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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...

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Deep learning-driven multi-omics analysis: enhancing cancer diagnostics and therapeutics.

Jiayang Zhang^1,2, Yilin Che², Rongrong Liu²

¹Department of Radiology, The Second Hospital of Jilin University, 218 zigiang Street, Changchun, 130041, People's Republic of China.

Briefings in Bioinformatics

|August 28, 2025

Summary

This summary is machine-generated.

Deep learning (DL), a type of artificial intelligence (AI), analyzes complex cancer multi-omics data for improved early detection, diagnosis, and prognosis. Its advanced pattern recognition capabilities are revolutionizing personalized cancer medicine.

Keywords:

artificial intelligence cancer early detection deep learning multi-omics tumor biomarker

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Area of Science:

Oncology
Bioinformatics
Artificial Intelligence

Background:

The rapid evolution of multi-omics technologies has led to a surge in cancer data volume, posing significant challenges for analysis.
Artificial intelligence (AI), particularly deep learning (DL), offers powerful capabilities for processing and extracting insights from large, high-dimensional datasets.
DL models mimic the human brain's neural networks, excelling at automatic feature extraction and pattern recognition.

Purpose of the Study:

To review the diverse applications of deep learning (DL) models in analyzing cancer multi-omics data.
To highlight the role of DL in enhancing cancer research areas, including early detection, diagnosis, molecular classification, biomarker discovery, and prognosis prediction.
To emphasize the potential of DL in advancing personalized cancer treatment approaches.

Main Methods:

Review of various deep learning (DL) models and their methodologies.
Analysis of DL applications across genomics, epigenomics, transcriptomics, proteomics, radiomics, and single-cell omics data.
Examination of DL's role in pattern recognition and feature extraction within complex cancer datasets.

Main Results:

Deep learning (DL) models are increasingly applied in cancer research due to their robust data processing capabilities.
DL effectively integrates high-dimensional multi-omics data to improve understanding of cancer development.
DL demonstrates significant potential in early detection, diagnosis, classification, and predicting patient prognosis and treatment response.

Conclusions:

Deep learning (DL) is a transformative tool in cancer multi-omics research, offering advanced analytical power.
The application of DL is expected to expand, further accelerating progress in personalized cancer medicine.
DL facilitates a deeper understanding of cancer biology and enhances the precision of diagnostic and therapeutic strategies.