「modeling」の検索結果

End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.

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。

Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.

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。

Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

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。

Analyzes time-to-event data using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression with lifelines. Builds survival models from clinical and omics features. Use when predicting patient survival or modeling time-to-event outcomes.

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。

Bulk transcriptomics differential expression with count-aware modeling, design validation, contrast handling, thresholded exports, and publication-ready DE figures.

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 constraint-based metabolic modeling, context-specific models, gene essentiality, and systems-level 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 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。

Use when the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.

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。

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

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。

Expert SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.

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。

Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.

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。

Use when designing, querying, or troubleshooting the Financial Services Cloud data model — including managed-package (FinServ__ namespace) and Core FSC (standard objects, no namespace) object structures, household relationship modeling, financial account ownership, and the FSC rollup framework. Trigger keywords: FSC data model, FinancialAccount, FinancialHolding, FinancialAccountRole, FinancialAccountParty, FinServ namespace, household rollup, AssetsAndLiabilities, FinancialGoal, LifeEvent, AccountContactRelation FSC. NOT for setting up financial accounts and roles in Setup — use admin/financial-account-setup. NOT for household membership and rollup scheduling — use admin/household-model-configuration.

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。

Trigger this skill when the user wants to train, fine-tune, or adapt Gemma models (e.g. SFT, DPO, RLHF, Reward Modeling) on local hardware. Covers TRL, Unsloth, dataset preparation, validation, and GGUF/LiteRT conversion.

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。

Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.

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。

Use this skill when authoring reusable, idempotent MATLAB scripts that build System Composer architecture models via the architecture-modeling API — `systemcomposer.createModel`, `addComponent`, `addPort`, `setInterface`, `connect(srcPort, dstPort)`, interface dictionaries (.sldd) with `addInterface`/`addElement`, profiles/stereotypes with `Profile.createProfile` and `addStereotype`, or `systemcomposer.allocation.createAllocationSet`. Also trigger when debugging these APIs (connections that don't appear, interfaces that don't resolve, profile save errors, `createAllocationSet` signature-mismatch errors). Do NOT trigger for ad-hoc structural edits to an already-built model (adding one SubSystem, rewiring a port) — use `building-simulink-models` with `model_edit` for that.

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。

Build and sharpen a project's domain model. Use when discussing codebase terminology, writing or editing a GLOSSARY.md, or recording or editing an ADR.

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。

Independently review a CUMCM computation package before its claims enter validation or paper writing. Use only inside a generated independent-review package.

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。

Build backends and APIs with Directus headless CMS. Use when a user asks to create a headless CMS, build a content API without coding, set up a backend admin panel, create REST or GraphQL APIs from a database, manage content with roles and permissions, build a data platform with auto-generated APIs, or replace traditional CMS with a headless solution. Covers data modeling, auto-generated REST/GraphQL APIs, roles/permissions, flows (automation), file storage, and SDK integration.

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。

Designs database schemas with proper normalization, indexing, constraints, and tenant isolation patterns. Use when someone needs to create a new database schema, add multi-tenant support, design row-level security policies, or optimize table structures. Trigger words: database schema, table design, RLS, row-level security, foreign keys, indexes, migrations, ERD, data model, normalization.

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。

Apache Cassandra is a distributed NoSQL database designed for high availability and linear scalability. Learn CQL (Cassandra Query Language), data modeling with partition keys, replication strategies, and integration with Node.js using the DataStax driver.

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。

Write and run Blender Python scripts for 3D automation and procedural modeling. Use when the user wants to automate Blender tasks, create 3D models from code, run headless scripts, manipulate scenes, batch process .blend files, build geometry with bmesh, apply modifiers, generate procedural shapes, or import/export 3D models using the bpy API.

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。

Build with Amazon DynamoDB for fast, scalable NoSQL storage. Design tables with partition and sort keys, create GSI and LSI for flexible queries, enable streams for change data capture, and apply single-table design patterns for efficient data modeling.

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。

Covers scripting Autodesk 3ds Max, the 3D modeling and rendering application, with MAXScript and Python (pymxs): scene manipulation, object creation, material assignment, camera and light setup, batch operations, and file I/O. Use when tasks involve automating repetitive 3ds Max workflows, batch processing scenes, running scripts headless with 3dsmaxbatch, creating custom tools, or scripting scene setup for archviz, product visualization, or VFX.

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。

Optimize paid advertising campaigns across Google Ads, Meta, TikTok, LinkedIn, and other platforms. Use when tasks involve bid optimization, audience targeting, creative testing, ROAS improvement, attribution modeling, budget allocation, campaign structure, retargeting strategies, lookalike audiences, or reducing customer acquisition cost. Covers multi-platform campaign management and creative performance analysis.

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。