RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization

Abstract

Microbes with abnormal levels have important impacts on the formation and development of various complex diseases. Identifying possible Microbe-Disease Associations (MDAs) helps to understand the mechanisms of complex diseases. However, experimental methods for MDA identification are costly and time-consuming. In this study, a new computational model, RNMFMDA, was developed to find possible MDAs. RNMFMDA contains two main processes. First, Reliable Negative MDA samples were selected based on Positive-Unlabeled (PU) learning and random walk with restart on the heterogeneous microbe-disease network. Second, Logistic Matrix Factorization with Neighborhood Regularization (LMFNR) was developed to compute the association probabilities for all microbe-disease pairs. To evaluate the performance of the proposed RNMFMDA method, we compared RNMFMDA with five state-of-the-art MDA prediction methods based on five-fold cross-validations on microbes, diseases, and MDAs. As a result, RNMFMDA obtained the best AUCs of 0.6332, 0.8669, and 0.9081, respectively for the three five-fold cross validations, significantly outperforming other models. The promising prediction performance may be attributed to the following three features: highly quality negative MDA sample selection, LMFNR-based MDA prediction model, and various biological information integration. In addition, a few predicted microbe-disease pairs with high association scores are worthy of further experimental validation.

Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction

“… Chen et al (2017) exploited the first MDA prediction method (KATZHMDA) based on the KATZ technique. Several MDA prediction models are then developed to discover the possible MDAs, for example, recommendation model based on neighbor information and MDA graph (NGRHMDA) ( Huang et al, 2017 ), network consistency projection method (NCPHMDA) ( Bao et al, 2017 ), network topological similarity method (NTSHMDA) ( Luo and Long, 2018 ), adaptive boosting method ( Peng et al, 2018 ), bi-direction similarity integration propagation method ( Zhang et al, 2018 ), binary matrix completion method (BMCMDA), matrix decomposition method ( Qu et al, 2019 ), and matrix factorization method combing credible negative MDA selection ( Peng et al, 2020a ). The above models obtained better performance for MDA prediction.…”

“…Therefore, in this manuscript, inspired by the neighborhood information method provided by Liu et al (2020) and Peng et al (2020a) and the neighbor propagation algorithm provided by Zhang et al (2018) , we developed an MDA prediction framework by integrating negative MDA selection, linear neighborhood similarity, label propagation, and information integration to find microbes associated with colon cancer and colorectal cancer. Firstly, microbe similarity matrix and disease similarity matrix were computed based on their Gaussian association profile (GAP) and symptom features.…”

“…Secondly, the linear neighborhood similarity of microbes and diseases was calculated based on their neighborhood information, respectively. Thirdly, negative MDAs were selected according to the positive-unlabeled learning algorithm provided by Peng et al (2020a) . Fourthly, a label propagation method was designed to score all unknown microbe–disease pairs, and the scores were integrated based on the information integration method.…”

Mining Microbe-disease Interactions From Literature via a Transfer Learning Model

“…The database only contains 39 disease entities and 292 microbial species, and the relationship between the two entities is established at the text level [11]. Most studies on the prediction of microbial disease associations are based on this database like KATZHMDA [12], NCPHMDA [13], MDLPHMDA [14], RNMFMDA [15]. However, due to the limited types of diseases and microorganisms included in this database, a large amount of information in biomedical texts has not been fully mined.…”

Predicting lncRNA–Protein Interaction With Weighted Graph-Regularized Matrix Factorization

“…Machine learning-based methods mainly contain matrix factorization-based LPI prediction methods and ensemble learning-based LPI prediction methods. Matrix factorization methods have been widely applied to various association prediction areas (Peng et al, 2020). Liu et al (2017), Zhang T. et al (2018), Zhao et al (2018a), and Shen et al (2019) used matrix factorization methods to predict possible LPIs.…”