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© 2026 ScholarGate · Bibliothèque de référence des méthodes de recherche
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genetics

LD Block Analysis

Linkage disequilibrium (LD) block analysis is a genomic method that partitions the human genome into distinct haplotype blocks—regions of limited recombination where variants are in strong statistical association. First systematically described by Gabriel and colleagues in 2002, this approach reveals the underlying str

3 sources2002
agronomy

Leaf Area Index

Leaf Area Index (LAI) is a dimensionless quantity that measures the total one-sided area of leaves per unit ground area covered by a canopy. It quantifies canopy density and structure: LAI = 0 for bare soil, LAI = 1 for a thin crop, LAI = 3-6 for dense cereal or grass canopies, and LAI > 8 for dense forest. LAI is a ke

3 sources1947
ecology

Leslie Matrix

The Leslie matrix is a deterministic model of age-structured population dynamics, introduced by Patrick Leslie (1945). It projects population size and structure forward in time using age-specific fertility and survival rates. A Leslie matrix encodes these vital rates in a square matrix; multiplying the matrix by a popu

3 sources1945
ecology

Life Table Response Experiment

Life Table Response Experiments (LTRE) decompose observed temporal changes in population growth rate (lambda) into contributions from changes in specific vital rates (survival, reproduction). Developed by Caswell (2000) and applied extensively by Wisdom and colleagues, LTRE reveals which demographic changes drove obser

3 sources2000
bioinformatics

Machine learning-assisted ChIP-seq peak calling

Machine learning-assisted ChIP-seq peak calling extends classical statistical peak detection with supervised or unsupervised learning models that distinguish genuine protein-binding sites from background noise. By training on sequence composition, read coverage profiles, and epigenomic features, these methods improve s

2 sources2008
bioinformatics

Machine learning-assisted copy number variation analysis

Machine learning-assisted CNV analysis applies supervised, unsupervised, or deep learning algorithms to detect genomic regions that are duplicated or deleted relative to a reference genome. Rather than relying on fixed statistical thresholds, ML models learn discriminative patterns from read-depth signals, allele frequ

2 sources2010
bioinformatics

Machine learning-assisted epigenome-wide association study

Machine learning-assisted EWAS integrates conventional epigenome-wide association testing with machine learning models to identify DNA methylation sites associated with a phenotype of interest. By combining the statistical rigour of EWAS with the pattern-recognition power of algorithms such as elastic net, random fores

2 sources2010
bioinformatics

Machine learning-assisted expression quantitative trait loci analysis

Machine learning-assisted eQTL analysis integrates supervised learning models — ranging from elastic-net regression to deep neural networks — into the classical eQTL framework to predict and map genetic variants that regulate gene expression. By training predictive models on reference panels (e.g., GTEx), the approach

2 sources2015
bioinformatics

Machine learning-assisted genome-wide association study

Machine learning-assisted GWAS integrates classical genome-wide association testing with machine learning models to improve the detection of genetic variants associated with complex traits. Where traditional GWAS tests each single nucleotide polymorphism (SNP) independently using linear or logistic regression, ML-GWAS

2 sources2015
bioinformatics

Machine learning-assisted metabolomics analysis

Machine learning-assisted metabolomics analysis is an integrative bioinformatics pipeline that couples untargeted or targeted metabolite profiling — via mass spectrometry or NMR — with supervised and unsupervised ML algorithms to discover biomarkers, classify phenotypes, and model metabolic states. By handling the extr

2 sources2000
bioinformatics

Machine learning-assisted microbiome diversity analysis

Machine learning-assisted microbiome diversity analysis integrates classical alpha and beta diversity metrics with supervised or unsupervised ML models to classify host phenotypes, identify discriminant taxa, and uncover community-level signatures from 16S rRNA or shotgun metagenomic data. It extends traditional divers

2 sources2011
bioinformatics

Machine learning-assisted pathway enrichment analysis

Machine learning-assisted pathway enrichment analysis integrates classical statistical pathway enrichment methods — such as over-representation analysis or gene set enrichment analysis — with machine learning algorithms to improve sensitivity, handle high-dimensional omics data, and uncover non-linear biological patter

2 sources2010
bioinformatics

Machine learning-assisted phylogenetic analysis

Machine learning-assisted phylogenetic analysis integrates supervised, unsupervised, or deep learning models into the evolutionary tree inference workflow to improve speed, accuracy, or scalability beyond what classical maximum-likelihood and Bayesian methods achieve alone. Applications range from substitution model se

2 sources2000
bioinformatics

Machine learning-assisted RNA-seq differential expression

Machine learning-assisted RNA-seq differential expression analysis augments classical statistical DE testing (DESeq2, edgeR, limma-voom) with ML models — including neural networks, random forests, and variational autoencoders — to better handle the high dimensionality, zero-inflation, and batch effects inherent in RNA-

2 sources2015
bioinformatics

Machine learning-assisted sequence alignment

Machine learning-assisted sequence alignment uses statistical learning models — including deep neural networks and protein language models — to compute biologically meaningful alignments between nucleotide or amino acid sequences. By learning substitution patterns and structural constraints from large training corpora,

2 sources2010
bioinformatics

Machine learning-assisted single-cell RNA-seq analysis

Machine learning-assisted single-cell RNA sequencing (scRNA-seq) analysis integrates supervised, unsupervised, and deep generative models into the standard scRNA-seq workflow to handle the unique challenges of single-cell data: extreme sparsity, high dimensionality, technical noise, and batch effects across experiments

2 sources2015
bioinformatics

Machine learning-assisted variant calling

Machine learning-assisted variant calling uses statistical learning models — most notably convolutional neural networks — to distinguish genuine genomic variants (SNPs, indels) from sequencing artifacts in aligned short- or long-read data. Unlike heuristic callers that rely on hand-crafted filters, ML-based approaches

2 sources2018
food science

Maillard Reaction Kinetics

Maillard Reaction Kinetics measures the rate of non-enzymatic browning when amino acids and reducing sugars react under heat. Understanding these kinetics enables optimization of flavor development, control of color changes during processing and storage, and prediction of product quality evolution.

1 source1912
genetics

McDonald-Kreitman Test

The McDonald-Kreitman (MK) test is a statistical method for detecting adaptive evolution by comparing ratios of synonymous and nonsynonymous substitutions within and between species. Developed by James McDonald and Martin Kreitman in 1991, this test exploits the key insight that neutral mutations accumulate at similar

3 sources1991
ecology

Metabolic Theory of Ecology

The Metabolic Theory of Ecology (MTE), developed by Brown and colleagues (2004), provides a unifying framework linking individual metabolic rate to ecological patterns across levels of organization (organisms, populations, ecosystems). MTE predicts how metabolic rate scales with body size (allometry) and temperature, a

3 sources2004
bioinformatics

Metabolomics analysis

Metabolomics analysis is the large-scale, systematic measurement of small-molecule metabolites in a biological sample to characterise the metabolome — the complete set of metabolic intermediates and products present under defined conditions. By coupling high-throughput analytical platforms such as mass spectrometry (MS

2 sources1998
bioinformatics

Metagenomic Binning

Metagenomic binning partitions assembled contigs from complex microbial communities into distinct genome bins, each representing an individual organism or strain. Pioneered by Banfield and colleagues, this pipeline isolates single-organism genomes (metagenome-assembled genomes or MAGs) from environmental samples withou

3 sources2011
veterinary science

Microhabitat Preference Analysis

Microhabitat Preference Analysis is a quantitative ecological method used to determine which fine-scale environmental features — such as vegetation structure, substrate type, temperature, or cover — animals actively select beyond what is randomly available to them. Widely applied in veterinary science, wildlife biology

2 sources1970
food science

Minimum Inhibitory Concentration Assay

The Minimum Inhibitory Concentration (MIC) assay is a quantitative in vitro method that determines the lowest concentration of an antimicrobial agent — such as a food preservative, essential oil, or synthetic antibiotic — that visibly inhibits the growth of a target microorganism. Widely used in food science, microbiol

2 sources1970
forestry

Modulus of Rupture and Elasticity

The Modulus of Rupture (MOR) and Modulus of Elasticity (MOE) are standardized measures of wood mechanical properties determined through static bending tests. MOR quantifies the maximum bending stress wood can withstand before failure; MOE measures stiffness (resistance to bending). These are fundamental properties used

2 sources1950
bioinformatics

Molecular Docking

Molecular docking predicts the preferred binding orientation and affinity of a ligand (small molecule) within a protein binding pocket. Pioneered by Kuntz and colleagues in 1982, this computational method searches conformational space to find energetically favorable ligand-protein complexes, enabling rapid screening of

3 sources1982
bioinformatics

Multi-omics epigenome-wide association study

A multi-omics epigenome-wide association study (multi-omics EWAS) systematically scans the entire epigenome — typically DNA methylation at CpG sites — for associations with a phenotype of interest, then integrates findings across additional omics layers such as transcriptomics, genomics, proteomics, or metabolomics. By

2 sources2011
bioinformatics

Multi-omics eQTL analysis

Multi-omics eQTL analysis maps genetic variants (SNPs or structural variants) to molecular phenotypes simultaneously across multiple omics layers — transcriptome, epigenome, proteome, and metabolome — in the same cohort. By linking genotype to gene expression and then tracing those effects through downstream molecular

2 sources2010
bioinformatics

Multi-omics gene set enrichment analysis

Multi-omics gene set enrichment analysis (multi-omics GSEA) is a computational pipeline that applies GSEA logic simultaneously across two or more molecular measurement layers — such as transcriptomics, proteomics, and metabolomics — to identify biological pathways or gene sets that are coordinately dysregulated across

2 sources2005
bioinformatics

Multi-omics metabolomics analysis

Multi-omics metabolomics analysis integrates metabolite profiling data — derived from mass spectrometry or NMR spectroscopy — with genomic, transcriptomic, and/or proteomic datasets to build a system-level view of biological phenotypes. By anchoring integration on the metabolome, which reflects the downstream functiona

2 sources2000
bioinformatics

Multi-omics microbiome diversity analysis

Multi-omics microbiome diversity analysis integrates two or more omic data layers — such as metagenomics, metatranscriptomics, metabolomics, and metaproteomics — to characterise both the composition and functional activity of microbial communities. By linking taxonomic diversity metrics with molecular phenotype data, t

2 sources2010
bioinformatics

Multi-omics Pathway Enrichment Analysis

Multi-omics pathway enrichment analysis is a bioinformatics pipeline that integrates molecular data from two or more omics layers — such as transcriptomics, proteomics, metabolomics, and epigenomics — and tests whether the combined signal from those layers converges on specific biological pathways more than expected by

2 sources2014
bioinformatics

Multi-omics Phylogenetic Analysis

Multi-omics phylogenetic analysis reconstructs evolutionary relationships among organisms by integrating sequence data from multiple molecular layers — genomes, transcriptomes, and proteomes — rather than relying on a single marker gene. By combining thousands of orthologous loci across omics layers, the approach drama

2 sources1990
bioinformatics

Multi-omics proteomics analysis

Multi-omics proteomics analysis integrates protein abundance data from mass spectrometry with at least one additional omics layer — such as genomics, transcriptomics, or metabolomics — to build a systems-level view of biological regulation. Rather than analyzing proteins in isolation, this approach correlates proteomic

2 sources2010
bioinformatics

Multi-omics RNA-seq differential expression

Multi-omics RNA-seq differential expression analysis combines transcript-level count data from RNA sequencing with one or more additional omics layers — such as proteomics, metabolomics, epigenomics, or genomic variant data — to identify genes, proteins, or metabolites that differ systematically between biological cond

2 sources2010
bioinformatics

Multi-omics single-cell RNA-seq analysis

Multi-omics single-cell RNA-seq analysis integrates two or more molecular layers — such as gene expression (scRNA-seq), chromatin accessibility (scATAC-seq), or surface protein abundance (CITE-seq) — measured simultaneously or co-profiled in the same individual cells. By aligning these modalities in a shared low-dimens

2 sources2015
veterinary science

NDF/ADF Analysis

Neutral Detergent Fiber (NDF) and Acid Detergent Fiber (ADF) analysis is a chemical fractionation method that separates feed components into digestible and indigestible portions based on their resistance to sequential detergent treatments. Developed by Peter J. Van Soest in the 1960s, NDF/ADF analysis provides rapid es

3 sources1963
bioinformatics

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 sources2011
bioinformatics

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 sources2010
bioinformatics

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 sources2008
bioinformatics

Network-based gene set enrichment analysis

Network-based gene set enrichment analysis (network GSEA) extends classical GSEA by incorporating biological interaction networks — such as protein-protein interaction (PPI) or co-expression graphs — into the enrichment test. Instead of treating each gene independently, the method propagates differential expression sig

2 sources2010
bioinformatics

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 sources2011
bioinformatics

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 sources2005
bioinformatics

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 sources2012
bioinformatics

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 sources2002
bioinformatics

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 sources1992
bioinformatics

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 sources2002
bioinformatics

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 sources2015
bioinformatics

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 sources2017
ecology

Niche Modeling

Niche modeling, also called species distribution modeling (SDM), predicts the geographic range and habitat suitability of species using presence-only or presence-background occurrence data and environmental variables. MaxEnt (Maximum Entropy, Phillips et al. 2006) and GARP (Genetic Algorithm for Rule-set Prediction, St

3 sources1999
agronomy

Nitrogen Use Efficiency

Nitrogen Use Efficiency (NUE) assessment and optimization is an analytical pipeline for evaluating how effectively crops convert applied nitrogen fertilizer into grain, biomass, or economic output. Developed by agronomic researchers (Dobermann, Raun) in the 2000s, this method quantifies nitrogen losses and identifies m

2 sources2005
agronomy

Palynology

Palynology is the scientific study of pollen grains and plant spores — microscopic structures that are chemically resistant and preserve well in sediment, soil, peat, ice, and other matrices. In agronomy, palynology is applied to reconstruct past vegetation and land-use histories, monitor crop pollination dynamics, tra

2 sources1916
bioinformatics

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 sources2003
agronomy

Pedogenesis Modeling

Pedogenesis modeling is a quantitative method used in agronomy and soil science to simulate the processes by which soils form and evolve over time. Rooted in Hans Jenny's 1941 factorial framework — soil as a function of climate, organisms, relief, parent material, and time — modern approaches translate these conceptual

2 sources1941
agronomy

Penman-Monteith Equation

The Penman-Monteith equation is a mechanistic model for estimating evapotranspiration (ET), the combined loss of water from soil and plant canopies to the atmosphere. First proposed by Penman (1948) for bare soil and water surfaces, then extended by Monteith (1965) to incorporate plant resistance to water vapor diffusi

3 sources1948
agronomy

Pesticide Efficacy Trial

Pesticide Efficacy Trial is an experimental design and analysis pipeline for evaluating the effectiveness of fungicides, insecticides, and other plant protection products under field or greenhouse conditions. Standardized by EPPO and IOBC, this method quantifies pest or disease control and informs regulatory approval,

2 sources2010
bioinformatics

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 sources1977
agronomy

Phenological Observation

Phenological Observation is an observational and classification pipeline for systematically recording crop development stages from germination to maturity. Standardized through crop-specific scales (Zadoks for cereals, Fehr for soybean), this method enables precise communication of crop status, timing of management dec

2 sources1974
horticulture

Phenological Stage Monitoring

Phenological stage monitoring uses standardized growth scales to track the developmental progression of plants from dormancy through flowering, fruit development, and maturity. The BBCH scale, formalized in 1997, provides a universal coding system for precise communication of developmental timing. This method enables o

2 sources1997
veterinary science

Photogrammetry

Photogrammetry is a non-contact measurement technique that derives accurate 3D geometry and spatial dimensions from sets of overlapping 2D photographs. In veterinary science it is used to obtain body measurements, wound areas, limb morphology, and anatomical volumes from live animals, carcasses, or skeletal specimens w

2 sources1850
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