Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Accesses the UniProt REST API to search, retrieve, and map protein sequence and functional data directly within scientific workflows.
Accesses over 230 million purchasable chemical compounds for virtual screening, drug discovery, and molecular docking studies.
Builds and deploys serverless bioinformatics pipelines using the Latch SDK and cloud infrastructure.
Streamlines biomedical data management and genomics workflow automation on the DNAnexus cloud platform.
Streamlines deep learning development by organizing PyTorch code into scalable, boilerplate-free LightningModules and automated training workflows.
Accesses and analyzes over 200 million AI-predicted protein structures from the AlphaFold DB for structural biology and drug discovery.
Performs advanced molecular analysis, manipulation, and chemical informatics tasks using the RDKit toolkit.
Integrates NCBI Gene data access into Claude for querying sequences, functional annotations, and genomic metadata.
Generates and tests scientific hypotheses from observational data and research literature to accelerate empirical discovery and predictive modeling.
Manages medical imaging data by reading, writing, and anonymizing DICOM files while providing guidance on pixel extraction and metadata manipulation.
Provides programmatic access to global statistical data, demographics, and economic indicators via the Data Commons knowledge graph.
Executes complex autonomous biomedical research tasks across genomics, drug discovery, and clinical analysis using integrated databases and code execution.
Develops, tests, and deploys healthcare-specific machine learning models using clinical data, medical coding systems, and physiological signals.
Implements reinforcement learning workflows including agent training, custom environment design, and parallelized experimentation using the Stable Baselines3 library.
Conducts automated exploratory data analysis on over 200 scientific file formats to extract metadata, assess quality, and generate comprehensive documentation reports.
Facilitates advanced astronomical research and data analysis using Python for coordinate transformations, FITS file manipulation, and cosmological calculations.
Performs advanced survival analysis and time-to-event modeling in Python using specialized machine learning techniques for censored data.
Accesses and analyzes comprehensive pharmaceutical data from DrugBank including molecular structures, drug interactions, targets, and pharmacological properties.
Processes and visualizes billion-row tabular datasets exceeding available RAM through lazy, out-of-core DataFrame operations.
Provides specialized algorithms and workflows for comprehensive time series analysis, including classification, forecasting, and anomaly detection.
Implements, fine-tunes, and deploys pre-trained transformer models for natural language processing, computer vision, and audio tasks.
Automates the end-to-end scientific research lifecycle from data-driven hypothesis generation to the production of publication-ready LaTeX manuscripts.
Designs, simulates, and executes quantum circuits across diverse hardware backends and simulators using Google's open-source framework.
Facilitates machine learning on genomic interval data by training embeddings for regions, single-cell ATAC-seq, and associated metadata.
Queries and analyzes millions of scholarly works, authors, and institutions using the OpenAlex API to facilitate comprehensive bibliometric research.
Explains machine learning model predictions and feature importance using Shapley values for improved transparency and debugging.
Manages and analyzes annotated data matrices for single-cell genomics and large-scale biological datasets in Python.
Manages biological datasets with end-to-end lineage tracking, ontology-based curation, and FAIR data principles using a unified Python API.
Detects system hardware capabilities and generates strategic recommendations for optimized scientific computing and data processing tasks.
Accelerates reinforcement learning workflows through high-performance training, optimized environment vectorization, and seamless multi-agent integration.
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