'preprocessing' 검색 결과

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Ergogen (エルゴノミック自作キーボード設計ツール) リファレンス。 YAML / JSON 設定で points, outlines, cases, pcbs を宣言し SVG / DXF / JSCAD / KiCad を出力。 Anchors, Zones, Adjustments, Preprocessing, Footprints, Traces。 キーレイアウト、ケース設計、PCB 生成。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reduce data with many features into fewer dimensions using UMAP. Create 2D or 3D views, prepare inputs for clustering, compare parameter settings, and project new samples while checking whether the results remain stable and meaningful.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Visualizing data with many features in 2D or 3D
• Preparing reduced features for HDBSCAN clustering
• Guiding dimensionality reduction with partial labels

Analyze large scientific tables with Vaex, including datasets that exceed available memory. Build calculated columns, filter rows, compute statistics, visualize aggregated data, and convert files while identifying operations that may still require substantial RAM.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Computing statistics on tables larger than available memory
• Creating calculated features without storing full columns
• Visualizing large datasets with aggregated grids

Fit probabilistic models to single-cell measurements for integration across experiments, cell annotation, combined RNA and protein or ATAC analysis, and gene expression comparisons. The skill covers input requirements, training checks, reference mapping, and interpretation of model uncertainty.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Integrating single-cell RNA data across experimental batches
• Assigning cell labels using partial annotations or a reference
• Analyzing RNA with protein or ATAC measurements

Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Classify time series and predict continuous outcomes
• Group time series with similar patterns
• Forecast future values

Manage Azure Machine Learning resources from Python with SDK v2. The skill provides implementation examples for registering data and models, configuring compute and environments, submitting training jobs, monitoring logs, and connecting steps into pipelines.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Submitting training jobs and monitoring logs
• Registering data and model versions
• Connecting preprocessing and training steps

Choose embedding models, which represent text as numerical vectors, for meaning-based search and RAG applications. Compare options for code, prose, and multilingual content, and plan document splitting, preprocessing, caching, and dimension reduction to improve retrieval quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing an embedding model for RAG
• Reviewing document chunk size and boundaries
• Planning code or specialist document search

Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare credit data before building a model
• Compare missing values and bad-sample rates across organizations
• Screen variables for usefulness, stability, and redundancy

Provide other skills with tools for generating and editing images, analyzing image content, and improving text through external APIs. This foundation passes inputs to backend services without preprocessing and returns results as text or JSON.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding image generation to another skill
• Editing reference images with written instructions
• Analyzing the contents of multiple images

Diagnose slow MSBuild startup by measuring project evaluation, the work done before compilation. Inspect logs for costly file searches, imported configuration, and repeated evaluations, then identify targeted changes supported by timing evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating delays before compilation starts
• Checking file searches across large directories
• Analyzing deep import chains

Inspect scientific data files to understand their format, structure, and quality. Covering over 200 formats, this skill produces Markdown reports with statistical summaries, potential issues, and recommendations for preprocessing, visualization, and further analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting an unfamiliar scientific data file
• Checking missing values and duplicates in experimental tables
• Reviewing sequence lengths and quality scores in FASTQ files

Explore scientific data files to understand their structure, statistical patterns, and quality. References cover over 200 formats, with workflows for producing Markdown reports that summarize findings and recommend preprocessing, visualizations, and further analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking missing values and outliers in experimental tables
• Examining sequence lengths and quality scores in FASTQ files
• Inspecting microscopy image dimensions and metadata

Split datasets into training, validation, and test partitions with the right stratification and temporal rules. Use as a narrow preprocessing helper once the broader ML workflow is already chosen, not as the main route owner for an end-to-end ML task.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Preprocess ribosome profiling data including adapter trimming, size selection, rRNA removal, and alignment. Use when preparing Ribo-seq reads for downstream analysis of translation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Preprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Load and preprocess imaging mass cytometry (IMC) and MIBI data. Covers MCD/TIFF handling, hot pixel removal, and image normalization. Use when starting IMC analysis from raw MCD files or preparing images for segmentation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation. Use when extracting single-cell data from IMC or MIBI images after preprocessing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.