Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Develop and train Graph Neural Networks using PyTorch Geometric for node classification, link prediction, and molecular modeling.
Develops, optimizes, and executes quantum circuits and algorithms across various hardware backends using the Qiskit framework.
Accelerates drug discovery and molecular science workflows using graph neural networks and PyTorch-based modeling.
Facilitates collaborative research ideation and hypothesis generation for scientists and academic researchers.
Generates publication-quality scientific figures and multi-panel layouts using Python libraries while adhering to journal-specific standards.
Streamlines genomics pipeline development and data management on the DNAnexus cloud platform using the dxpy Python SDK.
Builds and deploys serverless bioinformatics workflows using the Latch SDK and Registry.
Accesses the NIH Metabolomics Workbench API to retrieve metabolite structures, study metadata, and standardized chemical nomenclature for biomarker discovery.
Accesses and analyzes protein-protein interaction networks and functional enrichment data using the STRING API.
Performs rigorous statistical modeling, econometric analysis, and time series forecasting using the Statsmodels library.
Accesses comprehensive pharmacogenomics data including gene-drug interactions, CPIC guidelines, and allele functions for precision medicine.
Queries the PubChem database to retrieve chemical properties, perform structure searches, and access bioactivity data for over 110 million compounds.
Accesses and analyzes over 200 million AI-predicted protein structures for bioinformatics and structural biology research.
Accesses and analyzes comprehensive FDA regulatory data for drugs, medical devices, and food safety through the openFDA API.
Queries the ChEMBL database to retrieve bioactive molecule data, drug targets, and bioactivity measurements for medicinal chemistry.
Accesses the comprehensive BRENDA enzyme database to retrieve kinetic parameters, biochemical reactions, and metabolic pathway data.
Accesses and queries the Catalogue of Somatic Mutations in Cancer (COSMIC) to retrieve high-quality genomic data for precision oncology and cancer research.
Reads, manipulates, and writes genomic datasets including BAM, VCF, and FASTA files using a Pythonic interface to htslib.
Automates R&D data management by integrating Claude with the Benchling platform for biological entity tracking, inventory control, and lab notebook documentation.
Accesses the European Nucleotide Archive (ENA) to retrieve DNA/RNA sequences, raw reads, and genome assemblies for bioinformatics pipelines.
Generates publication-quality statistical graphics and complex data visualizations using a high-level, dataset-oriented Python interface.
Facilitates advanced molecular analysis and cheminformatics workflows including property calculation, substructure searching, and 3D coordinate generation.
Automates laboratory liquid handling workflows by writing Python-based Protocol API v2 scripts for Opentrons Flex and OT-2 robots.
Facilitates advanced biomedical literature research and programmatic access to the PubMed database using E-utilities and complex query syntax.
Analyzes mass spectrometry data using Python bindings for OpenMS to process complex proteomics and metabolomics workflows.
Provides direct REST API access to UniProt for protein sequence retrieval, identifier mapping, and comprehensive functional annotation searches.
Facilitates drug discovery and therapeutic machine learning with curated datasets, ADMET benchmarks, and molecular optimization oracles.
Performs fast, scalable non-linear dimensionality reduction for visualization, clustering, and high-dimensional data analysis.
Accesses and processes NCBI Gene Expression Omnibus (GEO) data for transcriptomics and functional genomics research.
Performs comprehensive single-cell RNA-seq analysis including quality control, clustering, and trajectory inference using the Scanpy framework.
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