data science & ml Claude 스킬을 발견하세요. 53개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Infers gene regulatory networks from transcriptomics data using scalable GRNBoost2 and GENIE3 algorithms.
Develops, tests, and deploys machine learning models for clinical healthcare data using standardized pipelines and specialized medical architectures.
Streamlines computational molecular biology tasks including sequence analysis, NCBI database integration, and structural protein modeling.
Executes complex autonomous research tasks across genomics, drug discovery, and clinical analysis using integrated biomedical databases.
Performs advanced biological computation, sequence analysis, and programmatic access to NCBI databases using Python.
Builds discrete-event simulation models in Python to analyze complex systems involving queues, resources, and time-based processes.
Accesses and retrieves genomic data, including DNA/RNA sequences and raw reads, from the European Nucleotide Archive (ENA) via REST APIs and FTP.
Simulates high-performance fluid dynamics using pseudospectral methods and Python-based HPC workflows.
Provides unified access to 20+ genomic databases and analysis methods for rapid bioinformatics research and sequence analysis.
Simulates complex fluid dynamics using high-performance Python pseudospectral methods for Navier-Stokes and geophysical flow equations.
Automates tissue detection and tile extraction from gigapixel histopathology images for computational pathology deep learning pipelines.
Designs, simulates, and executes quantum circuits on simulators and real quantum hardware using Google's Cirq framework.
Generates interactive, publication-quality scientific and statistical charts using the Plotly Python library.
Performs constraint-based metabolic modeling and simulation for systems biology and metabolic engineering applications.
Predicts high-accuracy 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug discovery.
Simulates and analyzes genome-scale metabolic models using constraint-based reconstruction and analysis (COBRA) techniques.
Automates the generation, refinement, and testing of scientific hypotheses using data-driven insights and literature integration.
Builds process-based discrete-event simulations in Python for modeling complex systems like logistics, manufacturing, and networks.
Executes complex autonomous biomedical research tasks including genomics, drug discovery, and clinical data analysis.
Predicts 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug design.
Develops and deploys specialized machine learning models for healthcare using clinical datasets, medical coding systems, and deep learning architectures.
Accesses and analyzes comprehensive FDA regulatory data for drugs, medical devices, food safety, and substances through the openFDA API.
Empowers protein research and design using state-of-the-art ESM3 and ESM C language models for sequence generation, structure prediction, and representation learning.
Predicts high-accuracy 3D binding poses of small molecule ligands to protein targets using state-of-the-art diffusion models.
Performs constraint-based reconstruction and analysis of metabolic models using Python for systems biology and metabolic engineering.
Processes and analyzes complex physiological signals including ECG, EEG, and EDA using a comprehensive Python toolkit.
Generates interactive, publication-quality data visualizations and dashboards using the Plotly Python library.
Infers gene regulatory networks from transcriptomics data using scalable gradient boosting and random forest algorithms.
Accesses the Human Metabolome Database to retrieve metabolite properties, clinical biomarker data, and spectral information for metabolomics research.
Analyzes high-throughput sequencing data to perform quality control, normalization, and publication-quality visualization for NGS experiments.
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