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AlterLab-IEU/AlterLab-Academic-Skills - 4페이지

SkillsMP는 AlterLab-IEU/AlterLab-Academic-Skills에서 240개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.

AlterLab-IEU/AlterLab-Academic-Skills

수집된 skill 240개 중 40개를 표시합니다.

직업 분류
소프트웨어 개발자
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Store and query genomic variant data at scale with TileDB-VCF — ingest VCF/BCF into compressed TileDB arrays, add samples incrementally, run fast parallel region/sample queries, and export back to VCF. Use when managing population-genomics variant datasets…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Predicts protein-ligand binding poses with DiffDock diffusion-based molecular docking from PDB structures and SMILES, producing pose confidence scores for virtual screening and structure-based drug design. Use when docking ligands into a protein, generating…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Computes mass-spectral similarity and identifies compounds for metabolomics with matchms — comparing mass spectra, scoring similarity (cosine, modified cosine), and searching spectral libraries to annotate unknowns. Use when matching MS/MS spectra,…

원문 언어: 영어

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직업 분류
소프트웨어 개발자
설명

Applies medicinal-chemistry filters with the medchem library — drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, and molecular complexity metrics for compound prioritization and library cleanup. Use when filtering or triaging a compound…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Runs and analyzes molecular dynamics simulations with OpenMM and MDAnalysis — setting up protein and small-molecule systems, assigning force fields, running energy minimization and production MD, and analyzing trajectories (RMSD, RMSF, contact maps, free…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Queries the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biomedical relationships across genes, drugs, diseases, phenotypes, pathways, and biological processes. Use when exploring drug-disease or gene-disease links, building disease-centric…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Loads Therapeutics Data Commons (TDC, PyTDC) AI-ready drug-discovery datasets and benchmarks — ADME, toxicity, drug-target interaction (DTI), scaffold splits, and molecular oracles for therapeutic ML and pharmacological prediction. Use when fetching a…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Drives the Rowan cloud quantum-chemistry platform via its Python API for computational chemistry — pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Generates professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings — biomarker-stratified patient cohort analyses with outcomes and evidence-based treatment recommendation reports with decision algorithms,…

원문 언어: 영어

업데이트
직업 분류
일반 내과 의사
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Writes comprehensive clinical reports — case reports (CARE guidelines), diagnostic reports (radiology, pathology, lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP notes, H&P, discharge summaries) — with templates, regulatory…

원문 언어: 영어

업데이트
직업 분류
컴플라이언스 담당자
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Prepares ISO 13485 certification documentation for medical device Quality Management Systems (QMS) — gap analysis of existing documentation, Quality Manuals, required procedures and work instructions, and Medical Device Files. Use for ISO 13485 QMS…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Processes and analyzes physiological biosignals with the NeuroKit2 Python toolkit — ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Reads, writes, and manipulates DICOM (Digital Imaging and Communications in Medicine) medical imaging files with the pydicom Python library. Use when reading/writing/modifying DICOM data, extracting pixel data from CT, MRI, X-ray, or ultrasound images,…

원문 언어: 영어

업데이트
직업 분류
가정의학과 의사
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Generates concise (3-4 page), focused medical treatment plans in LaTeX/PDF format across all clinical specialties — general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management —…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Audits and repairs Markdown link health across a skills repo via a four-tier pipeline (config hardening, intra-repo file-ref fixes, external URL substitutions, residual exclusions) and enforces a Tier 3 substitution guardrail that prevents regressions of…

원문 언어: 영어

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직업 분류
소프트웨어 개발자
설명

Composes existing AlterLab skills into multi-agent agentic workflows using current Claude Code subagent and Claude Agent SDK orchestration patterns: parallel subagent fan-out, sequential pipelines, judge panels, adversarial verification, and loop-until-clean…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Scales pandas/NumPy workflows beyond memory with Dask distributed computing — parallel DataFrames, arrays, delayed task graphs, and cluster execution. Use when existing pandas/NumPy code must run on larger-than-RAM data or across clusters, for parallel file…

원문 언어: 영어

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직업 분류
데이터 과학자
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Exploratory data analysis (EDA) on a scientific data file — auto-detects the format, runs structure/quality/statistics checks, and writes a markdown EDA report with downstream recommendations. Use when asked to "explore", "analyze", "summarize", "profile", or…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
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Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering),…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
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Fast in-memory DataFrame analytics with Polars — lazy evaluation, parallel execution, and an Apache Arrow backend for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory, for 1-100GB datasets, ETL pipelines, or a faster pandas…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
설명

Bayesian modeling and probabilistic programming with PyMC — hierarchical models, MCMC (NUTS) sampling, variational inference, LOO/WAIC model comparison, and posterior predictive checks. Use when fitting Bayesian or hierarchical models, estimating posteriors…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Multi-objective optimization with pymoo — NSGA-II, NSGA-III, MOEA/D, Pareto-front computation, constraint handling, and standard benchmarks (ZDT, DTLZ). Use when solving multi-objective or constrained optimization problems, computing Pareto-optimal…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Scalable deep-learning training with PyTorch Lightning — organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, build data pipelines and callbacks, log to W&B or TensorBoard, and run distributed training (DDP, FSDP, DeepSpeed). Use…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Classical machine learning in Python with scikit-learn — algorithms, preprocessing, pipelines, and best-practice reference documentation. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
설명

Survival analysis and time-to-event modeling in Python with scikit-survival. Use when working with censored survival data, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating predictions with concordance index…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
설명

Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models,…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
설명

Process-based discrete-event simulation in Python with SimPy — processes, queues, shared resources, and time-based events. Use when simulating systems where entities contend for shared resources over time, such as manufacturing systems, service operations,…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
설명

Guided statistical analysis with hypothesis-test selection, assumption checking, power analysis, and APA-formatted reporting. Use when choosing the appropriate statistical test for data, verifying test assumptions, computing power/sample size, or producing…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
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Statistical modeling in Python with statsmodels — OLS, GLM, mixed models, and ARIMA with detailed diagnostics, residuals, and inference. Use when fitting specific model classes for econometrics, time series, or rigorous inference with coefficient tables and…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
설명

Symbolic mathematics in Python with SymPy — solve equations algebraically, perform calculus (derivatives, integrals, limits), manipulate algebraic expressions, work with symbolic matrices, and generate executable code from formulas. Use when exact symbolic…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
설명

Zero-shot univariate time-series forecasting with Google's TimesFM foundation model, producing point forecasts and prediction intervals from CSV/DataFrame/array inputs, with a preflight system checker for RAM/GPU. Use to forecast any univariate series (sales,…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Graph Neural Networks with PyTorch Geometric (PyG) — node and graph classification, link prediction, GCN, GAT, and GraphSAGE layers, heterogeneous graphs, and molecular property prediction. Use when building or training GNNs for geometric deep learning on…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
설명

Pre-trained transformer models with Hugging Face Transformers for NLP, computer vision, audio, and multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
설명

Nonlinear dimensionality reduction with UMAP — fast manifold learning for 2D/3D visualization, clustering preprocessing (e.g., HDBSCAN), and supervised or parametric UMAP. Use when projecting high-dimensional data to low dimensions for visualization,…

원문 언어: 영어

업데이트
직업 분류
데이터 과학자
설명

Out-of-core tabular analytics with Vaex for billion-row datasets that exceed RAM — lazy evaluation, fast aggregations, big-data visualization, and ML on a single machine. Use when working with large CSV/HDF5/Arrow/Parquet files, computing fast statistics on…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Chunked, compressed N-dimensional arrays for cloud storage with Zarr — parallel I/O, S3/GCS integration, and NumPy/Dask/Xarray compatibility. Use when storing or reading large N-D scientific arrays, streaming chunked data to/from cloud object stores, or…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Search and retrieve preprints from arXiv via the Atom API by keywords, authors, arXiv IDs, date ranges, or subject categories. Use when finding or fetching papers in physics, mathematics, computer science, quantitative biology, quantitative finance,…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
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Search the bioRxiv preprint server and retrieve paper metadata or download PDFs via its API. Use when finding life sciences preprints by keywords, authors, DOI, date ranges, or categories, or when conducting a biology literature review of…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Access the BRENDA enzyme database via its SOAP API to retrieve kinetic parameters (Km, kcat, Ki), reaction equations, organism data, and substrate-specific enzyme information indexed by EC number. Use when looking up enzyme kinetics, turnover numbers, or…

원문 언어: 영어

업데이트
직업 분류
소프트웨어 개발자
설명

Query cBioPortal via its keyless REST API for cancer genomics across TCGA, GENIE, MSK-IMPACT and hundreds of studies — somatic mutations, copy-number alterations (GISTIC), mRNA/protein expression, structural variants, and patient-level clinical/survival data.…

원문 언어: 영어

업데이트
수집된 skill 240개 중 40개를 표시합니다.