「awe」の検索結果

Workflow for integrating matched or partially matched omics layers into shared latent structure and cross-modal interpretation.

日本語の概要は準備中です。原文の説明を表示しています。

Workflow for RNA modification analysis such as m6A peak calling, differential modification, and transcript-level visualization.

日本語の概要は準備中です。原文の説明を表示しています。

End-to-end EHR predictive modeling pipeline with PyHealth, covering dataset loading, task definition, model training, evaluation, calibration, and clinical interpretation.

日本語の概要は準備中です。原文の説明を表示しています。

End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.

日本語の概要は準備中です。原文の説明を表示しています。

Workflow for copy-number estimation, segmentation, annotation, and visualization in sequencing-based assays.

日本語の概要は準備中です。原文の説明を表示しています。

Cell-free protein synthesis (CFPS) planning and optimization guidance. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).

日本語の概要は準備中です。原文の説明を表示しています。

Workflow for ligand-receptor communication inference in single-cell or spatial data with sender-receiver summaries and cautious interpretation.

日本語の概要は準備中です。原文の説明を表示しています。

Workflow for fine-mapping, colocalization, mediation, pleiotropy analysis, and Mendelian randomization.

日本語の概要は準備中です。原文の説明を表示しています。

Python-first workflow for bulk RNA-seq expression intake, normalization, sample QC, and downstream-ready matrices.

日本語の概要は準備中です。原文の説明を表示しています。

Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-design-qc. For AlphaFold2 prediction, use alphafold2-multimer. For Chai prediction, use chai1-structure-prediction.

日本語の概要は準備中です。原文の説明を表示しています。

Binder design campaign planning, monitoring, and troubleshooting. Use this skill when: (1) Planning a complete binder design campaign, (2) Converting high-level goals into runnable pipelines, (3) Assessing campaign health and pass rates, (4) Diagnosing why designs are failing QC, (5) Estimating time, cost, and expected yields, (6) Selecting between design tools for a specific target. This skill orchestrates the other protein design tools. For individual tool parameters, use the specific tool skills.

日本語の概要は準備中です。原文の説明を表示しています。

Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.

日本語の概要は準備中です。原文の説明を表示しています。

Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.

日本語の概要は準備中です。原文の説明を表示しています。

ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai1-structure-prediction or boltz-structure-prediction. For QC thresholds, use protein-design-qc.

日本語の概要は準備中です。原文の説明を表示しています。

Access UniProt for protein sequence and annotation retrieval. Use this skill when: (1) Looking up protein sequences by accession, (2) Finding functional annotations, (3) Getting domain boundaries, (4) Finding homologs and variants, (5) Cross-referencing to PDB structures. For structure retrieval, use pdb. For sequence design, use proteinmpnn.

日本語の概要は準備中です。原文の説明を表示しています。

End-to-end protein design pipeline guide across preparation, generation, validation, and filtering. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design-tool-selection. For QC thresholds, use protein-design-qc.

日本語の概要は準備中です。原文の説明を表示しています。

Workflow for retrieving public omics datasets, sequences, annotations, and literature-linked biological resources.

日本語の概要は準備中です。原文の説明を表示しています。

Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn. For ligand-aware design, use ligandmpnn.

日本語の概要は準備中です。原文の説明を表示しています。

Structure similarity search with Foldseek. Use this skill when: (1) Finding similar structures in PDB/AFDB databases, (2) Structural homology search, (3) Database queries by 3D structure, (4) Finding remote homologs not detected by sequence, (5) Clustering structures by similarity. For sequence similarity, use uniprot BLAST. For structure prediction, use chai or boltz.

日本語の概要は準備中です。原文の説明を表示しています。

Structure prediction using Boltz-1/Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For Chai prediction, use chai.

日本語の概要は準備中です。原文の説明を表示しています。

Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or chai. For QC thresholds, use protein-qc.

日本語の概要は準備中です。原文の説明を表示しています。

ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.

日本語の概要は準備中です。原文の説明を表示しています。

First-time setup for protein design tools. Use this skill when: (1) User is new and hasn't run any tools yet, (2) Commands fail with "file not found" or "modal: command not found", (3) Modal authentication errors occur, (4) User asks how to get started or set up the environment, (5) biomodals directory is missing or tools aren't working.

日本語の概要は準備中です。原文の説明を表示しています。

Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For solubility optimization, use solublempnn.

日本語の概要は準備中です。原文の説明を表示しています。