MKL-LP: Predicting Disease-Associated Microbes with Multiple-Similarity Kernel Learning-Based Label Propagation

A growing number of clinical evidences have proved that there are considerable associations between microbes and diseases. At present, developing computational models to explore unbeknown microbe-disease associations, rather than using the traditionally experimental method which is usually expensive and costs time, is a hot research trend. In this paper, a new method, MKL-LP, which utilizes Multiple Kernel Learning (MKL) and Label Propagation (LP), is presented on the basis of known microbe-disease associations and multiple microbe/disease similarities. Firstly, multiple microbe/disease similarities are calculated. Secondly, for the more comprehensive input information, multiple microbe/disease similarity kernels are fused by MKL to obtain the fused microbe/disease kernel. Then, considering that many non-associations may be positive, a pre-processing step is applied for estimating the association probability of unknown cases in the association matrix by using the microbe/disease similarity information. Then LP is applied for predicting novel microbe-disease associations. After that, 5-fold cross validation is applied to validate the predictive performance of our method with the comparison of the other four predicting methods. Also, in the case study of Chronic Obstructive Pulmonary Disease (COPD), 10 of the first 15 candidate microbes associated with the corresponding disease have literature proof. These suggest that MKL-LP has played a significant role in discovering novel microbe-disease associations, thus providing important insights into complicated disease mechanisms, as well as facilitating new approaches to the diagnosis and treatment of the disease.