Data analysis, machine learning, LLM apps, and prompt design (8.9K skills)
Build and check clinical machine learning workflows with PyHealth, from loading electronic health records, signals, or images to training and evaluation. Includes patient-level data splits, prediction task checks, model checkpoints, and medical code mapping.
Process mass spectrometry data using scripts for format conversion, signal cleanup, peak detection, and quantification across samples. Prepare protein and metabolite analyses with identification filtering, candidate annotations, data tables, and plots.
Process paired-end 16S sequencing reads in QIIME 2 to produce ASVs, distinct sequence variants, and taxonomic assignments. Check input files, primer settings, and read retention while preserving artifacts that record the processing history.
Work with genomic regions in Polars tables to find overlaps, nearby regions, covered bases, and gaps. Read and write bioinformatics files, query data with SQL, and check coordinate conventions when joining datasets or migrating from bioframe.
Build Bayesian models in PyMC to estimate effects and quantify uncertainty. Fit regression or hierarchical models, check sampling diagnostics, assess predictions against observed data, predict new groups, and compare models using predictive performance.
Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.
Read, query, filter, and write genomic files with pysam in Python. Extract regions, summarize variants, and count aligned bases while handling coordinate conventions, file indexes, headers, and the reference sequences needed to decode CRAM files.
Analyze gene-expression differences in bulk RNA-seq data using PyDESeq2. Validate count tables and experimental designs, test explicit comparisons, adjust for multiple testing, shrink effect estimates, and export statistical results with volcano and MA plots.
Build and optimize quantum circuits with PennyLane, including models that combine quantum circuits with PyTorch or JAX. Explore molecular energy estimation and optimization problems, validate values and gradients in simulation, and prepare tested circuits for provider plugins.
Explore public pathogen genome data to verify lineage names, summarize weekly sequence proportions, examine reporting delays, and compare mutation frequencies. Results include filters and denominators, with clear limits on what submitted sequences reveal about infections.
Solve optimization problems in Python with one or several competing goals. Define constraints, compare search algorithms, visualize trade-offs, and assess candidate solutions before choosing an option that fits your priorities.
Use PathML for local pathology image research, from loading whole-slide images and splitting them into tiles to preprocessing and quality checks. Plan multiplex measurements, analyze spatial relationships between cells, and run model inference in bounded batches.
Analyze drug concentration and response data to calculate exposure, fit models of drug movement and effects, and simulate dosing schedules. Produces exploratory research results with explicit assumptions and diagnostic checks.
Build Python machine learning workflows for classification, numerical prediction, and grouping similar data. Combine preprocessing with training, compare algorithms, tune model settings, and evaluate performance using data splits suited to the task.
Find biological pathways and functions enriched in gene lists or ranked gene data. Choose an appropriate statistical method, align gene identifiers, define comparison backgrounds, and turn results into interpretable tables and plots with multiple-testing corrections.
Combine laboratory animal welfare measurements into reference-relative RELSA severity scores. Analyze individual trajectories, explore forecasts for specified observation times, and derive candidate score zones, with explicit checks on normalization, reference selection, prediction intervals, and evaluation limits.
Use PyTDC to discover therapeutic machine-learning datasets, create task-appropriate training and test splits, and evaluate predictions against curated benchmarks. Also score selected molecules with supported scoring functions, with explicit download plans and bounded outputs.
Analyze biological sequences and microbial communities in Python. Process and align DNA, RNA, or proteins, build evolutionary trees, calculate diversity, visualize sample differences, test group differences, and convert common biological file formats.
Organize PyTorch models and data pipelines with PyTorch Lightning, automate training, save checkpoints, and track experiments. Includes templates and guidance for configuring training across multiple GPUs or TPUs.
Plan and audit HypoGeniC workflows that use language models to propose patterns from labeled text. Check configurations, dataset splits, cost limits, and saved predictions while keeping candidate hypotheses separate from scientific confirmation.
Process calibrated one-dimensional NMR signals into spectra, candidate peaks, and signed integration results. Supports manual phase adjustment, baseline correction, and recorded processing settings so results can be reviewed and reproduced.
Prepare and analyze genomic intervals, regions defined by chromosome coordinates, with Gtars. Validate BED files, compare overlaps and coverage, combine region sets, and plan tokenization or reference storage while tracking data provenance and resource limits.
Match scientific labels to ontology identifiers, standardized IDs for concepts such as tissues, cell types, and diseases. Checks existing IDs, labels, obsolete terms, and prefixes to support metadata annotation, submission preparation, and table audits.
Work with geographic points, lines, and areas in Python using GeoPandas. Guidance and local audit tools help check coordinate systems, geometry validity, spatial join matches, and export settings before trusting or sharing analysis results.