Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Facilitates collaborative research ideation to generate novel hypotheses, identify research gaps, and develop innovative scientific methodologies.
Predicts high-accuracy 3D protein-ligand binding poses using state-of-the-art diffusion-based deep learning models.
Accesses and retrieves nucleotide sequences, raw reads, and genomic metadata from the European Nucleotide Archive (ENA).
Automates financial budget-vs-actual variance analysis with professional reporting, automated commentary, and executive summaries.
Solves complex single and multi-objective optimization problems using evolutionary algorithms and Pareto front analysis.
Detects system hardware capabilities to provide strategic recommendations for computationally intensive scientific tasks.
Analyzes cryptocurrency market sentiment, social trends, and on-chain data to generate actionable trading insights and risk assessments.
Provides interpretability and transparency for machine learning models by explaining predictions and identifying key feature importance.
Create publication-quality statistical graphics and complex data visualizations using the Seaborn Python library.
Queries the NHGRI-EBI GWAS Catalog for genetic variant associations, study metadata, and summary statistics.
Provides rapid access to 20+ genomic databases and bioinformatics tools for gene searching, sequence alignment, and protein structure prediction.
Analyzes, manipulates, and visualizes phylogenetic and hierarchical trees for genomic research and bioinformatics.
Builds and deploys production-ready generative AI agents leveraging Google Cloud's Vertex AI and Gemini models.
Converts chemical structures into numerical representations for machine learning using over 100 specialized featurizers.
Conducts real-time academic research and technical documentation searches using Perplexity's Sonar models with automated citations.
Manages and tracks AI/ML model versions, lineage, and performance metrics directly within your development environment.
Automates the tracking, management, and performance monitoring of machine learning model versions within your development workflow.
Optimizes deep learning model performance, accuracy, and training efficiency through automated architectural analysis and advanced algorithm selection.
Automates the end-to-end creation, training, and evaluation of machine learning models using sophisticated AutoML techniques.
Queries and retrieves global clinical study data from ClinicalTrials.gov via API v2 for medical research and patient matching.
Accesses and analyzes somatic mutation data from the world's largest cancer genomics database for precision oncology research.
Automates the creation of complex Excel pivot tables and data visualizations using natural language commands.
Optimizes neural network performance by fine-tuning architectures, hyperparameters, and training schedules to maximize accuracy and efficiency.
Accesses the comprehensive BRENDA enzyme database to retrieve kinetic parameters, reaction equations, and biochemical data for metabolic research.
Provides deep insights and interpretability for machine learning models using advanced techniques like SHAP and LIME.
Analyzes historical time-series data to predict future values and identify temporal patterns like seasonality and trends.
Provides a comprehensive suite of tools for processing, normalizing, and visualizing high-throughput sequencing data like ChIP-seq and RNA-seq.
Automates the configuration and integration of MLflow and Weights & Biases to streamline machine learning experiment management.
Optimizes machine learning model performance by automatically searching for ideal hyperparameter configurations using advanced search strategies.
Provides a specialized toolkit for applying machine learning to molecular property prediction, drug discovery, and materials science.
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