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Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

Updated: Jun 29, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Published on: July 1, 2020

Gene and pathway identification with Lp penalized Bayesian logistic regression.

Zhenqiu Liu1, Ronald B Gartenhaus, Ming Tan

  • 1Division of Biostatistics, University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu

BMC Bioinformatics
|October 7, 2008
PubMed
Summary

This study introduces a faster Bayesian approach to identify cancer-related genes and pathways by eliminating the need for time-consuming parameter tuning. The method efficiently finds important regulatory genes, even with small expression changes, improving biological significance detection.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Bioinformatics and computational biology
  • Cancer genomics
  • Systems biology

Background:

  • Identifying disease-associated genes and pathways is crucial in bioinformatics.
  • Gene expression magnitude doesn't always correlate with biological significance.
  • Sparse logistic regression requires time-consuming parameter tuning via cross-validation.

Purpose of the Study:

  • To develop a computationally efficient method for identifying biologically significant genes and pathways in cancer.
  • To overcome the limitations of traditional sparse logistic regression, particularly the need for parameter tuning.

Main Methods:

  • A novel Bayesian approach integrating the regularization parameter analytically using a new prior.
  • Development of the Bayesian Lp Logistic (BLpLog) algorithm, eliminating the need for parameter selection.
  • Introduction of a new similarity measure to identify regulatory genes with low expression changes but high correlation.

Main Results:

  • The BLpLog algorithm is significantly faster (2-3 orders of magnitude) and free from performance estimation bias.
  • Successfully identified regulatory genes with subtle expression changes but high biological correlation.
  • Utilized DAVID to identify pathways associated with these correlated genes.

Conclusions:

  • The proposed methods effectively identify important cancer-related genes and pathways.
  • Enables the construction of parsimonious models for future patient predictions.
  • Offers a more efficient and accurate approach to gene expression analysis in cancer research.