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Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Cancer-Critical Genes II: Tumor Suppressor Genes

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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
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Updated: Sep 13, 2025

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Deep graph convolutional network-based multi-omics integration for cancer driver gene identification.

Yingzhuo Wu¹, Jialuo Xu¹, Junming Li²

¹School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072 Shaanxi, China.

Briefings in Bioinformatics

|July 27, 2025

Summary

This summary is machine-generated.

We developed deepCDG, a novel graph convolutional network (GCN) model, to identify cancer driver genes by integrating multi-omics data. Our method enhances prediction accuracy and provides biological insights into gene interactions.

Keywords:

cancer driver genes gene modules graph convolutional networks multi-omics data

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Area of Science:

Computational biology and bioinformatics
Genomics and cancer research
Machine learning in healthcare

Background:

Cancer driver genes are crucial for understanding cancer development and guiding therapeutic strategies.
The increasing availability of multi-omics data offers opportunities for advanced computational analysis.
Existing methods for integrating multi-omics data often have limitations in performance.

Purpose of the Study:

To propose deepCDG, a novel deep graph convolutional network (GCN) model for accurate cancer driver gene identification.
To effectively integrate multi-omics data for improved predictive performance in cancer research.
To provide biological interpretability for identified cancer driver genes and gene modules.

Main Methods:

Developed deepCDG, a GCN-based model utilizing shared-parameter GCN encoders for multi-omics feature extraction.
Employed an attention layer for effective feature integration across different omics data.
Utilized a residual-connected GCN predictor and GNNExplainer for gene identification and module discovery.

Main Results:

deepCDG demonstrated effective predictive performance in identifying cancer driver genes.
The model exhibited robustness and computational efficiency in experimental evaluations.
Biological interpretability analysis confirmed the reliability of identified genes and provided insights into gene interactions.

Conclusions:

deepCDG offers an advanced framework for multi-omics data integration in cancer driver gene identification.
The model's ability to identify gene modules enhances understanding of complex biological networks.
The approach shows potential for broader applications in biological research beyond cancer genomics.