「networks」の検索結果

Build enhancer-driven gene regulatory networks (eGRNs) by integrating single-cell RNA-seq and ATAC-seq using SCENIC+, CellOracle base GRNs, Pando, FigR, DIRECT-NET, TRIPOD, and scMEGA. Covers the accessibility-defines-enhancers principle, peak-to-gene linking and its cell-composition confound, the paired-vs-unpaired decision, and TF-region-gene eRegulon triplets. Use when analyzing 10x multiome or paired/unpaired scRNA+scATAC to infer cis-regulatory GRNs. For RNA-only regulons see scenic-regulons; for in silico TF perturbation see perturbation-simulation.

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Visualize biological networks (PPI, gene-regulatory, co-expression, pathway) with layout algorithm choice (ForceAtlas2, Fruchterman-Reingold, Kamada-Kawai, hive plots), edge bundling, community-based coloring, and reproducible seeds using NetworkX, PyVis, igraph, and Cytoscape automation. Use when rendering biological networks for static publication, interactive HTML exploration, or Cytoscape-format export.

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Detect introgression and admixture between species or populations using Dsuite (Malinsky 2021 fast D-statistics), Patterson's D / ABBA-BABA test (Green 2010; Durand 2011), f4-ratio and f-branch statistic (Malinsky 2018), TreeMix (Pickrell & Pritchard 2012), HyDe (Blischak 2018), QuIBL (Edelman 2019), sprime (Browning 2018), Twisst (Martin 2017), PhyloNet (Than 2008) for explicit phylogenetic networks, and qpAdm / qpGraph (Patterson 2012). Distinguish introgression from incomplete lineage sorting (ILS), ancestral structure, ghost-lineage admixture, and rate variation. Use when testing inter-species gene flow, dating admixture events, identifying introgressed segments, building phylogenetic networks for reticulate evolution, or applying the ABBAclustering (Koppetsch-Malinsky-Matschiner 2024) framework for divergent-species gene flow.

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Expert-level industrial automation covering PLCs, SCADA, industrial networks, safety systems, robot integration, and Industry 4.0 technologies.

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Expert-level computer networks covering OSI model, TCP/IP stack, routing protocols, transport layer, application protocols, network security, and software-defined networking.

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サプライチェーン最適化コンサルティングスキル。需要予測、在庫最適化、調達戦略、 物流ネットワーク設計、S&OP(Sales and Operations Planning)を支援。 Use when optimizing supply chain operations, improving inventory management, designing logistics networks, or conducting supply chain risk assessments. Triggers: "supply chain", "在庫最適化", "inventory optimization", "demand forecasting", "S&OP", "procurement strategy", "logistics network".

METAINFORMANT rules for directory src/metainformant/networks/workflow. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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METAINFORMANT rules for directory src/metainformant/networks/interaction. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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。

METAINFORMANT rules for directory src/metainformant/networks/regulatory. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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。

METAINFORMANT rules for directory src/metainformant/networks/config. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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。

METAINFORMANT rules for directory src/metainformant/networks. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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。

METAINFORMANT rules for directory src/metainformant/networks/analysis. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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。

METAINFORMANT rules for directory scripts/networks. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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。

METAINFORMANT rules for directory examples/networks. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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。

METAINFORMANT rules for directory docs/networks. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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。

METAINFORMANT rules for directory config/networks. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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。

This skill should be used when the user asks to "perform vulnerability scanning", "scan networks for open ports", "assess web application security", "scan wireless networks", "detect malware", "check cloud security", or "evaluate system compliance". It provides comprehensive guidance on security scanning tools and methodologies.

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 enhancer-driven gene regulatory networks (eGRNs) by integrating single-cell RNA-seq and ATAC-seq using SCENIC+, CellOracle base GRNs, Pando, FigR, DIRECT-NET, TRIPOD, and scMEGA. Covers the accessibility-defines-enhancers principle, peak-to-gene linking and its cell-composition confound, the paired-vs-unpaired decision, and TF-region-gene eRegulon triplets. Use when analyzing 10x multiome or paired/unpaired scRNA+scATAC to infer cis-regulatory GRNs. For RNA-only regulons see scenic-regulons; for in silico TF perturbation see perturbation-simulation.

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。

Visualize biological networks (PPI, gene-regulatory, co-expression, pathway) with layout algorithm choice (ForceAtlas2, Fruchterman-Reingold, Kamada-Kawai, hive plots), edge bundling, community-based coloring, and reproducible seeds using NetworkX, PyVis, igraph, and Cytoscape automation. Use when rendering biological networks for static publication, interactive HTML exploration, or Cytoscape-format export.

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。

Detect introgression and admixture between species or populations using Dsuite (Malinsky 2021 fast D-statistics), Patterson's D / ABBA-BABA test (Green 2010; Durand 2011), f4-ratio and f-branch statistic (Malinsky 2018), TreeMix (Pickrell & Pritchard 2012), HyDe (Blischak 2018), QuIBL (Edelman 2019), sprime (Browning 2018), Twisst (Martin 2017), PhyloNet (Solis-Lemus 2017) for explicit phylogenetic networks, and qpAdm / qpGraph (Patterson 2012; Lipson 2013). Distinguish introgression from incomplete lineage sorting (ILS), ancestral structure, ghost-lineage admixture, and rate variation. Use when testing inter-species gene flow, dating admixture events, identifying introgressed segments, building phylogenetic networks for reticulate evolution, or applying the ABBAclustering (Koppetsch-Malinsky-Matschiner 2024) framework for divergent-species gene flow.

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 enhancer-driven gene regulatory networks (eGRNs) by integrating single-cell RNA-seq and ATAC-seq using SCENIC+, CellOracle base GRNs, Pando, FigR, DIRECT-NET, TRIPOD, and scMEGA. Covers the accessibility-defines-enhancers principle, peak-to-gene linking and its cell-composition confound, the paired-vs-unpaired decision, and TF-region-gene eRegulon triplets. Use when analyzing 10x multiome or paired/unpaired scRNA+scATAC to infer cis-regulatory GRNs. For RNA-only regulons see scenic-regulons; for in silico TF perturbation see perturbation-simulation.

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。

Visualize biological networks (PPI, gene-regulatory, co-expression, pathway) with layout algorithm choice (ForceAtlas2, Fruchterman-Reingold, Kamada-Kawai, hive plots), edge bundling, community-based coloring, and reproducible seeds using NetworkX, PyVis, igraph, and Cytoscape automation. Use when rendering biological networks for static publication, interactive HTML exploration, or Cytoscape-format export.

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。

Detect introgression and admixture between species or populations using Dsuite (Malinsky 2021 fast D-statistics), Patterson's D / ABBA-BABA test (Green 2010; Durand 2011), f4-ratio and f-branch statistic (Malinsky 2018), TreeMix (Pickrell & Pritchard 2012), HyDe (Blischak 2018), QuIBL (Edelman 2019), sprime (Browning 2018), Twisst (Martin 2017), PhyloNet (Solis-Lemus 2017) for explicit phylogenetic networks, and qpAdm / qpGraph (Patterson 2012; Lipson 2013). Distinguish introgression from incomplete lineage sorting (ILS), ancestral structure, ghost-lineage admixture, and rate variation. Use when testing inter-species gene flow, dating admixture events, identifying introgressed segments, building phylogenetic networks for reticulate evolution, or applying the ABBAclustering (Koppetsch-Malinsky-Matschiner 2024) framework for divergent-species gene flow.

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。

Manage Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile optimization.

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。