“deep”的搜索结果

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2024 bioRxiv; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

中文简介正在准备中。当前显示原文说明。

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

中文简介正在准备中。当前显示原文说明。

Deep, iterative, multi-source research that explores a topic from multiple perspectives, detects contradictions, and produces a comprehensive vault note with fact-level citations and a confidence map. Use this skill whenever the user says /deep-research, "deep research X", "research X thoroughly", "investigate X deeply", "I need to really understand X", or wants research that goes beyond a quick web search. Also use when the user wants to understand a complex, contested, or multi-faceted topic, compare competing approaches with evidence, or build a comprehensive mental model they can trust for decisions. This is heavier than /research — it searches web, academic papers, Reddit, and the vault, then loops through exploration and critique rounds until the evidence base is solid. When in doubt between /research and /deep-research, prefer this one if the topic is complex, contested, or the user wants depth over speed.

中文简介正在准备中。当前显示原文说明。

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2024 bioRxiv; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

中文简介正在准备中。当前显示原文说明。

Multi-angle deep-research workflow over Ahmad's OpenMark knowledge graph (13k+ bookmarks). Use whenever Ahmad asks anything that requires more than one search to answer well — "what's the landscape of X in my saves", "do a deep dive on Y", "research X across my bookmarks", "compare what I saved about X vs Y", "give me everything on X". Also use when fast-search returned thin results and Ahmad pushed back ("dig deeper", "more results", "actually research it"). Pairs Neo4j graph search with WebFetch on top picks to bring back fresh page content. Triggers on "deep research", "dig deeper", "research", "landscape", "everything on", "thorough", "comprehensive".

中文简介正在准备中。当前显示原文说明。

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

中文简介正在准备中。当前显示原文说明。

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

中文简介正在准备中。当前显示原文说明。

帮助设计用法精简、内部集中处理复杂逻辑的模块。通过统一术语和判断标准，评估模块是否易用、实现替换位置是否合适，以及是否便于测试。

• 简化模块的调用方式
• 集中分散的复杂逻辑
• 选择可替换实现的位置

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.

中文简介正在准备中。当前显示英语简介。

• 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

Create an implementation plan grounded in code relationships and verified source files, with proposed changes, test scenarios, risks, and a structured handoff for an implementation agent. It can also revisit and strengthen an existing plan.

中文简介正在准备中。当前显示英语简介。

• Planning a new feature
• Assessing a shared API change before implementation
• Strengthening and rechecking an existing plan

Manage mathematical proof projects across Codex runs, keeping sources, attempts, and unresolved steps organized. Prepare manual GPT Pro handoffs for difficult steps, then check returned arguments and edit the proof for clarity.

中文简介正在准备中。当前显示英语简介。

• Continue a proof from a previous run
• Prepare a manual GPT Pro handoff for a difficult lemma
• Track proof sources and unresolved steps

Research a topic through five web searches, five GitHub data collections, and five X/Twitter data collections using DeepAPI. Bring coverage, relevant repositories, and social discussion together in a concise, readable Markdown report.

中文简介正在准备中。当前显示英语简介。

• Research a topic across web pages, GitHub, and X/Twitter
• Compare web coverage with relevant repositories
• Include social discussion in topic research

Explores an existing codebase at quick, deep, or architecture-focused levels. Summarizes structure, function calls, and dependencies, then produces documentation or a handoff for implementation without changing the code.

中文简介正在准备中。当前显示英语简介。

• Getting oriented in an unfamiliar codebase
• Understanding how a specific feature works
• Mapping layers and circular dependencies

Replace the object detector in VSS Alerts Blueprint verification mode with an ONNX model. This guide covers model placement, interpreting detection outputs, DeepStream configuration, TensorRT engine builds, redeployment, and common loading or mount failures.

中文简介正在准备中。当前显示英语简介。

• Replace the verification detector with a YOLO11 ONNX model
• Investigate DeepStream custom parser loading failures
• Fix ONNX mounts and TensorRT engine rebuild settings

Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger for its internals: SOPVideoProcessor, DeepStream GEBD model (e.g. DDM) via Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.

中文简介正在准备中。当前显示原文说明。

Use DeepSeek's chat API: list models, run chat completions, check account balance. Trigger phrases: deepseek, deepseek chat, deepseek api.

中文简介正在准备中。当前显示原文说明。

Optional design-improvement pass for when you have spare usage to drain. Finds the shallowest modules in the code the spec touches, researches a deeper design, and proposes refactors that shrink interfaces and hide decisions — behavior held constant, tests green before and after. Proposes §I/§V/§T edits, never silent rewrites. Triggers when the user says "deepen this", "improve the design", "this module feels shallow", "pull complexity down", "use spare budget on the codebase", or invokes /ck:deepen. Leans on the codebase-design skill's deep-module vocabulary when present.

中文简介正在准备中。当前显示原文说明。

Deepseek 联网搜索工具。一键调用 AI 联网搜索能力，快速获取实时信息。当用户需要使用 Deepseek 搜索、联网搜索、AI 搜索获取实时信息时使用。触发词：Deepseek搜索、deepseek websearch、联网搜索、AI搜索、ds搜索。

中文简介正在准备中。当前显示原文说明。

Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller or when highest germline calling accuracy is required.

中文简介正在准备中。当前显示原文说明。

GTM prospecting, enrichment, outreach, and Deepline Play work/audits. Providers: adyntel,affinity,ai_ark,akta,allegrow,amplemarket,apify,attention,attio,aviato,bettercontact,bigquery,bluesky,bounceban,brave,browserbase,builtwith,cbinsights,clay,clickhouse,cloudflare,contactout,contextdev,crunchbase,crustdata-v3,customer_db,databricks,dataforseo,datagma,dealroom,deepline_ip_to_company,deepline_native,deeplineagent,discolike,dnb,dropleads,edges,emailbison,emailguard,enformion,enigma,exa,findymail,firecrawl,fireflies,firmable,forager,fullenrich,fundable,generic_http,getleads,gong,google_ads_audiences,google_workspace,govfiles,grain,hackernews,harvestapi,heyreach,hubspot,hunter,icypeas,instantly,intercom,ipqs,kaspr,kernel,leadmagic,lemlist,limadata,linkedin_ads_audiences,luma,lusha,meta_audiences,millionverifier,nooks,notion,openmart,opensosdata,openwebninja,outreach,parallel,peopledatalabs,pitchbook,podscan,polymarket,postgres,predictleads,prospeo,quickenrich,rapidapi,redshift,rocketreach,salesforce,….

中文简介正在准备中。当前显示原文说明。

Use when surfacing architectural friction and proposing deepening refactors (shallow modules → deep modules) for testability and AI-navigability. Triggers on "improve architecture", "find deepening opportunities", "where is this codebase shallow", "architecture review". Emits structured candidates + precise patches; delegates ALL rendering to mk:preview. NOT for diagram/HTML rendering (see mk:preview); NOT for trade-off deliberation (see mk:party); NOT for plan critique (see mk:plan-ceo-review).

中文简介正在准备中。当前显示原文说明。

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

中文简介正在准备中。当前显示原文说明。

Document a project's structure, commands, and specific conventions in a hierarchy of AGENTS.md files. Analyze directory complexity to choose useful locations, update existing guidance, and check that child files avoid repeating their parents.

中文简介正在准备中。当前显示英语简介。

• Create project guidance for coding agents
• Refresh existing AGENTS.md documentation
• Document rules specific to complex directories

Design modules that hide complex behavior behind a small, clear interface. Use shared design principles to review dependencies, choose where behavior can be swapped, and make code easier to test and maintain.

中文简介正在准备中。当前显示英语简介。

• Simplifying a module's interface
• Reviewing where to separate responsibilities
• Designing code that is easier to test