'express' 검색 결과

Write and troubleshoot n8n expressions, the formulas that pass and transform data between workflow steps. Covers field references, webhook inputs, dates, strings, and choosing between expressions and Code nodes for data transformations.

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• Fixing expression syntax errors
• Using webhook data in downstream steps
• Formatting dates and strings

Search BioStudies and ArrayExpress by keywords or retrieve study information using an accession, a study’s registration identifier. Returns concise summaries and can save complete API responses to a file for further inspection.

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• Search for studies related to RNA
• Find single-cell studies in ArrayExpress
• Retrieve study details by accession

Write and troubleshoot n8n expressions, the formulas that pass and transform data between workflow steps. Correct field references, read webhook inputs, format dates and text, and query nested JSON while checking for silently missing results.

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• Include webhook form data in Slack messages
• Fix expressions that return null or literal text
• Extract and total values from nested JSON

Node.js, Express, Next.js의 API와 서버 데이터 처리를 설계하고 검토합니다. 데이터베이스 조회, 캐시, 인증, 오류 처리를 정리합니다.

• API 설계와 업무 로직 및 데이터 조회 분리
• 중복 데이터베이스 조회 줄이기
• 캐시와 비동기 처리 도입

Build Node.js APIs and backend services with Express/Fastify. The skill guides authentication, database integration, error handling, and operational practices such as logging and health checks, which report whether a service is working.

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• Build a REST API with Express/Fastify
• Add authentication and database integration
• Organize middleware and error handling

Node.js, Express, Next.js의 API와 데이터 처리를 설계하고 검토합니다. 역할 분리, 데이터베이스 최적화, 캐시와 인증 구현 예시를 제공합니다.

• API를 설계하고 처리 역할을 나눕니다
• 데이터베이스 질의 횟수를 줄입니다
• 데이터 조회에 캐시를 추가합니다

Guide for integrating GTEx tissue expression data with ENCODE regulatory elements. Use when users need to check if a gene is expressed in a tissue, correlate regulatory elements with expression, or validate ENCODE findings against GTEx. Trigger on: GTEx, tissue expression, gene expression levels, expression atlas, eQTL, tissue-specific expression, TPM values.

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Node.js, Express, Next.js의 서버 개발에 사용할 패턴을 제공합니다. API 설계, 데이터 조회, 캐시, 인증, 오류 처리를 정리하고 데이터 접근과 업무 규칙을 분리합니다.

• API 설계와 서버 처리 역할 분담
• 불필요한 데이터베이스 쿼리 줄이기
• 캐시 및 비동기 처리 도입

Predict spatial gene expression from H&E-stained tissue images using DeepSpot-M. Query a released panel of about 19,000 genes and combine predictions from small image tiles into slide-wide maps for research analysis.

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• Mapping predicted marker-gene expression across a tumor section
• Predicting expression across slides without matching gene-expression assays
• Adding predicted expression to a histology image analysis pipeline

Organize Node.js, Express, and TypeScript APIs into layers with clear responsibilities. This guide covers request handling, business logic, database access, input validation, error tracking, configuration, and testing to support consistent implementation and refactoring across services.

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• Adding an API endpoint with clearly separated responsibilities
• Organizing database access through Prisma
• Adding input validation and error tracking

Build Node.js, Express, and TypeScript APIs with consistent structure. Separate request handling, business logic, and database access, while following guidance for input validation, error tracking, configuration, and tests when adding features or refactoring existing services.

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• Adding an API endpoint
• Separating business logic from database access
• Standardizing input validation and error handling

Create flyers, social posts, business cards, and other visuals from Adobe Express templates. Choose a template, customize its text and background color, and receive a design link, with additional editing and export options depending on the available tools.

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• Creating an event flyer
• Designing an Instagram post
• Customizing business card text and colors

Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.

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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.

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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.

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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.

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Use hosted DNA models to predict gene structures, expression levels, and regions that regulate gene activity. Start from a gene symbol, genomic coordinates, or a DNA sequence, without installing model weights or running a local GPU.

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• Finding promoter regions in a DNA sequence
• Predicting splice donor and acceptor sites
• Estimating gene expression for a specified cell type

Routes bulk RNA-seq requests to workflows for generating expression counts and checking quality, or comparing gene expression between groups. Checks input data, sample information, and analysis requirements before directing the next step.

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• Generate expression counts from FASTQ files
• Compare gene expression between sample groups
• Plan quality checks and statistical analysis together

Controls facial expressions in Seedance 2.0 with FACS (Facial Action Coding System) Action Unit codes — muscle-level direction (AU12 = lip-corner puller, AU6 = cheek raiser) instead of emotion labels. Use whenever the user wants precise facial acting, a forced/uncanny/mixed expression, micro-performance in a close-up, monologue or dialogue facial beats, a 'which AU code for anger/fear/disgust' answer, or to generate a FACS reference sheet for a character. Pairs with higgsfield-soul Micro-Expressions (named expressions), higgsfield-audio (dialogue + lip-sync), and higgsfield-gpt-image-2 (the reference-sheet image).

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Analyze gene-expression differences in bulk RNA-seq data using PyDESeq2. Validate count tables and experimental designs, test explicit comparisons, adjust for multiple testing, shrink effect estimates, and export statistical results with volcano and MA plots.

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• Compare gene expression between treatment and control groups
• Analyze expression differences while accounting for batch effects
• Validate count tables against sample metadata

Query public single-cell and spatial gene expression data by tissue, disease, or cell type. Retrieve selected expression values, cell metadata, and summary counts for analysis and comparisons across datasets without first downloading entire collections.

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• Explore cell types within a tissue
• Retrieve expression values for selected marker genes
• Compare cells across tissues and datasets

Design and improve server-side applications using patterns for Node.js, Express, and Next.js API routes. Code examples cover API structure, database access, caching, authentication, error handling, and background jobs to guide implementation choices.

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• Organize API endpoints and application logic
• Reduce unnecessary database queries
• Add authentication and permission checks

Build web applications from a bundled Express, Vite, React, Tailwind CSS, and Drizzle ORM template. Follow a schema-first workflow to create pages, backend APIs, and connected forms, with guidance for styling, testing, and deployment.

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• Building a dashboard with data displays
• Creating an admin panel with forms and APIs
• Prototyping an online store

Add a complete dashboard panel in plain TypeScript, from server routes and data services to the visible component and registration. The workflow checks existing routing patterns and includes request failure handling, caching, and saved panel state.

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• Adding a dashboard panel across server and browser code
• Connecting routes to Express or http.createServer
• Adding caching and cooldowns after repeated failures