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手法統計学1,836 人工知能・機械学習1,661 意思決定科学932 研究方法1,354 測定1,745 因果とエビデンス532 研究実践118

112 件の手法 Life Sciencesで · 人工知能・機械学習クリア

2つのフィルターが交差する位置にある手法。

並べ替え人気順 A–Z Z–A 新着順

Network-based copy number variation analysis

Network-based copy number variation analysis integrates genome-wide CNV data with biological interaction networks — such as protein-protein interaction (PPI) or pathway networks — to identify functionally coherent regions, driver genes, and altered subnetworks that raw CNV calling alone would miss. By propagating CNV s

2件の出典2011

Network-based epigenome-wide association study

Network-based EWAS extends conventional epigenome-wide association studies by overlaying differentially methylated positions or regions onto biological interaction networks — such as protein-protein interaction, co-expression, or gene regulatory networks — to identify functionally coherent epigenetic modules rather tha

2件の出典2010

Network-based eQTL analysis

Network-based eQTL analysis extends classical eQTL mapping by embedding genetic variant-to-expression associations within gene regulatory or protein interaction networks. Rather than treating each SNP-gene pair independently, this approach leverages network topology — such as co-expression modules or known pathway stru

2件の出典2008

Network-based GWAS

Network-based GWAS integrates conventional genome-wide association study results with biological network data — such as protein-protein interaction (PPI) networks or gene co-expression graphs — to identify disease-relevant gene modules or subnetworks. Instead of reporting only the top individual SNPs, this approach pro

2件の出典2011

Network-based metabolomics analysis

Network-based metabolomics analysis integrates quantitative metabolite profiling data with biological network structures — metabolic pathways, protein-metabolite interaction graphs, and disease networks — to reveal coordinated biochemical disruptions that individual metabolite lists would miss. Rather than treating eac

2件の出典2005

Network-based microbiome diversity analysis

Network-based microbiome diversity analysis integrates graph-theoretic co-occurrence network inference with classical alpha- and beta-diversity metrics to characterize the structural organization of microbial communities. Rather than treating taxa as independent entities, the method models pairwise microbial associatio

2件の出典2012

Network-based pathway enrichment analysis

Network-based pathway enrichment analysis integrates molecular interaction networks — protein-protein interactions, signalling graphs, or gene regulatory networks — with omics measurements to identify biological pathways that are coordinately altered in a condition. Unlike classical over-representation or gene-set enri

2件の出典2002

Network-based Phylogenetic Analysis

Network-based phylogenetic analysis constructs graph-structured representations of evolutionary relationships that explicitly accommodate reticulate events — including hybridization, horizontal gene transfer, recombination, and incomplete lineage sorting — which strictly bifurcating phylogenetic trees cannot represent.

2件の出典1992

Network-based RNA-seq differential expression

Network-based RNA-seq differential expression analysis integrates conventional differential expression testing with gene interaction networks — such as protein-protein interaction graphs or weighted co-expression networks — to identify not just individual differentially expressed genes but coherent, biologically meanin

2件の出典2002

Network-based single-cell RNA-seq analysis

Network-based single-cell RNA-seq analysis extends standard scRNA-seq workflows by constructing and interrogating molecular interaction networks — gene regulatory networks, co-expression networks, or cell-cell communication graphs — from single-cell transcriptomic data. Rather than treating each gene independently, thi

2件の出典2015

Network-based variant calling

Network-based (graph-genome) variant calling replaces the conventional single linear reference genome with a variation graph — a network in which nodes represent sequence segments and edges represent known alternative paths through the genome. Reads are mapped onto this graph, enabling detection of SNPs, indels, and st

2件の出典2017

Pathway Enrichment Analysis

Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By

2件の出典2003

Pharmacophore Modeling

Pharmacophore modeling identifies the spatial arrangement of molecular features (hydrogen bond donors, acceptors, aromatic rings) that are essential for biological activity. Introduced by Gund in 1977, this ligand-based method creates a three-dimensional pattern that can screen chemical libraries and design new active

3件の出典1977

Phylogenetic Analysis

Phylogenetic analysis reconstructs the evolutionary history of organisms, genes, or proteins by comparing molecular sequence data and estimating the branching tree that best explains observed similarities and differences. Rooted in the work of Felsenstein and colleagues from the 1960s onward, it is a cornerstone techni

2件の出典1960

Phylogenetic Independent Contrasts

Phylogenetic Independent Contrasts (PIC) is a comparative statistical method that tests for associations between traits across species while accounting for shared evolutionary history. Developed by Joseph Felsenstein in 1985, PIC solves a fundamental problem in comparative biology: related species share traits due to c

3件の出典1985

Polygenic Risk Score

A polygenic risk score (PRS) is a summary measure that aggregates the effects of many genetic variants across the genome to predict an individual's genetic predisposition to disease or other complex traits. Developed initially by Purcell and colleagues in 2007, PRS methods combine genome-wide association study (GWAS) r

3件の出典2007

PPI Network Topology

Protein-protein interaction network analysis identifies and characterizes the structural properties of cellular interaction networks. Pioneered by Uetz and colleagues through large-scale yeast two-hybrid screening, this approach reveals topological features like hubs, modules, and motifs that encode functional organiza

3件の出典2000

Proteomics Analysis

Proteomics analysis is a systematic pipeline for identifying and quantifying proteins in biological samples using mass spectrometry. Starting from raw spectral data, the workflow searches protein sequence databases, estimates abundance across conditions, applies statistical tests for differential expression, and maps f

2件の出典1994

QSAR

Quantitative Structure-Activity Relationship (QSAR) modeling predicts biological activity from molecular structure using statistical or machine learning models. Pioneered by Hansch in 1964, QSAR correlates numerical molecular descriptors with measured bioactivity, enabling prediction of activity for untested compounds

3件の出典1964

QTL Mapping

Quantitative trait loci (QTL) mapping is a genetic method that localizes chromosomal regions influencing quantitative traits—continuous phenotypes controlled by multiple genes and environmental factors. Developed by Lander and Botstein in 1989, QTL mapping uses linkage analysis and trait variation in segregating popula

3件の出典1989

RNA Velocity

RNA velocity is a computational method that infers the future developmental state of individual cells from single-cell RNA-sequencing data. Developed by La Manno and colleagues in 2018, RNA velocity analysis measures the direction and pace of cell state transitions by analyzing the ratio of unspliced to spliced mRNA tr

3件の出典2018

RNA-seq Differential Expression

RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normal

2件の出典2008

Selection Sweep (Tajima's D)

Tajima's D is a statistical test designed to detect selective sweeps—recent, rapid fixation of advantageous mutations—from patterns of genetic variation in DNA sequences. Developed by Fumio Tajima in 1989, this test measures deviations from neutrality by comparing different measures of DNA sequence diversity. A signifi

3件の出典1989

Sequence Alignment

Sequence alignment is a foundational bioinformatics technique that arranges two or more DNA, RNA, or protein sequences to reveal regions of similarity, infer evolutionary relationships, identify functional domains, and map sequencing reads to reference genomes. It underpins virtually every downstream genomic analysis,

2件の出典1970

Single-cell ChIP-seq peak calling

Single-cell ChIP-seq peak calling is a bioinformatics pipeline that identifies genomic regions enriched for histone modifications or transcription factor binding in individual cells. By profiling chromatin states at single-cell resolution, it reveals epigenomic heterogeneity hidden in bulk ChIP-seq experiments, enablin

2件の出典2019

Single-cell Copy Number Variation Analysis

Single-cell copy number variation (scCNV) analysis detects gains and losses of genomic segments within individual cells, enabling researchers to resolve intratumor heterogeneity, reconstruct clonal evolution, and distinguish malignant from normal cells at single-cell resolution. It can be applied to single-cell whole-g

2件の出典2011

Single-cell epigenome-wide association study

A single-cell epigenome-wide association study (scEWAS) interrogates epigenetic marks — primarily DNA methylation or chromatin accessibility — across the entire genome at single-cell resolution, then statistically associates variation in those marks with a phenotype, disease, or exposure. By resolving cell-type heterog

2件の出典2015

Single-cell eQTL analysis

Single-cell eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression in a cell-type-specific manner by jointly analysing single-cell RNA-seq profiles and donor genotype data. Unlike bulk eQTL methods, it resolves regulatory effects that are diluted or masked when cell types are mixed, enabling di

2件の出典2020

Single-cell Gene Set Enrichment Analysis

Single-cell gene set enrichment analysis (scGSEA) extends classical bulk GSEA to the resolution of individual cells. Rather than testing whether a gene set is enriched in a sample-level comparison, scGSEA assigns an enrichment or activity score to each cell, enabling researchers to map pathway activity across heterogen

2件の出典2017

Single-cell GWAS

Single-cell GWAS is an integrative bioinformatics pipeline that maps genome-wide association study (GWAS) signals onto single-cell transcriptomic landscapes to identify which cell types and individual cells carry disproportionate genetic risk for a disease or trait. By leveraging single-cell RNA-seq atlases alongside G

2件の出典2019

Single-cell metabolomics analysis

Single-cell metabolomics analysis measures the small-molecule metabolite content of individual cells, revealing cell-to-cell metabolic heterogeneity that bulk methods obscure by averaging. Rooted in mass spectrometry and microfluidics advances, it enables researchers to map metabolic states across cell populations, ide

2件の出典2013

Single-cell Microbiome Diversity Analysis

Single-cell microbiome diversity analysis resolves the composition and functional heterogeneity of microbial communities at the level of individual cells or bacteria. By combining single-cell or single-bacterium isolation with high-throughput sequencing, this pipeline overcomes the averaging effect of bulk metagenomics

2件の出典2019

Single-cell Phylogenetic Analysis

Single-cell phylogenetic analysis reconstructs evolutionary or developmental trees from single-cell sequencing data, tracing how individual cells diverged from a common ancestor. By leveraging somatic mutations, CRISPR-introduced barcodes, or copy-number changes as heritable characters, this method maps clonal relation

2件の出典2014

Single-cell RNA-seq analysis

Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds th

2件の出典2009

Single-cell RNA-seq differential expression

Single-cell RNA-seq differential expression (scRNA-seq DE) analysis identifies genes whose expression levels differ significantly between defined groups of individual cells — such as cell types, disease states, or treatment conditions. Unlike bulk RNA-seq, which averages signals across millions of cells, scRNA-seq DE o

2件の出典2013

Single-cell sequence alignment

Single-cell sequence alignment is the computational step that maps millions of short sequencing reads produced by single-cell RNA-seq experiments back to a reference genome or transcriptome. Unlike bulk RNA-seq alignment, each read carries a cell barcode and a Unique Molecular Identifier (UMI) that together identify th

2件の出典2013

Single-cell variant calling

Single-cell variant calling is a bioinformatics pipeline that identifies DNA sequence variants — single-nucleotide variants (SNVs), small insertions and deletions, and copy-number alterations — within individual cells rather than across a bulk tissue mixture. By resolving the mutational landscape cell by cell, it revea

2件の出典2016

Time-series ChIP-seq peak calling

Time-series ChIP-seq peak calling extends standard chromatin immunoprecipitation sequencing analysis to samples collected at multiple time points. By identifying and comparing protein-DNA binding peaks across a temporal dimension, the method reveals how transcription factor occupancy, histone modifications, or chromati

2件の出典2008

Time-series copy number variation analysis

Time-series copy number variation (CNV) analysis is a computational genomics pipeline that characterizes chromosomal gains and losses across multiple sequential samples from the same individual or tumor. By comparing copy number profiles at successive time points — such as diagnosis, mid-treatment, relapse — it reconst

2件の出典2010

Time-series Epigenome-wide Association Study

A time-series epigenome-wide association study (time-series EWAS) extends the classic cross-sectional EWAS design to longitudinal settings, measuring DNA methylation across the entire epigenome at multiple time points within the same subjects. The goal is to identify CpG sites whose methylation levels change systematic

2件の出典2010

Time-series eQTL analysis

Time-series eQTL analysis identifies genetic variants (eQTLs) whose effect on gene expression changes over time or across developmental stages. By combining longitudinal RNA-seq data with individual genotypes, the method captures how the same SNP can activate, silence, or reshape gene regulation at different time point

2件の出典2010

Time-series gene set enrichment analysis

Time-series gene set enrichment analysis (TS-GSEA) extends the classical GSEA framework to detect biologically coordinated gene sets — pathways, gene ontology terms, or curated signatures — whose collective expression changes meaningfully over time. Rather than comparing two snapshots, it models the full temporal traje

2件の出典2005

Time-series metabolomics analysis

Time-series metabolomics analysis profiles small-molecule metabolites from biological samples collected at multiple, ordered time points, enabling researchers to capture the dynamic flux of metabolic pathways in response to stimuli, disease progression, drug treatment, or developmental change. By integrating longitudin

2件の出典2000

Time-series microbiome diversity analysis

Time-series microbiome diversity analysis tracks how the richness, evenness, and community composition of microbial communities change across multiple time points within the same subjects. By combining standard diversity metrics with longitudinal statistical models, it separates true temporal dynamics from inter-indivi

2件の出典2010

Time-series pathway enrichment analysis

Time-series pathway enrichment analysis identifies biological pathways whose coordinated gene activity changes significantly across ordered time points. Rather than treating each time point independently, the method models the temporal trajectory of gene expression within each pathway and tests whether entire biologica

2件の出典2005

Time-series phylogenetic analysis

Time-series phylogenetic analysis reconstructs the evolutionary history of organisms or genetic variants using sequences sampled at known time points. By incorporating sampling dates directly into the model, it estimates divergence times, substitution rates, and ancestral relationships on an absolute timescale — making

2件の出典2000

Time-series proteomics analysis

Time-series proteomics analysis quantifies protein abundance across two or more ordered time points to reveal how the proteome changes dynamically in response to stimuli, developmental stages, or disease progression. By combining mass spectrometry-based protein quantification with statistical models designed for tempor

2件の出典2000

Time-series RNA-seq differential expression

Time-series RNA-seq differential expression analysis identifies genes whose expression levels change systematically across ordered time points — such as during development, disease progression, or response to a treatment. Unlike two-condition DE analysis, it explicitly models the temporal structure of the data, capturi

2件の出典2006

Time-series single-cell RNA-seq analysis

Time-series single-cell RNA-seq analysis captures gene expression across multiple time points at single-cell resolution to reveal how cell populations emerge, transition, and diverge during dynamic biological processes such as development, differentiation, or disease progression. By combining pseudotime ordering, RNA v

2件の出典2014

Time-series variant calling

Time-series variant calling is a bioinformatics pipeline that identifies and tracks genomic variants — typically somatic mutations — across multiple sequencing samples collected from the same subject at different time points. It is most widely applied in cancer genomics to reconstruct tumour evolution, monitor minimal

2件の出典2009

Transmission Disequilibrium Test

The Transmission Disequilibrium Test (TDT) is a family-based statistical method for testing genetic association with disease or traits while inherently controlling for population stratification. Developed by Spielman and Ewens in 1993, the TDT examines whether an allele is preferentially transmitted from heterozygous p

3件の出典1993

Variant Calling

Variant calling is the computational process of identifying positions in a sequenced genome that differ from a reference sequence — including single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), and structural variants. It transforms aligned sequencing reads into an interpretable catalogue o

2件の出典2009

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