Abstract
BackgroundSynthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments.ResultsIn this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction.ConclusionsIn this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.
Highlights
Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets
To address the above problems, in this paper, we introduce a novel graph regularized self-representative matrix factorization (GRSMF) model for synthetic lethal (SL) interaction prediction
Results we demonstrate the performance of GRSMF on SynLethDB database [17]
Summary
Cancers are complex diseases that caused by the defects of multiple genes. Exploring the genetic interactions within cancer cells is important for understanding the mechanisms of cancers. According to the principle of the model, existing methods can be roughly classified into three categories: knowledge-based methods, supervised machine learning methods and matrix factorization methods. Supervised machine learning methods utilize existing SL interactions to build up classification models which could be used to predict novel SL interactions. Based on available SL interactions of yeast, various classification models such as maximum likelihood estimation (MLE) [15], support vector machines (SVM) [15] and ensemble classifiers [16], have been developed for predicting SL interactions. Matrix factorization methods have become popular for link prediction in recent years due to their utilities in capturing the underlying mechanisms of observed links and incorporating extra relevant information. Case studies of predicting novel SL interactions demonstrate the effectiveness of GRSMF in predicting SL interactions in human cancer
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