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。
Provides unified Python access to over 40 bioinformatics web services and databases for integrated biological data analysis.
Access over 40 bioinformatics web services and databases including UniProt, KEGG, and ChEMBL through a unified Python interface.
Accesses over 40 bioinformatics web services and databases through a unified Python interface for biological data retrieval and analysis.
Processes and analyzes high-throughput sequencing data to generate publication-quality genomic visualizations and quality control metrics.
Processes and analyzes physiological signals like ECG, EEG, and EDA using the NeuroKit2 Python library.
Automates Next-Generation Sequencing (NGS) data processing, quality control, and publication-quality visualization.
Infers gene regulatory networks from transcriptomics data using scalable GRNBoost2 and GENIE3 algorithms.
Automates scientific hypothesis generation and testing by combining observational data with research literature using large language models.
Analyzes and visualizes complex network structures and graph data using the Python NetworkX library.
Simplifies astronomical data processing and astrophysical calculations using the core Python Astropy library.
Queries the NCBI Gene database to retrieve comprehensive genetic information, metadata, and sequences for annotation and functional analysis.
Simplifies complex molecular informatics workflows by providing a Pythonic interface for RDKit with sensible defaults and built-in parallelization.
Facilitates advanced astronomical research and data processing through Python-based coordinate transforms, FITS manipulation, and cosmological modeling.
Simplifies molecular cheminformatics and drug discovery workflows with a Pythonic abstraction layer over RDKit.
Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic interface for RDKit.
Provides specialized tools for biological computation, sequence analysis, and programmatic access to NCBI databases using Biopython.
Builds discrete-event simulation models in Python to analyze complex systems involving queues, resources, and time-based processes.
Builds and optimizes complex discrete-event simulations using the SimPy framework for Python.
Develops, tests, and deploys machine learning models for clinical healthcare data using standardized pipelines and specialized medical architectures.
Provides unified access to 20+ genomic databases for sequence analysis, protein structure prediction, and rapid bioinformatics queries.
Processes and generates multimedia content including audio, video, images, and documents using the Google Gemini API.
Integrates managed vector databases into AI applications for production-grade RAG, semantic search, and recommendation systems.
Analyzes high-throughput sequencing data to perform quality control, normalization, and publication-quality visualization for NGS experiments.
Simulates and analyzes genome-scale metabolic models using constraint-based reconstruction and analysis (COBRA) techniques.
Automates tissue detection and tile extraction from gigapixel histopathology images for computational pathology deep learning pipelines.
Builds process-based discrete-event simulations in Python for modeling complex systems like logistics, manufacturing, and networks.
Predicts 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug design.
Queries NCBI Gene databases to retrieve comprehensive genomic data, including sequences, annotations, and functional pathways.
Executes complex autonomous research tasks across genomics, drug discovery, and clinical analysis using integrated biomedical databases.
Empowers protein research and design using state-of-the-art ESM3 and ESM C language models for sequence generation, structure prediction, and representation learning.
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