Mulenga, Mwenge and Rajamanikam, Arutchelvan and Kumar, Suresh and Muhammad, Saharuddin bin and Bhassu, Subha and Samudid, Chandramathi and Sabri, Aznul Qalid Md and Seera, Manjeevan and Eke, Christopher Ifeanyi (2025) Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. PLoS ONE, 20 (1). ISSN 1932-6203, DOI https://doi.org/10.1371/journal.pone.0316493.
Full text not available from this repository.Abstract
The emergence of Next Generation Sequencing (NGS) technology has catalyzed a paradigm shift in clinical diagnostics and personalized medicine, enabling unprecedented access to high-throughput microbiome data. However, the inherent high dimensionality, noise, and variability of microbiome data present substantial obstacles to conventional statistical methods and machine learning techniques. Even the promising deep learning (DL) methods are not immune to these challenges. This paper introduces a novel feature engineering method that circumvents these limitations by amalgamating two feature sets derived from input data to generate a new dataset, which is then subjected to feature selection. This innovative approach markedly enhances the Area Under the Curve (AUC) performance of the Deep Neural Network (DNN) algorithm in colorectal cancer (CRC) detection using gut microbiome data, elevating it from 0.800 to 0.923. The proposed method constitutes a significant advancement in the field, providing a robust solution to the intricacies of microbiome data analysis and amplifying the potential of DL methods in disease detection.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Higher Education (MOHE) Transdisciplinary Research Grant Scheme [Grant No: TRGS/1/2018/UM/01/7] |
| Uncontrolled Keywords: | Next Generation Sequencing (NGS) technology; Disease detection; Novel feature engineering method |
| Subjects: | Q Science > Q Science (General) T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science & Information Technology Faculty of Medicine Faculty of Science |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 02 Oct 2025 07:23 |
| Last Modified: | 02 Oct 2025 07:23 |
| URI: | http://eprints.um.edu.my/id/eprint/47963 |
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