Abstract:
Recent studies have demonstrated that IncRNAs play pivotal roles in various biological processes. Some computational methods have been developed to infer IncRNA-disease a...Show MoreMetadata
Abstract:
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.
Date of Conference: 03-06 December 2018
Date Added to IEEE Xplore: 24 January 2019
ISBN Information: