A deep learning approach to predict inter-omics interactions in multi-layer networks

• DOI: 10.1186/s12859-022-04569-2
• Publication Date: 2022-01-26T14:03:43Z
• Authors: Niloofar Borhani; Jafar Ghaisari; Maryam Abedi; Marzieh Kamali; Yousof Gheisari
• Keywords: Data Integration; Feature representation; Deep Learning; QH301-705.5; Research; Inter-omics interaction prediction; Computer applications to medicine. Medical informatics; R858-859.7; Proteins; Deep learning; Biology (General); Algorithms

AbstractBackgroundDespite enormous achievements in the production of high-throughput datasets, constructing comprehensive maps of interactions remains a major challenge. Lack of sufficient experimental evidence on interactions is more significant for heterogeneous molecular types. Hence, developing strategies to predict inter-omics connections is essential to construct holistic maps of disease.ResultsHere, as a novel nonlinear deep learning method, Data Integration with Deep Learning (DIDL) was proposed to predict inter-omics interactions. It consisted of an encoder that performs automatic feature extraction for biomolecules according to existing interactions coupled with a predictor that predicts unforeseen interactions. Applicability of DIDL was assessed on different networks, namely drug–target protein, transcription factor-DNA element, and miRNA–mRNA. Also, validity of the novel predictions was evaluated by literature surveys. According to the results, the DIDL outperformed state-of-the-art methods. For all three networks, the areas under the curve and the precision–recall curve exceeded 0.85 and 0.83, respectively.ConclusionsDIDL offers several advantages like automatic feature extraction from raw data, end-to-end training, and robustness to network sparsity. In addition, reliance solely on existing inter-layer interactions and independence of biochemical features of interacting molecules make this algorithm applicable for a wide variety of networks. DIDL paves the way to understand the underlying mechanisms of complex disorders through constructing integrative networks.