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DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data.

Kasmika Borah¹, Himanish Shekhar Das¹, Ram Kaji Budhathoki²

¹Department of Computer Science and Information Technology, Cotton University, Hem Baruah Rd, Panbazar, Guwahati, Kamrup Metropolitan district, Assam 781001, India.

Briefings in Bioinformatics

|April 3, 2025

Summary

This summary is machine-generated.

This study introduces a novel hybrid deep recurrent neural network, DOMSCNet, for stomach cancer classification using multi-layer omics data. The DOMSCNet model effectively identifies key features and outperforms existing methods in cancer data analysis.

Keywords:

classification hybrid deep learning hybrid feature selection molecular signature multi-layer omics

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) has accelerated biomedical research, particularly in understanding cancer's molecular basis.
Analyzing complex, high-dimensional multi-layer omics data for cancer is crucial for diagnosis and therapy.
Existing computational methods struggle with diverse omics data and effective feature extraction for integrated analysis.

Purpose of the Study:

To develop a robust hybrid feature selection (HFS) technique for optimal feature detection from multi-layer omics datasets.
To propose a novel hybrid deep recurrent neural network-based model, DOMSCNet, for stomach cancer classification.
To ensure the model's generalizability across different omics datasets and validate its performance on external datasets.

Main Methods:

A hybrid feature selection (HFS) technique, including SelectKBest-maximum relevancy minimum redundancy-Boruta (SMB), was developed.
A novel hybrid deep recurrent neural network model, DOMSCNet, was designed for stomach cancer classification.
The DOMSCNet model was trained and validated on four multi-layer omics datasets and eight external datasets.

Main Results:

The proposed SMB HFS technique demonstrated superior performance compared to other HFS methods.
The DOMSCNet model achieved higher accuracy in classifying stomach cancer across multiple omics datasets.
DOMSCNet outperformed existing and other proposed classifiers in validation tests.

Conclusions:

The developed HFS technique effectively extracts optimal features from complex omics data.
DOMSCNet offers a powerful and generalizable deep learning approach for stomach cancer classification.
This study advances computational methods for multi-layer omics data analysis in cancer research.