MCLPMDA : A novel method for mi RNA ‐disease association prediction based on matrix completion and label propagation

Mi RNA s are a class of small non‐coding RNA s that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between mi RNA s and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related mi RNA s. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for mi RNA ‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new mi RNA /disease similarity matrix by matrix completion algorithm based on known experimentally verified mi RNA ‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related mi RNA s. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true mi RNA ‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for mi RNA ‐disease association prediction.