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。
Predicts high-accuracy 3D protein-ligand binding poses using state-of-the-art diffusion-based deep learning models.
Queries the NCBI ClinVar database to interpret genetic variant pathogenicity and annotate genomic data for research and medicine.
Optimizes data processing workflows with high-performance Polars expressions, lazy evaluation, and efficient DataFrame manipulations.
Applies medicinal chemistry rules and structural filters to prioritize drug-like compounds in molecular discovery workflows.
Solves complex single and multi-objective optimization problems using evolutionary algorithms and Pareto front analysis.
Provides a comprehensive toolkit for rigorous statistical modeling, econometric analysis, and time-series forecasting within the Claude environment.
Enables parallel and distributed computing in Python to scale data processing beyond memory limits.
Processes and analyzes high-performance genomic interval data for computational biology and machine learning applications.
Simulates high-performance computational fluid dynamics using pseudospectral methods and Python-based analysis.
Facilitates advanced mass spectrometry data analysis using the Python interface to the OpenMS library for proteomics and metabolomics.
Facilitates direct REST API access to the KEGG database for biological pathway analysis, gene mapping, and molecular interaction research.
Parses and manipulates Flow Cytometry Standard (FCS) files, converting biological data into NumPy arrays and CSV formats for scientific analysis.
Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering tasks.
Streamlines biomedical data management and genomics workflow automation on the DNAnexus cloud platform.
Performs complex biological computation, sequence analysis, and bioinformatics workflows using the Biopython library.
Processes and visualizes billion-row tabular datasets exceeding available RAM through lazy, out-of-core DataFrame operations.
Executes exact symbolic mathematics in Python to solve equations, perform calculus, and manipulate algebraic expressions with mathematical precision.
Accesses and analyzes comprehensive pharmaceutical data from DrugBank including molecular structures, drug interactions, targets, and pharmacological properties.
Performs advanced survival analysis and time-to-event modeling in Python using specialized machine learning techniques for censored data.
Processes digital pathology whole slide images (WSI) through automated tissue detection, tile extraction, and specialized image preprocessing.
Generates and designs novel proteins using evolutionary scale language models for sequence, structure, and functional analysis.
Facilitates machine learning on genomic interval data by training embeddings for regions, single-cell ATAC-seq, and associated metadata.
Conducts automated exploratory data analysis on over 200 scientific file formats to extract metadata, assess quality, and generate comprehensive documentation reports.
Accelerates reinforcement learning workflows through high-performance training, optimized environment vectorization, and seamless multi-agent integration.
Designs, simulates, and executes quantum circuits across diverse hardware backends and simulators using Google's open-source framework.
Implements, fine-tunes, and deploys pre-trained transformer models for natural language processing, computer vision, and audio tasks.
Queries and analyzes millions of scholarly works, authors, and institutions using the OpenAlex API to facilitate comprehensive bibliometric research.
Models complex discrete-event systems where entities interact with shared resources over time using process-based Python simulations.
Automates the end-to-end scientific research lifecycle from data-driven hypothesis generation to the production of publication-ready LaTeX manuscripts.
Manages biological datasets with end-to-end lineage tracking, ontology-based curation, and FAIR data principles using a unified Python API.
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