发现data science & ml类别的 Claude 技能。浏览 61 个技能,找到适合您 AI 工作流程的完美功能。
Performs comprehensive biological data analysis including sequence manipulation, phylogenetics, and microbial ecology statistics.
Accesses and processes NCBI Gene Expression Omnibus (GEO) data for transcriptomics and functional genomics research.
Accesses comprehensive pharmacogenomics data including gene-drug interactions, CPIC guidelines, and allele functions for precision medicine.
Streamlines deep learning development by organizing PyTorch code into scalable, modular structures for automated training and multi-GPU orchestration.
Conducts systematic, high-rigor peer reviews of scientific manuscripts and grant proposals across all major research disciplines.
Develop and train Graph Neural Networks using PyTorch Geometric for node classification, link prediction, and molecular modeling.
Accesses and analyzes comprehensive FDA regulatory data for drugs, medical devices, and food safety through the openFDA API.
Accesses and queries the Catalogue of Somatic Mutations in Cancer (COSMIC) to retrieve high-quality genomic data for precision oncology and cancer research.
Provides direct REST API access to UniProt for protein sequence retrieval, identifier mapping, and comprehensive functional annotation searches.
Accesses and analyzes RCSB Protein Data Bank (PDB) structures, metadata, and 3D coordinates for structural biology and drug discovery research.
Queries the ChEMBL database to retrieve bioactive molecule data, drug targets, and bioactivity measurements for medicinal chemistry.
Generates publication-ready clinical decision support documents and biomarker-stratified cohort analyses for pharmaceutical and clinical research.
Develops, optimizes, and executes quantum circuits and algorithms across various hardware backends using the Qiskit framework.
Performs high-performance genomic interval analysis and sequence tokenization using Rust-powered tools and Python bindings.
Extends pandas to enable powerful spatial operations and vector data analysis for complex geographic workflows.
Processes and analyzes mass spectrometry data using advanced spectral similarity metrics and automated metadata harmonization.
Simplifies bioinformatics workflows by providing unified access to over 20 genomic and proteomic databases for sequence analysis and protein modeling.
Simplifies molecular featurization for machine learning by providing a unified interface for over 100 descriptors, fingerprints, and pretrained embeddings.
Facilitates advanced Bayesian statistical modeling and probabilistic programming using the PyMC and ArviZ ecosystems.
Empowers molecular machine learning and drug discovery through advanced chemical featurization, property prediction, and Graph Neural Networks.
Simplifies molecular cheminformatics and drug discovery workflows with a Pythonic abstraction layer for RDKit.
Parses and manages Flow Cytometry Standard (FCS) files, enabling seamless conversion to NumPy arrays and metadata extraction for bioinformatics workflows.
Facilitates comprehensive Next-Generation Sequencing (NGS) data processing, quality control, and publication-quality visualization.
Solves complex multi-objective optimization problems using evolutionary algorithms to find Pareto-optimal solutions.
Builds, analyzes, and visualizes complex network structures and graph algorithms using Python's NetworkX library.
Analyzes crystal structures, generates phase diagrams, and integrates with the Materials Project for advanced computational materials science.
Predicts 3D protein-ligand binding poses and confidence scores using state-of-the-art diffusion models for structure-based drug design.
Filters and prioritizes molecular libraries using medicinal chemistry rules, structural alerts, and drug-likeness metrics.
Performs differential gene expression analysis on bulk RNA-seq data using Python's DESeq2 implementation.
Generates interactive, publication-quality scientific and statistical visualizations using the Plotly Python library.
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