for any black-box model.

A skill for performing sequence alignment using NCBI BLAST API. Supports nucleotide and protein sequence comparison against major biological databases.

Statistical visualization library integrated with pandas; use it when you need fast EDA of distributions, relationships, and categorical comparisons (e.g., box/violin/pair plots and heatmaps) with strong default aesthetics on top of matplotlib.

Deep generative models for single-cell omics; use when you need probabilistic batch correction (scVI), transfer learning, uncertainty-aware differential expression, or multimodal integration (totalVI/MultiVI).

Auto-annotate cell clusters from single-cell RNA data using marker genes.

A comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival; use it when you need to model censored time-to-event outcomes, fit Cox/RSF/GB models or Survival SVMs, evaluate with C-index/Brier score, or handle competing risks.

A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM).

Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata.

Use sanger chromatogram qa for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

Cloud-based quantum chemistry platform providing a Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformational search, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Suitable for tasks involving quantum chemistry calculations, molecular property prediction, DFT or semi-empirical methods, neural network potentials (AIMNet2), protein-ligand binding prediction, ...

Automates Risk of Bias 2 (ROB2) assessment for RCT papers by analyzing text against specific domains and synthesizing a report. Use when you need to assess the quality of a clinical trial paper or evaluate risk of bias.