LncRNA-disease association prediction based on neighborhood information aggregation in neural network

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Conference Proceeding
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, 2019, pp. 175-178
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© 2018 IEEE. Recent studies have demonstrated that IncRNAs play pivotal roles in various biological processes. Some computational methods have been developed to infer IncRNA-disease associations. However, the experimental identification is time-consuming. In this paper, we introduce a method named NNHLDA, which is based on neighborhood information aggregation in neural network. NNHLDA outperforms previous methods at IncRNA-disease association prediction in heterogeneous network. To evaluate our method, we conduct several experiments. In leave-one-out cross-validation (LOOCV) experiments, our NNHLDA method performed better than current state-of-the-art approach. Furthermore, we extracted top 100 IncRNA-disease associations identified by our method and conducted case studies on gastric cancer. The predictions have been confirmed by verified experimental results. Therefore, it is anticipated that NNHLDA could be a useful tool for biomedical researches.
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