Vocab Master — 小升初英语单词深度学习系统
An MCP server that provides on-demand skill loading for AI coding assistants.
5,155,857 AI resources indexedWorking toward the world’s largest AI resource index.
Reusable instructions for compatible AI agents.
Field counts come from the latest full-catalog snapshot. A resource can belong to several fields and platforms.
An MCP server that provides on-demand skill loading for AI coding assistants.
Interpret Alpha and Beta diversity metrics from 16S rRNA sequencing results.
Cell (Cell Press) figure preparation: resolution (300-1000 DPI), formats (TIFF/PDF), RGB color, Avenir/Arial fonts, uppercase panel labels, strict image manipulation policies.
PNAS figure preparation: resolution (300-1000 PPI), formats (TIFF/EPS/PDF), strict RGB-only color, Arial/Helvetica fonts, italicized uppercase panel labels, automated image screening.
Create graphical abstracts, schematic diagrams, and scientific illustrations
Design and generate publication-quality charts — bar, line, pie, scatter, heatmaps, and interactive D3 visualizations.
Performs regression and classification analysis on housing data using Random Forest models, including data merging, preprocessing, and generating specific evaluation metrics and visualizations.
Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like workflow in Python; use when you need Wald tests, FDR correction, and optional LFC shrinkage for condition/batch/covariate designs.
Use Bio.Phylo to read/write phylogenetic trees and perform visualization and statistics; use when tree parsing/conversion, pruning/rerooting, distance calculation, or plotting is required.
Advanced Biopython modules for motifs, population genetics, sequence utilities, restriction analysis, clustering, and GenomeDiagram visualization; use when you need extended bioinformatics analysis beyond basic sequence I/O and alignment.
Builds diagnostic and prognostic classifiers on omics feature matrices with regularized logistic regression, random forest, and gradient-boosted trees, handling the p>>n regime, batch shortcut learning, class imbalance, and probability calibration. Use when building a classifier from expression, ...
Explains ML predictions on omics data with SHAP, LIME, and permutation importance, handling the correlated-feature trap, the conditional-vs-interventional Shapley choice, and the attribution-is-not-causation boundary. Use when interpreting an omics classifier, debugging shortcut/batch learning, o...
Try a broader search or clear some filters.
Platform labels describe intended use. Read each resource's author credits, requirements and available access methods.