Latent factor model with heterogeneous similarity regularization for predicting gene disease associations
The correct prediction of human genes related to diseases has been a challenge in biological research. Considering extensive gene disease data verified by biological experiments, we can apply computational methods to perform correct predictions with reduced time and expenses. On the basis of a previously designed latent factorization model (LFM) which performs well in recommender systems, we propose a latent factor model with heterogeneous similarity regularization (LFMHSR) to predict disease related genes. Various types of data, including those of humans and other related species, are used in this method. First, model I with an average heterogeneous regularization is proposed on the basis of a typical LFM. Second, model II with personal heterogeneous regularization is developed to improve the deficiency of the previous model. Data on other nonhuman species and vector space similarity or Pearson correlation coefficient metrics are also utilized in our method. Results reveal that the performance of LFMHSR is 7% more efficient than that of other existing approaches. Therefore, our proposed approach can be employed to predict novel diseases or genes with no known associations.
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)