데이터·AI 스킬

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.

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• Finding variants that overlap genomic regions
• Merging regions and measuring covered bases
• Summarizing sequencing read depth

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.

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• Compare gene expression between treatment and control groups
• Analyze expression differences while accounting for batch effects
• Validate count tables against sample metadata

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.

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• Find datasets for therapeutic machine-learning tasks
• Plan and create task-appropriate data splits
• Evaluate predictions with benchmark-specific metrics

Read, analyze, modify, and visualize molecular structures with RDKit in Python. Calculate molecular properties, search for similar structures or specific structural patterns, apply reaction rules, and generate 2D or 3D coordinates with detailed control over processing.

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• Calculating properties for a collection of molecules
• Searching for molecules with similar structures
• Filtering molecules by specific structural patterns

Build evolutionary trees from related nucleotide or protein sequences using MAFFT with IQ-TREE or FastTree. Review branch support, a measure of evidence for each grouping, and produce tree summaries and optional visualizations.

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• Build gene trees from related nucleotide sequences
• Compare evolutionary relationships within protein families
• Review branch support and analysis logs

Inspect and transform local DICOM medical imaging files, including metadata, image frames, and compression formats. Generate technical inventories and preview images, check installed codecs, and review pseudonymized derivatives without treating those checks as proof of de-identification.

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• Create a technical inventory of local DICOM files
• Check compression support and frame memory needs
• Export a frame as a non-diagnostic preview

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.

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• Tracking weekly lineage proportions in submitted sequences
• Verifying current Pango lineage names
• Examining delays between sample collection and reporting

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.

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• Calculate exposure and half-life from concentration data
• Compare models of drug concentration over time
• Simulate dosing schedules for research

Plan and review PufferLib reinforcement learning workflows, where agents learn through rewards. Covers version-specific environment checks, parallel execution, training and evaluation plans, plus checkpoint inspection before loading saved models.

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• Choose APIs for a specific PufferLib version
• Check environment behavior before training
• Create training and evaluation plans with separate seeds

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.

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• Filtering and summarizing large CSV files
• Migrating pandas processing code to Polars
• Joining and reshaping multiple tables

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.

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• Finding enriched pathways among differentially expressed genes
• Running GSEA on a complete ranked gene list
• Calculating gene-set scores for individual samples or cells

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.

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• Planning tile extraction from whole-slide images
• Reviewing preprocessing and image quality
• Validating multiplex measurements and spatial graphs

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.

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• Calculate and compare microbiome diversity
• Align DNA, RNA, or protein sequences
• Build and compare evolutionary trees

Analyze single-cell RNA-seq data with Scanpy, from quality checks and normalization to cell grouping, exploratory marker genes, and plots. Bundled scripts also prepare sample-level count summaries and support conversion of R objects to h5ad.

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• Check data quality and normalize single-cell measurements
• Visualize cell groups with UMAP or t-SNE
• Explore marker genes and review cell-type labels

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.

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• Restructure PyTorch training code into LightningModules
• Build reusable data preparation and loading pipelines
• Configure checkpoints, early stopping, and experiment logs

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.

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• Check quantum circuit values and gradients in simulation
• Train hybrid quantum and PyTorch or JAX models
• Estimate molecular energies with VQE

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.

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• Estimate regression effects with uncertainty
• Model differences across patients or groups
• Inspect sampling diagnostics and predictive checks

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.

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• Validate paired FASTQ files and sample metadata
• Remove primers and denoise reads with DADA2
• Assign taxonomy using a compatible classifier

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.

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• Building classification or numerical prediction models
• Grouping similar records and visualizing the results
• Combining missing-value handling with model training

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.

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• Checking geographic data before analysis
• Reviewing coordinate systems and transformations
• Auditing matches between points and areas

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.

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• Convert raw one-dimensional NMR signals into spectra
• Check peak positions against reference signals
• Review phase and baseline corrections

Prepare small image tiles from whole-slide histology scans with Histolab. Inspect tissue regions, choose random, grid, or score-based extraction, standardize H&E staining, and organize tile datasets with patient-level splits and visual checks.

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• Extracting training tiles from whole-slide histology scans
• Previewing tissue masks and tile locations
• Standardizing H&E stain appearance

Analyze pooled CRISPR screens from FASTQ reads or guide-count tables using MAGeCK. Validate inputs, assess library and replicate quality, and rank enriched or depleted genes with effect sizes and false discovery rates, which estimate false-positive proportions.

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• Counting guide reads from FASTQ files
• Checking library representation and replicate consistency
• Comparing treatment and control screens

Clean and compare tandem mass spectra, measurements used to study molecular fragments, with matchms. Standardize metadata, filter peaks, search reference libraries, and export ranked candidate matches with similarity scores and matched-peak counts.

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• Clean MS/MS spectra and standardize metadata
• Search reference libraries for compound candidates
• Compare spectra using similarity scores and matched peaks