Semi-supervised deep matrix factorization model for clustering multi-omics data

This method provides a robust framework for multi-omics data integration and holds promise for applications in genomics and precision medicine.

Authors

Nirav Joshi (ORCID: https://orcid.org/0000-0003-4220-9622)
Richi Nayak (ORCID: https://orcid.org/0000-0002-9954-0159)
Khanh Luong (ORCID: https://orcid.org/0000-0001-6981-7367)

Institutions

Queensland University of Technology (AU)

Publication Details

Journal: Computer Methods and Programs in Biomedicine
Published: 2025-10-08
DOI: https://doi.org/10.1016/j.cmpb.2025.109094
Primary Topic: Bioinformatics and Genomic Networks
Type: article
Field-Weighted Citation Impact: 0.00

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article

Semi-supervised deep matrix factorization model for clustering multi-omics data

Nirav Joshi, Richi Nayak, Khanh Luong

Oct 8, 2025

Computer Methods and Programs in Biomedicine

Bioinformatics and Genomic Networks

article

Semi-supervised deep matrix factorization model for clustering multi-omics data

Nirav Joshi, Richi Nayak, Khanh Luong

October 8, 2025article en

Abstract

This method provides a robust framework for multi-omics data integration and holds promise for applications in genomics and precision medicine.

Computer Methods and Programs in BiomedicineVol. 273

Queensland University of Technology (AU)

Openalex Percentile: Top 75%

Bioinformatics and Genomic Networks

DOI Open Access PDF OpenAlex

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Semi-supervised deep matrix factorization model for clustering multi-omics data — Nirav Joshi, Richi Nayak, et al. · Computer Methods and Programs in Biomedicine (2025) | TGRS Research Map | TGRS