“meta”的搜索结果

METAINFORMANT rules for directory src/metainformant/metabolomics/pathways. 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/metabolomics/visualization. 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/metagenomics/shotgun. 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/metabolomics/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.

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METAINFORMANT rules for directory src/metainformant/metagenomics/visualization. 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/metabolomics/io. 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/metagenomics/diversity. 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/metagenomics. 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/metagenomics/comparative. 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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Maps metabolomics results to biological pathways via over-representation (ORA), metabolite-set enrichment (MSEA/QEA), mummichog/PSEA on raw m/z peaks, and network-diffusion enrichment (FELLA), with correct background-set construction and honest interpretive ceilings. Use when interpreting differential metabolites or an untargeted LC-MS feature table in pathway context, choosing ORA vs MSEA vs mummichog vs topology, or setting the reference/background set. For annotation confidence levels feeding ORA see metabolomics/metabolite-annotation; for gene-set concepts see pathway-analysis/go-enrichment and pathway-analysis/gsea; for joint gene+metabolite pathways see multi-omics-integration/mofa-integration.

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Programmatic untargeted LC-MS feature extraction in R with the modern xcms 4.x MsExperiment/XcmsExperiment API, taking raw mzML to a feature table via CentWave peak detection, retention-time alignment, peak-density correspondence, gap-filling, CAMERA redundancy collapse, and built-in QC feature filtering. Use when converting centroided LC-MS runs into a features-by-samples matrix and deciding centWave/grouping/alignment parameters. For drift correction and QC/CV filtering execution see metabolomics/normalization-qc; for metabolite identification see metabolomics/metabolite-annotation; for the MS-DIAL GUI alternative with MS2Dec deconvolution see metabolomics/msdial-preprocessing; for downstream statistics see metabolomics/statistical-analysis.

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Turns untargeted LC-MS/MS features (m/z, RT, MS/MS) into confidence-stratified metabolite annotations using spectral-library matching (matchms), in-silico tools (SIRIUS/CSI:FingerID, MetFrag) and molecular networking, and assigns a defensible MSI/Schymanski confidence level to each. Use when naming detected features, scoring MS/MS against a reference library, running SIRIUS, or deciding what confidence level an evidence set actually supports. For upstream feature extraction see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for downstream enrichment that must respect these levels see metabolomics/pathway-mapping; for lipid-specific structural annotation see metabolomics/lipidomics.

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Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool profiling. Covers tracer choice, isotopologue vs isotopomer, mass-isotopomer distributions (MID), fractional enrichment, the mandatory natural-abundance + tracer-purity correction (IsoCor, AccuCor), and the metabolic/isotopic steady-state vs non-stationary (INST-MFA) distinction. Use when feeding a labeled tracer and interpreting labeling patterns, correcting raw isotopologue intensities, computing or plotting an MID, or deciding tracing vs abundance profiling. For absolute pool concentration and MRM mechanics see metabolomics/targeted-analysis; for constraint-based genome-scale flux (FBA, not empirical tracing) see systems-biology/flux-balance-analysis; for feature detection see metabolomics/xcms-preprocessing; for pathway enrichment that ignores the pool-vs-flux caveat see metabolomics/pathway-mapping.

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Assigns honest lipid annotation levels, designs class-based internal-standard quantification, and runs lipid-aware differential and enrichment analysis with lipidr, guarding against in-source-fragment phantoms, sn-position over-claims, and invalid cross-class quantification. Use when naming or canonicalizing lipid species (shorthand separators, Goslin), deciding shotgun vs RP vs HILIC LC-MS, picking internal standards (SPLASH/EquiSPLASH), interpreting MS-DIAL/LipidSearch output, or comparing lipid classes. For general feature detection see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for non-lipid annotation confidence see metabolomics/metabolite-annotation; for normalization/QC see metabolomics/normalization-qc; for multivariate stats see metabolomics/statistical-analysis.

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Assess a repository's readiness for an AI agent execution framework across five dimensions. Produce JSON or a table with readiness scores, estimated cost per run, and scaffold status, without changing repository files.

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• Assessing readiness before adopting an agent execution framework
• Comparing readiness scores across five dimensions
• Checking estimated run costs and scaffold readiness

Inspect page HTML for metadata tags placed outside the head section, where page settings belong. Identify misplaced tags and guide fixes to templates or scripts that insert them into the body.

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• Checking metadata placement after CMS plugin changes
• Finding metadata inserted into the body by JavaScript
• Reviewing robots, description, OG, and canonical placement

Develop a research question, search and verify literature, and combine findings into an academic report. Modes also support guided research discussions, claim checking, paper comparisons, and systematic reviews with optional meta-analysis, which statistically combines study results.

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• Clarify a research direction through guided questions
• Search literature and compare findings across sources
• Check specific claims against supporting sources

Record and inspect source history, trust scores, and handling labels on AI-generated or modified files. Use AKF metadata, information attached to files, to support review, handoff, and advisory audit checks without treating labels as proof of compliance.

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• Recording how an AI-generated file was created
• Inspecting existing metadata before editing a file
• Labeling files as public, internal, or confidential

Create paid ad headlines, descriptions, and primary text for platforms including Google Ads, Meta, LinkedIn, and TikTok. Organize variations by message angle, check character limits, and use performance data to guide the next round.

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• Create headlines and descriptions for Google Ads
• Write social ad copy from several message angles
• Develop new variations from ad performance data

Plan paid advertising across Google Ads, Meta, LinkedIn, and other platforms. Build campaign structures, ad copy, audience plans, and budgets, then use performance metrics and account checks to identify practical improvements.

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• Plan a first paid advertising campaign
• Create headline, body copy, and call-to-action variants
• Design retargeting audiences and exclusion lists

Review daily Google Ads and Meta Ads results for budget pacing, declining performance, and unusual activity. Get prioritized findings with supporting numbers and concrete recommendations for a marketing team to assess and carry out.

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• Checking whether campaign budgets are being spent on schedule
• Investigating rising acquisition costs or falling ad returns
• Finding ads and keywords that spend without converting

Guides adding metadata, extra information attached to data elements, to Apache Beam's WindowedValue. Maps the changes needed across serialization, storage, processing, and runner interfaces so new fields remain available during pipeline execution.

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• Adding trace context or drain flags to WindowedValue
• Checking metadata preservation through Windmill storage
• Reviewing propagation through grouping and timers

Add and tune animated liquid-metal WebGL borders with the React `metal-fx` package. Use when buttons, icon controls, chips, tabs, cards, or selected surfaces need a metallic active, selected, hover, focus, or premium border; when implementing the MetalFx component from metal.jakubantalik.com; or when troubleshooting its presets, themes, strength, glow, reflections, sizing, radius, animation, accessibility, SSR, or performance.

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When the user wants a full-page metadata audit or implementation across discovery, search intent, title, description, canonical, robots, hreflang, Open Graph, Twitter Cards, code or CMS changes, build validation, rendered HTML, production verification, localization, or post-release observation. Also use for "metadata audit," "sitewide meta optimization," "metadata implementation," "meta robots," "hreflang," "viewport," or "charset." For an isolated title, description, heading, Open Graph, or Twitter Card task, use its specialist skill directly.

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