关键词: 基因-疾病关联预测, 矩阵补全, 异构信息网络, 弹性网正则化, 生物医学信息处理

Abstract: Predicting gene-disease associations has been a focus in current biomedical research. Most existing methods suffer from the sparsity of gene-disease associations and PU (positive and unlabeled) problem. Therefore, a new algorithm called KIMC (Katz method to boost inductive matrix completion) has been proposed to predict gene-disease associations. The model consists of two steps: pre-estimation based on Katz method and refined estimation based on inductive matrix completion method. It first exploits Katz method to estimate gene-disease associations based on gene-disease heterogeneous networks. This step can alleviate the effect caused by the sparsity of gene-disease associations and PU problem. However, subject to the quality of the similarity network, the Katz method inevitably introduces some noise. Then, to address the challenge, this paper introduces the elastic-net regularization into IMC (inductive matrix completion) to enhance robustness and improve the prediction of gene-disease associations. The experimental results on real datasets show that the method achieves significantly superior precision and recall rates compared with several state-of-the-art models. Meanwhile, this method can solve the cold start problem.

Key words: gene-disease association prediction, matrix completion, heterogeneous information networks, elastic-net regularization, biomedical information processing