「express」の検索結果

Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.

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。

Perform differential expression analysis of miRNAs between conditions using DESeq2 or edgeR with small RNA-specific considerations. Use when identifying miRNAs that change between treatment groups, disease states, or developmental stages.

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。

Differential abundance and state analysis for cytometry data. Compare cell populations between conditions using statistical methods. Use when testing for significant changes in cell frequencies or marker expression between groups.

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。

Perform differential expression analysis using DESeq2 in R/Bioconductor. Use for analyzing RNA-seq count data, creating DESeqDataSet objects, running the DESeq workflow, and extracting results with log fold change shrinkage. Use when performing DE analysis with DESeq2.

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。

Extract, filter, annotate, and export differential expression results from DESeq2 or edgeR. Use for identifying significant genes, applying multiple testing corrections, adding gene annotations, and preparing results for downstream analysis. Use when filtering and exporting DE analysis results.

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。

Quality control and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE 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。

Build tissue and condition-specific metabolic models using GIMME, iMAT, and INIT algorithms with expression data constraints. Create models that reflect cell-type specific metabolism. Use when building tissue-specific metabolic models or integrating transcriptomics with FBA.

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。

Analyze codon usage, calculate CAI (Codon Adaptation Index), and examine synonymous codon bias using Biopython. Use when analyzing coding sequences for expression optimization or evolutionary 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。

End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, classifier training with nested CV, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data.

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。

Quantify transcript expression using pseudo-alignment with Salmon or kallisto. Use when quantifying transcripts with Salmon or kallisto.

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。

Review SDS-PAGE or protein purification gel images using DNA sequence, protein sequence, base-pair length, expected protein size, and lane labels. Use when the user wants to judge whether a gel ran well, whether the main band matches the expected product, or whether there may be impurities, degradation, aggregation, or low expression.

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。

Protein design quality control, filtering thresholds, and ranking guidance. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage filtering pipelines, (5) Computing PyRosetta interface metrics (dG, SC, dSASA), (6) Checking biophysical properties (instability, GRAVY, pI), (7) Ranking designs with composite scoring. This skill provides research-backed thresholds from binder design competitions and published benchmarks.

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。

Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design. For backbone generation, use rfdiffusion or bindcraft. For ligand-aware design, use ligandmpnn. 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。

Quality control metrics and filtering thresholds for protein design. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage filtering pipelines, (5) Computing PyRosetta interface metrics (dG, SC, dSASA), (6) Checking biophysical properties (instability, GRAVY, pI), (7) Ranking designs with composite scoring. This skill provides research-backed thresholds from binder design competitions and published benchmarks.

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 for writing, reviewing, debugging, testing, or optimizing Python code that uses PyArrow arrays, schemas, Tables, RecordBatches, RecordBatchReaders, compute kernels, Dataset scans, Parquet, or Arrow IPC and C data interfaces. Trigger on schema drift, null/type semantics, chunking, batch streaming, predicate projection pushdown, conversion, and memory ownership. Do not use for pandas-only transformations, Polars-only expressions, generic SQL, Arrow Flight service design, C++/Rust Arrow, or Parquet work that does not use PyArrow.

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 has positioning and wants a reusable Xiaohongshu topic/title bank instead of asking AI to freely brainstorm, especially to map topics to user needs such as emotional resonance, growth proof, new insight, learning value, or identity expression. Trigger phrases: "选题库", "标题公式", "每天发什么", "批量选题", "爆款需求", "XHS topic bank", "title formulas". Do not use before account positioning and target audience are known.

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 has positioning and wants a reusable Xiaohongshu topic/title bank instead of asking AI to freely brainstorm, especially to map topics to user needs such as emotional resonance, growth proof, new insight, learning value, or identity expression. Trigger phrases: "选题库", "标题公式", "每天发什么", "批量选题", "爆款需求", "XHS topic bank", "title formulas". Do not use before account positioning and target audience are known.

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。

Migrate codebases between frameworks, languages, and API versions. Use when a user asks to convert JavaScript to TypeScript, migrate React class components to hooks, upgrade Vue 2 to Vue 3, migrate Python 2 to 3, update deprecated APIs, switch ORMs, convert Express to Fastify, or modernize legacy code. Handles incremental migration with backward compatibility.

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。

You are an expert in Chi, the lightweight, idiomatic Go HTTP router built on `net/http`. You help developers build composable HTTP services using Chi's middleware stack, route groups, URL parameters, sub-routers, and context-based request scoping — providing Express-like ergonomics while staying 100% compatible with Go's standard library.

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。

Clerk is a hosted authentication and user-management service with drop-in UI components, social login, email/password, organizations, RBAC and webhooks. Use when adding sign-in to Next.js, React, or Express apps, protecting routes with clerkMiddleware, setting up multi-tenant organizations and roles, or syncing Clerk users to your own database.

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。

Auth.js (formerly NextAuth.js) is an open-source authentication library that adds OAuth sign-in, magic links, credentials and WebAuthn to Next.js, SvelteKit, Express and other frameworks. Use when a user asks to add login with Google or GitHub, set up NextAuth v5, protect routes with middleware or proxy, add roles to the session, or connect a database adapter such as Drizzle or Prisma. Notes that the project is in maintenance mode under Better Auth.

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。

algorithmic-art workflow skill. Use this skill when the user needs Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

algorithmic-art workflow skill. Use this skill when the user needs Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

algorithmic-art workflow skill. Use this skill when the user needs Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。