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Related Experiment Video

Updated: Oct 14, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe-Disease Associations.

Xiaoyu Yang1,2, Linai Kuang1, Zhiping Chen2

  • 1Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.

Frontiers in Genetics
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces MSBMFHMDA, a novel computational method to predict microbe-disease associations using multi-similarities bilinear matrix factorization. The model demonstrates high accuracy, aiding in understanding the link between microbes and human diseases.

Keywords:
association predictiondiseasematrix factorizationmicrobemulti-similarities

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Area of Science:

  • Microbiology
  • Computational Biology
  • Genomics

Background:

  • Growing evidence links microbial communities to various human diseases.
  • Accurate prediction of microbe-disease associations is crucial for understanding disease mechanisms and developing targeted therapies.

Purpose of the Study:

  • To develop and validate a novel computational method, MSBMFHMDA, for predicting potential microbe-disease associations.
  • To leverage multi-similarities and bilinear matrix factorization for enhanced prediction accuracy.

Main Methods:

  • Constructed a microbe multiple similarities matrix using Gaussian interaction profile kernel similarity and cosine similarity.
  • Composed a disease multiple similarities matrix incorporating Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity.
  • Integrated similarity matrices and the microbe-disease association matrix into the MSBMFHMDA model.

Main Results:

  • Achieved high Area Under the Curve (AUC) values of 0.9186 (LOOCV) and 0.9043 (5-fold CV).
  • Identified top-ranking microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus with high validation rates (10/10, 10/10, and 8/10, respectively).

Conclusions:

  • MSBMFHMDA demonstrates favorable performance and reliability in predicting microbe-disease associations.
  • The method provides a valuable tool for exploring the complex interplay between microbes and human health.