「poc」の検索結果

Generates Mermaid flowchart code and visual diagrams for pathophysiological.

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。

Screens research papers based on title/abstract and inclusion criteria, providing a structured Yes/No/Maybe decision. Use when you need to filter literature for meta-analysis or systematic reviews.

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。

Professional beautification tool for gene expression heatmaps, automatically adds clustering trees, color annotation tracks, and intelligently optimizes label layout.

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。

Generate Baujat plots for heterogeneity analysis. Identify studies that contribute most to the overall meta-analysis results and heterogeneity, helping discover potential outlier studies. Input meta-analysis data CSV, output Baujat plot PNG and contribution data CSV.

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。

Generate meta-analysis forest plots for binary classification data. Input is a CSV file containing study names, event counts and sample sizes for experimental and control groups. Output includes forest plot PNG and data table CSV.

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。

Generate forest plots for meta-analysis of continuous data. Input a CSV file containing study names, means, standard deviations, and sample sizes for experimental and control groups. Output forest plot PNG and data table CSV.

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。

Generate forest plots for meta-analysis of survival data. Input is a CSV file containing study names, HR and 95% confidence intervals, output forest plot PNG and data table CSV. Supports both R and Python scripts.

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。

Generates PI(E)COS structure (Population, Intervention, Comparator, Outcomes, Study Design) from Meta-analysis or study titles. Use when the user wants to extract these elements from a title.

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。

Digital pathology image processing toolkit for whole slide images (WSI). Supports WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.

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。

Generate radial plots (Radial Plot/Galbraith Plot) for heterogeneity analysis. Visually assess heterogeneity across studies by displaying the relationship between standardized effect sizes and precision. Input: Meta-analysis data in CSV format; Output: Radial plot PNG and data CSV.

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。

Generate leave-one-out sensitivity analysis plots for meta-analysis. Input is a CSV file containing meta-analysis data; outputs are a sensitivity forest plot (PNG) and a sensitivity data table (CSV) showing pooled effect estimates after excluding each study in turn.

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。

Generate Meta-analysis funnel plots and perform publication bias testing. Takes CSV file with Meta-analysis data as input, outputs funnel plot PNG, Egger test and Begg test results.

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。

Unified Python access to 40+ bioinformatics web services; use when you need to query multiple databases (e.g., UniProt/KEGG/ChEMBL/Reactome) with one consistent API in a single workflow, especially for cross-database analysis and identifier mapping.

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。

Perform quantitative meta-analysis with effect size calculation, forest plots, funnel plots, and heterogeneity assessment. Use when: user asks to combine results from multiple studies, calculate pooled effect sizes, assess publication bias, or create forest/funnel plots. NOT f...

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。

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generate...

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。

Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports.

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。

Performs epidemiological analyses including disease modeling (SIR/SEIR), outbreak investigation, risk factor identification, incidence/prevalence estimation, and causal inference from observational data; trigger when users discuss disease spread, public health data, or populat...

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 is applicable when using LaminDB. LaminDB is an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR-compliant. It is suitable for managing biological datasets (scRNA-seq, spatial transcriptomics, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakes, or ensuring data lineage and reproducibility in biological research. It covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integration with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

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。

Process CRISPR screening data to identify essential genes and hit candidates. Performs quality control, statistical analysis (RRA), and hit calling for pooled CRISPR screens including viability screens and drug resistance/sensitivity studies.

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。

Generate Circos configuration files for circular genomics data visualization. Supports genomic variations (SNPs, CNVs, structural variants), cell-cell communication networks, and custom track configurations for publication-ready circular plots.

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 cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data fo...

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。

Constraint-based reconstruction and analysis (COBRA) for metabolic models; use when you need to simulate growth/production, analyze flux ranges, or run knockout and medium studies from SBML/JSON/YAML models.

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。

Analyze data with `forest-plot-styler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

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。

DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, process FASTQ/BAM/VCF, for developing and executing genomics pipelines.

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。