ScholarGate

اكتشف

المكتبة مكتبتي المنضدة فحص مسبق Review Studio المساعد

مساحة العمل

أنشئ رفّ كتبك

احفظ الطرق، ونظّم المجموعات، وانقلها إلى منضدتك.

إنشاء حساب

المكتبة / تصفح

تسجيل الدخول

المكتبة

استكشف العلم حسب المنهج والمجال والأدلة.

فهرس واحد لمناهج البحث — تعرّف على طريقة عمل كل منهج، ومتى يُستخدم، وما الذي لا يستطيع فعله.

6,495 مناهج11 مجالات7 عائلات مناهج40 لغات

أطلس العلمارسم خريطة بنية العلم قبل أن تستخدمه.المجالات · المناهج · مسارات الأدلةاستكشف الخريطة

المجالHealth & Medicine716 Psychology570 Business & Finance410 Engineering330 Life Sciences263 Education261 Research Practice248 Natural Sciences

مكتبة مرجعية يتصدّرها المحتوى لطرق البحث — ما كل طريقة، وكيف تعمل، ومن أين جاءت.

بيانات مفتوحة (CC-BY)

اكتشف

المكتبة
ابحث في الطرق…
تصفّح حسب المجال
المجالات
الرحلة
قارن
أي طريقة؟

مرجع

المواضيع
الأطلس
المسرد
المنهجية
الفلسفة

مساحة العمل

مكتبتي
المنضدة
محادثة

الشركة

حول
الأسعار
اتصل بنا
اقترح طريقة

المداخل مجمَّعة من مصادر منشورة لأغراض مرجعية. ويبقى التحقق من دقة أي معلومة ومدى ملاءمتها لاستخدامك الخاص مسؤوليتك وحدك.

© 2026 ScholarGate · مكتبة مرجعية لطرق البحث

الخصوصية
ملفات تعريف الارتباط
الشروط
حذف الحساب

236

Social Sciences185

Environment & Sustainability160

المنهجالإحصاء1,836 الذكاء الاصطناعي وتعلم الآلة1,661 علوم القرار932 مناهج البحث1,354 القياس1,745 السببية والأدلة532 الممارسة البحثية118

21 مناهج في Life Sciences · الإحصاءمسح

المناهج عند تقاطع عاملَي التصفية لديك.

ترتيبالشيوع أ–ي ي–أ الأحدث

Bayesian ChIP-seq peak calling

Bayesian ChIP-seq peak calling applies probabilistic models — typically Poisson, negative binomial, or hidden Markov models with Bayesian inference — to detect genomic regions enriched for a protein of interest in chromatin immunoprecipitation followed by sequencing experiments. By explicitly modelling read-count noise

2 مصادر2008

Bayesian Copy Number Variation Analysis

Bayesian copy number variation (CNV) analysis is a probabilistic framework for detecting genomic segments where an individual's DNA copy count deviates from the diploid norm. By placing prior distributions over copy-number states and updating them with array CGH, SNP array, or sequencing read-depth evidence, the approa

2 مصادر2004

Bayesian epigenome-wide association study

A Bayesian EWAS is a genome-scale association analysis that links epigenetic marks — most commonly CpG-site DNA methylation — to a phenotype or trait of interest, replacing or supplementing the classical frequentist p-value framework with a Bayesian probabilistic model. It yields posterior probabilities of association

2 مصادر2010

Bayesian epigenome-wide association study in educational research

A Bayesian epigenome-wide association study (Bayesian EWAS) scans hundreds of thousands of DNA methylation sites across the genome to identify those statistically associated with an educational outcome — such as cognitive ability, attainment, or socioeconomic exposure during schooling. Unlike classical frequentist EWAS

2 مصادر2010

Bayesian eQTL analysis

Bayesian eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression by combining genotype and RNA-seq data within a probabilistic framework. Unlike frequentist approaches that rely on p-value thresholds, the Bayesian formulation produces posterior probabilities of association, enabling principled f

2 مصادر2000

Bayesian Gene Set Enrichment Analysis

Bayesian gene set enrichment analysis (Bayesian GSEA) applies a probabilistic framework to determine whether predefined sets of genes — representing biological pathways, cellular processes, or functional categories — are collectively more differentially expressed than expected by chance. Unlike classical frequentist GS

2 مصادر2004

Bayesian genome-wide association study in educational research

Bayesian genome-wide association study (Bayesian GWAS) applies Bayesian statistical models to millions of single-nucleotide polymorphisms (SNPs) to identify genetic variants associated with educational outcomes such as years of schooling or cognitive test scores. Unlike classical frequentist GWAS, Bayesian approaches a

2 مصادر2013

Bayesian GWAS

Bayesian GWAS applies Bayesian statistical inference to genome-wide association studies, replacing classical p-value thresholds with Bayes factors and posterior probabilities. This framework naturally incorporates prior knowledge about effect sizes and variant frequencies, quantifies evidence for association on a conti

2 مصادر2007

Bayesian Metabolomics Analysis

Bayesian metabolomics analysis applies probabilistic inference to metabolite abundance data — typically from mass spectrometry or NMR spectroscopy — to identify differentially abundant metabolites, annotate spectral features, and integrate pathway knowledge. By encoding prior biological knowledge into prior distributio

2 مصادر2005

Bayesian Microbiome Diversity Analysis

Bayesian microbiome diversity analysis applies probabilistic models — chiefly Dirichlet-Multinomial and related hierarchical frameworks — to 16S rRNA or shotgun metagenomic count data to estimate alpha-diversity (within-sample richness and evenness) and beta-diversity (between-sample compositional differences) while pr

2 مصادر2010

Bayesian Pathway Enrichment Analysis

Bayesian pathway enrichment analysis tests whether a predefined set of genes — a biological pathway — is systematically overrepresented among genes that show evidence of differential activity in an experiment. Unlike classical over-representation tests, it encodes prior biological knowledge as a prior distribution and

2 مصادر2001

Bayesian Phylogenetic Analysis

Bayesian phylogenetic analysis uses Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling to estimate the posterior probability distribution over phylogenetic trees and model parameters given observed sequence data. Unlike bootstrapped maximum-likelihood methods that return a single best tree, Bayesian inference

2 مصادر1996

Bayesian Proteomics Analysis

Bayesian proteomics analysis applies probabilistic models to mass spectrometry data to identify peptides, infer protein presence, and quantify differential protein abundance across conditions. By encoding prior knowledge and propagating uncertainty through each step of the pipeline, Bayesian approaches produce calibrat

2 مصادر2000

Bayesian RNA-seq differential expression

Bayesian RNA-seq differential expression analysis applies hierarchical Bayesian models to RNA sequencing read-count data to identify genes whose expression levels differ significantly between biological conditions. Rather than relying solely on p-values, these methods quantify the posterior probability that a gene is d

2 مصادر2010

Bayesian Sequence Alignment

Bayesian sequence alignment treats the alignment of biological sequences (DNA, RNA, or protein) as a probabilistic inference problem rather than a deterministic optimization. Instead of returning a single best alignment, it samples from a posterior distribution over all plausible alignments given a substitution model a

2 مصادر2001

Bayesian single-cell RNA-seq analysis

Bayesian single-cell RNA-seq analysis applies probabilistic generative models to the sparse, overdispersed count matrices produced by single-cell RNA sequencing. By placing prior distributions over latent biological variables — cell state, batch effects, dropout — the framework propagates uncertainty through every down

2 مصادر2018

Bayesian Variant Calling

Bayesian variant calling is a computational pipeline that uses probabilistic inference to identify single-nucleotide polymorphisms (SNPs), insertions, and deletions in a genome by treating sequencing data as evidence and computing posterior probabilities over candidate genotypes. Unlike deterministic threshold-based ca

2 مصادر2010

Freeze-Drying (Lyophilization)

Freeze-drying, also called lyophilization, is a low-temperature dehydration process in which water is first frozen solid and then removed by sublimation under reduced pressure, bypassing the liquid phase entirely. Widely used in food science, pharmaceuticals, and biotechnology, it preserves the physical structure, nutr

2 مصادر1890

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 مصادر2000

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 مصادر2010

SIAR Mixing Model

The Stable Isotope Analysis in R (SIAR) mixing model is a Bayesian framework for estimating the proportional contributions of dietary sources to a consumer, using stable isotope ratios. Developed by Parnell and colleagues (2010) and implemented in the R package siar (and its successor MixSIAR), this method integrates i

3 مصادر2010