Affiliation:
1. College of Computer Science and Technology, Qingdao University , Qingdao 266071, China
2. Department of Gastroenterology, Shouguang Hospital of Traditional Chinese Medicine , Weifang 262700, China
Abstract
Abstract
Motivation
The human microbiome, found throughout various body parts, plays a crucial role in health dynamics and disease development. Recent research has highlighted microbiome disparities between patients with different diseases and healthy individuals, suggesting the microbiome’s potential in recognizing health states. Traditionally, microbiome-based status classification relies on pre-trained machine learning (ML) models. However, most ML methods overlook microbial relationships, limiting model performance.
Results
To address this gap, we propose PM-CNN (Phylogenetic Multi-path Convolutional Neural Network), a novel phylogeny-based neural network model for multi-status classification and disease detection using microbiome data. PM-CNN organizes microbes based on their phylogenetic relationships and extracts features using a multi-path convolutional neural network. An ensemble learning method then fuses these features to make accurate classification decisions. We applied PM-CNN to human microbiome data for status and disease detection, demonstrating its significant superiority over existing ML models. These results provide a robust foundation for microbiome-based state recognition and disease prediction in future research and applications.
Availability and implementation
PM-CNN software is available at https://github.com/qdu-bioinfo/PM_CNN.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Shandong Province Youth Entrepreneurial Talent Introduction and Training Program
Shandong Province Taishan Scholars Youth Experts Program
Publisher
Oxford University Press (OUP)