Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Analyzes single-cell omics data using deep generative models and probabilistic inference for batch correction and multimodal integration.
Facilitates advanced survival analysis and time-to-event modeling using the scikit-survival library in Python.
Queries the Open Targets Platform to identify and prioritize therapeutic drug targets through systematic data integration.
Provides comprehensive access to the Human Metabolome Database for metabolomics research, metabolite identification, and clinical biomarker discovery.
Performs fast, non-linear dimensionality reduction to visualize high-dimensional data and improve clustering performance.
Performs comprehensive single-cell RNA-seq analysis pipelines including quality control, clustering, and visualization using the Scanpy toolkit.
Accesses and integrates gene expression and functional genomics data from the NCBI Gene Expression Omnibus (GEO).
Extracts unique cognitive fingerprints and reasoning patterns from text using the Digital Human DNA framework.
Streamlines next-generation sequencing (NGS) data processing, quality control, and publication-quality genomic visualization.
Generates visually engaging, research-backed presentation slides for conferences, seminars, and academic defenses.
Enables parallel and distributed computing for large-scale data analysis using familiar pandas and NumPy APIs.
Builds and trains high-performance Graph Neural Networks for irregular data structures like social networks and molecular graphs.
Powers computational molecular biology tasks including sequence analysis, file parsing, and programmatic NCBI database access.
Automates research documentation and data management by integrating the LabArchives electronic lab notebook platform via its REST API.
Builds and analyzes phylogenetic trees to infer evolutionary relationships between biological sequences using industry-standard bioinformatics tools.
Enables comprehensive mass spectrometry data processing for proteomics and metabolomics workflows within Claude Code.
Evaluates scientific rigor and methodology to ensure research validity and identify potential biases using established evidence frameworks.
Accesses and analyzes 3D macromolecular structures from the RCSB Protein Data Bank for structural biology and drug discovery.
Builds, evaluates, and deploys classical machine learning models using the industry-standard scikit-learn library.
Analyzes and visualizes complex networks and graph data structures using the comprehensive Python NetworkX library.
Performs comprehensive bioinformatics analysis including sequence manipulation, phylogenetics, and microbial ecology statistics.
Accesses and analyzes comprehensive pharmaceutical data from DrugBank for drug discovery, pharmacology research, and interaction analysis.
Processes and analyzes high-performance genomic interval data using Rust-powered algorithms and Python bindings.
Streamlines the development, deployment, and management of serverless bioinformatics pipelines using the LatchBio SDK and cloud infrastructure.
Performs advanced time series machine learning tasks including classification, forecasting, and anomaly detection using a scikit-learn compatible interface.
Queries the Monarch Initiative knowledge graph to explore complex disease-gene-phenotype associations across multiple species.
Automates protein sequence optimization and experimental validation through cloud-based laboratory assays and API integration.
Automates cloud-based quantum chemistry workflows, molecular property predictions, and protein-ligand modeling via a unified Python API.
Provides programmatic access to the ChEMBL database for bioactive molecule research and medicinal chemistry.
Generates publication-ready clinical decision support documents and biomarker-stratified cohort analyses for pharmaceutical and clinical research.
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