Predicting Microbe-Disease Associations Based on a Linear Neighborhood Label Propagation Method with Multi-order Similarity Fusion Learning
- 4 March 2024
- journal article
- research article
- Published by Springer Nature in Interdisciplinary Sciences: Computational Life Sciences
- Vol. 16 (2) , 345-360
Abstract
Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases.Keywords
Funding Information
- National Natural Science Foundation of China (62002070, 82001331)
- Science and Technology Plan Project of Guangzhou City (202102021236)
This publication has 46 references indexed in Scilit:
- MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear normComputational and Structural Biotechnology Journal, 2023
- Organoids and organs-on-chips: Insights into human gut-microbe interactionsCell Host & Microbe, 2021
- Exercise and Type 1 Diabetes2020
- Bacteroidetes and Firmicutes levels in gut microbiota and effects of hosts TLR2/TLR4 gene expression levels in adult type 1 diabetes patients in Istanbul, TurkeyJournal of Diabetes and its Complications, 2020
- The Human Microbiota and AsthmaClinical Reviews in Allergy & Immunology, 2019
- The linear neighborhood propagation method for predicting long non-coding RNA–protein interactionsNeurocomputing, 2018
- CMFHMDA: Collaborative Matrix Factorization for Human Microbe-Disease Association Prediction2017
- Human Intestinal Microbiota and Type 1 DiabetesCurrent Diabetes Reports, 2013
- Understanding Type 1 Diabetes: Etiology and ModelsCanadian Journal of Diabetes, 2013
- Epidemiology of Type 1 DiabetesEndocrinology and Metabolism Clinics of North America, 2010