562 件 ・ 関連度順
概要と使いどころ
Use when you need a standardized R CLI workflow to run two-class SVM-RFE feature ranking on an expression-like matrix, choose an informative feature count from cross-validated error, and generate reproducible ranking and error plots. NOT for regression, multi-class classification, missing-value imputation, or remote data fetching.
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Use when building XGBoost models on tabular data and returning feature importance ranking outputs. Supports binary classification and regression with automatic task detection, train-test split, performance tables, feature importance ranking tables, and PNG importance plots.
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Use when building a decision tree model in R and generating feature importance ranking outputs. Supports classification and regression, automatic task detection, parameter validation, model evaluation summaries, and exports of feature-importance tables and figures.
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Classify environmental and meteorological variables into driver categories for attribution analysis. Use when you need to group multiple variables into meaningful factor categories.
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。
SetFit few-shot learning for efficient intent classification with minimal data
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。
LLM-based zero-shot and few-shot classification for flexible intent detection
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。
Hugging Face transformer model fine-tuning and inference for intent classification
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。
Security vulnerability assessment identifying OWASP risks, injection vectors, authentication issues, and data exposure with severity classification.
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。
Urgent issue classification, root cause analysis, and fast-path routing for production hotfixes
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。
Capture raw thoughts with automatic domain classification and vault routing
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。
Use before routing a /meta-agent request to choose single-agent-creator, team-builder, or agentlas-packager from the user's wording and available files.
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On-demand RAI planner reference pack covering Phase 1 capture, Phase 2 risk classification and the optional Mural board bootstrap, Phase 5 impact assessment, and Phase 6 review and backlog handoff.
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Write an API versioning strategy document for a service or API platform. Use when asked to define versioning policy, plan API deprecation, classify breaking changes, or document version lifecycle. Produces a complete versioning strategy with breaking-change classification table, deprecation timeline, migration guide template, and client communication template.
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。
Quick capture of raw thoughts with intelligent domain classification and competitive intelligence extraction
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。
Use when running the final pre-submission preflight for the American Economic Journal: Microeconomics (AEJ: Micro) via the AEA submission system — single-blind review, the 100-word abstract, JEL classification, the submission fee, format, and the AEA Data and Code Availability Policy. Final checks; it does not draft content.
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AI-agent-powered lead enrichment using Sixtyfour as primary source. Takes an email (+ optional name) and returns comprehensive person + company data with funding, AI/B2B classification, and full error visibility. Higher cost (~$0.20/lead) but simpler architecture.
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End-to-end Google Search Ads campaign builder. Performs deep keyword research (competitor SEO, review language mining, Reddit/HN community terminology, site audit), builds keyword architecture with funnel mapping and intent classification, creates ad group structure, generates headline/description variants, builds negative keyword lists, recommends bid strategy, and exports a campaign-ready CSV for Google Ads Editor import.
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Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.
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Turns Hi-C/Micro-C FASTQ into a deduplicated, filtered .pairs file with pairtools and decides whether the library worked. Covers the bwa mem -SP5M / bwa-mem2 / chromap --preset hic alignment idiom (mates mapped as independent single-end reads), pairtools parse vs parse2 and the walks-policy choice (5unique pairwise vs all for Pore-C/Micro-C concatemers), pair-type classification (keep UU and rescued UC), dedup (PCR vs optical/by-tile), select by pair_type/MAPQ/distance, restriction-fragment handling (restrict, Arima dual-enzyme, Micro-C/DNase fragment-free), and allele-specific phasing (pairtools phase to two coolers). The library-QC decision uses % long-range cis as the one-number quality metric, trans as the noise floor, orientation balance as fragment-map-free dangling-end/self-circle QC, and % duplicates as a complexity proxy. Use when processing Hi-C/Micro-C/Omni-C reads into pairs, judging library quality, handling multi-enzyme or restriction-agnostic protocols, or generating allele-specific contacts.
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Discovers, classifies, and masks repetitive elements and transposable elements with RepeatModeler2 (de novo family library), RepeatMasker (masking against a library), EDTA (plant/structural TEs), or EarlGrey (auto-curating wrapper), and quantifies TE expression from RNA-seq with TEtranscripts/SQuIRE. Covers de-novo-library-as-curation-project, soft-vs-hard masking, the domesticated-gene over-masking massacre, Dfam-vs-RepBase, TE classification (Class I/II, family-vs-copy), Kimura repeat landscapes, LAI, and the RNA-seq multimapping problem. Use when masking repeats before gene prediction, building a TE library for a non-model genome, or analyzing transposable-element content or expression.
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Plot per-gene mutation distributions on a protein-domain map (lollipop / needle plots) showing mutation position, recurrence count, and variant classification with maftools, g3-lollipop, trackViewer, and ProteinPaint. Use when visualizing recurrent mutation hotspots on a single gene's protein, marking domain boundaries from UniProt/Pfam, comparing missense vs truncating distributions, or contrasting two cohorts on the same lollipop.
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Classify constitutional (germline) copy number variants for clinical reporting using the 2019 ACMG/ClinGen technical standards points-based framework, with ClassifyCNV and AnnotSV for semi-automated scoring. Covers the separate copy-number-loss and copy-number-gain rubrics, the five-tier classification, ClinGen haploinsufficiency/triplosensitivity and dosage-sensitive regions, de novo and segregation evidence, and population-frequency benign evidence. Use when assigning pathogenic/likely-pathogenic/VUS/likely-benign/benign to a constitutional CNV, scoring a CNV against ACMG/ClinGen criteria, or distinguishing the automatable evidence from the case-specific evidence requiring manual input.
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Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.
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Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.
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。