'expression' 검색 결과

Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build weighted gene co-expression networks to identify modules of co-regulated genes and relate them to phenotypes using WGCNA and CEMiTool. Detects hub genes and module-trait relationships from bulk or single-cell expression data. Use when finding co-expression modules, identifying hub genes, or relating gene networks to clinical or experimental variables.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Query NCBI GEO for gene expression datasets. Use when user asks about RNA-seq datasets, microarray data, expression data, GEO accessions, or finding public datasets. Triggers on "geo", "gene expression omnibus", "expression dataset", "RNA-seq dataset", "microarray dataset", "GSE", "GDS".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluate and simplify Boolean expressions using truth tables, algebraic laws (De Morgan, distributive, absorption, idempotent, consensus), and Karnaugh maps for up to six variables. Use when you need to reduce a Boolean expression to its minimal sum-of-products or product-of-sums form, verify logical equivalence between two expressions, or prepare a minimized function for gate-level implementation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Visual expression system for AI personas with emotion-based face switching. Use at session start to display the expression UI, and switch expressions whenever the emotional state changes during conversation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Validate n8n expression syntax and fix common errors. Use when writing n8n expressions, using {{}} syntax, accessing $json/$node variables, troubleshooting expression errors, or working with webhook data in workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fit probabilistic models to single-cell measurements for integration across experiments, cell annotation, combined RNA and protein or ATAC analysis, and gene expression comparisons. The skill covers input requirements, training checks, reference mapping, and interpretation of model uncertainty.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Integrating single-cell RNA data across experimental batches
• Assigning cell labels using partial annotations or a reference
• Analyzing RNA with protein or ATAC measurements

Prepare experiments for Ginkgo Cloud Lab by choosing a protocol, organizing required inputs, checking configured pricing, and navigating browser ordering. Covers protein expression, RNA synthesis, stability assays, and setup of measurement methods.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a protein expression and purification protocol
• Preparing inputs for mRNA or circRNA synthesis
• Reviewing stability assays and measurement method setup

Look up genes, sequences, protein structures, expression patterns, and disease associations across more than 20 bioinformatics resources using command-line tools or Python. Useful for interactive exploration, with results that can be saved for further analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Looking up gene details and sequences
• Exploring protein structures and residue annotations
• Comparing tissue expression and disease associations

Investigate failed Power Automate cloud flows through FlowStudio MCP. Inspect each action’s inputs and outputs, trace incorrect data upstream, fix expression or data issues, and replay a previous run to check the repair.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate why a cloud flow failed
• Find null values or incorrect field paths in expressions
• Inspect HTTP error response details

Audit GitHub Actions workflows and CI/CD configurations for supply chain risks. Use Python and YAML parsing to identify unpinned actions, unsafe event expressions, excessive permissions, dependency confusion, and potential exposure of secrets in build pipelines.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing GitHub Actions workflow security
• Checking action version pinning
• Finding unsafe event expressions in scripts

Find alternative words and phrases that fit a sentence, audience, and tone. Suggestions distinguish close synonyms from related expressions, explain differences in meaning or formality, and include brief usage examples when helpful.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Vary repeated words in a piece of writing
• Choose wording for formal or conversational contexts
• Compare subtle differences between similar words

Helps create, explain, debug, and optimize regular expressions for finding, extracting, or replacing text. Accounts for differences between programming languages and checks matching and nonmatching examples to identify overly broad patterns and potential performance problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create patterns for extracting text
• Explain unfamiliar regular expressions
• Fix unwanted matches and missed matches

Query Bgee for healthy wild-type gene expression metadata and receive concise summaries. The skill supports lookups that account for relationships between biological terms, keeps requests small, and saves raw results only when requested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Looking up healthy wild-type expression metadata
• Searching with relationships between biological terms
• Checking whether matching data exists

Write and review GitHub Actions workflows for syntax and structural correctness. Check quoting around expressions and special characters, then validate with actionlint, a workflow checker, to catch errors that ordinary YAML parsing can miss.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing new or edited GitHub Actions workflow files
• Checking quotes around expressions containing special characters
• Investigating workflow failures before any jobs start

Analyze single-cell RNA sequencing data with scVI and scANVI. Combine experimental batches while correcting technical differences, produce compact cell features for clustering, assign cell types using reference labels, and assess differences in gene expression between groups.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Integrating single-cell RNA sequencing batches
• Preparing cell features for clustering
• Assigning cell types from reference annotations

Use SymPy in Python to solve equations, simplify formulas, calculate derivatives and integrals, and work with matrices. Keep exact results such as fractions and square roots, then turn expressions into executable code or LaTeX.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Solve equations and verify the solutions
• Calculate derivatives, integrals, and limits
• Find matrix eigenvalues and inverse matrices

Work with mathematical formulas in Python to solve equations, calculate derivatives and integrals, and manipulate matrices. Keep exact results using symbols, then turn expressions into executable code or formatted LaTeX equations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Solve equations while keeping variables and exact values
• Calculate derivatives, integrals, and limits
• Find matrix eigenvalues and inverses