Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis
Document Type
Article
Publication Date
1-1-2025
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.
Keywords
Next Generation Sequencing (NGS) technology, Disease detection, Novel feature engineering method
Divisions
fsktm,fac_med,Science
Funders
Ministry of Higher Education (MOHE) Transdisciplinary Research Grant Scheme [Grant No: TRGS/1/2018/UM/01/7]
Publication Title
PLoS ONE
Volume
20
Issue
1
Publisher
Public Library of Science
Publisher Location
1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA