'preprocessing' 검색 결과

Workflow for small RNA and miRNA preprocessing, quantification, differential analysis, and target-oriented interpretation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Workflow for ribosome profiling, P-site aware preprocessing, periodicity checks, ORF detection, and translation efficiency analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Workflow for spatial transcriptomics preprocessing, domain detection, deconvolution, neighborhood analysis, and publication-ready maps.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Workflow for untargeted or targeted metabolomics including preprocessing, normalization, annotation, statistics, and pathway mapping.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Builds end-to-end predictive modeling pipelines on tabular datasets—data exploration, preprocessing, baseline modeling, and mandatory optimization—producing valid saved predictions in the required output format. Use for any supervised machine learning or data modeling task, including classification and regression on tabular data, Kaggle-style competitions or submission-file challenges, training a model to predict a target from features, or requests to fit/tune/evaluate models with scikit-learn, XGBoost, Random Forest, Logistic Regression, and similar non-deep-learning methods. Runs fully automated end-to-end without asking the user for intermediate decisions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when building or changing features, preprocessing, encoders, resampling or train/validation/test splits — keep every fitted transform inside a Pipeline fitted on training folds only, and build time and group features without seeing the future or the same entity twice

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Advises on setting up and validating topic models for survey and experimental text: discovery vs. measurement, STM/LDA/BERTopic choice, preprocessing, covariate formulas, choosing K across coherence, FREX, held-out likelihood, and residuals, validating topics against human coding, robustness, and reporting. Coding against a fixed codebook goes to text-classification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fMRI data analysis — preprocessing, GLM modeling, and group-level inference for task and resting-state studies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

EEG preprocessing and analysis — filtering, artifact removal, time-frequency decomposition, and ERP workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze text data with NLP — preprocessing, embeddings, topic modeling, sentiment, classification, and evaluation. Use when turning unstructured text into structured insight.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Manage ML datasets with the datasets ecosystem — loading, streaming, preprocessing, versioning, and sharing. Use when data handling (not modeling) is the bottleneck.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzing behavioral experiment data — RT/accuracy preprocessing, mixed-effects models, and robust inference.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create video compositions, animations, title cards, overlays, captions, voiceovers, audio-reactive visuals, and scene transitions in HyperFrames HTML. Use when asked to build any HTML-based video content, add captions or subtitles synced to audio, generate text-to-speech narration, create audio-reactive animation (beat sync, glow, pulse driven by music), add animated text highlighting (marker sweeps, hand-drawn circles, burst lines, scribble, sketchout), or add transitions between scenes (crossfades, wipes, reveals, shader transitions). Covers composition authoring, timing, media, and the full video production workflow. For dev-loop CLI commands (init, lint, inspect, preview, render) see the hyperframes-cli skill; for asset preprocessing commands (tts, transcribe, remove-background) see the hyperframes-media skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Decompose DAGs into execution chains using width bounds, stratification, virtual nodes, and matching. Use when minimizing workflow lanes in dependency graphs. NOT for cyclic graphs, generic scheduling, or one-off queries with no preprocessing payoff.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies, extracting the event matrix, choosing a reader, or bridging FCS to the scanpy/AnnData ecosystem before preprocessing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Preprocess CLIP-seq reads (eCLIP, iCLIP, iCLIP2, iCLIP3, irCLIP, PAR-CLIP, FLASH) with protocol-specific UMI extraction, adapter trimming, length filtering, and post-alignment PCR-duplicate collapse. Use when raw CLIP FASTQ must be turned into deduplicated, crosslink-preserving BAM input for peak calling; choosing between two-pass and single-pass adapter trimming; deciding minimum read length; or mapping UMI patterns to specific eCLIP/iCLIP/iCLIP2/iCLIP3 library preps.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Decides how to preprocess plasma cfDNA sequencing data so the recoverable signal survives - library-prep-aware fragment expectations (dsDNA vs ssDNA/adaptase prep), UMI/duplex consensus with fgbio (ExtractUmisFromBam, GroupReadsByUmi --strategy paired for duplex, CallMolecularConsensusReads vs CallDuplexConsensusReads, FilterConsensusReads min-reads "total s1 s2"), the align->group->consensus->RE-align ordering, and the cfDNA dedup trap where naive coordinate dedup collapses nucleosome-coincident independent molecules. Covers when single-strand consensus suffices vs when duplex is mandatory, the singleton/sensitivity tax at low input, and reading the insert-size histogram as a pre-analytical QC instrument. Use when processing plasma cfDNA reads before fragmentomics, ctDNA mutation calling, or tumor-fraction estimation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression and target prediction. Use when analyzing miRNA, isomiR, tRF, or piRNA sequencing data through preprocessing, quantification, discovery, DE, and targets.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

End-to-end imaging mass cytometry workflow from raw acquisitions to spatial cell analysis. Orchestrates image preprocessing, segmentation, phenotyping, and spatial statistics. Use when analyzing imaging mass cytometry data end-to-end.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.