Abstract
Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations.
Highlights
Functional studies revealed that RNAs, once thought to be simple messengers from DNA to protein, have important roles in many cellular processes.[1]
In this paper, drawing on previous research on the development of computational models on the miRNA-disease association prediction,[34,35,36,37,38] we introduced a novel computational model of HeteSimbased inference for SM-miRNA Association prediction (HSSMMA) to calculate the relevance between miRNAs and SMs by implementing a path-based measurement method of HeteSim in a heterogeneous network that was constructed based on integrated miRNA similarity, integrated SM similarity, and experimentally confirmed miRNA-SM associations
We further compared the performance of HSSMMA with one previous classical computational model of SMiR-network-based inference (NBI) based on dataset 1 and dataset 2, respectively
Summary
Functional studies revealed that RNAs, once thought to be simple messengers from DNA to protein, have important roles in many cellular processes.[1] They were found to regulate transcription, translation, RNA modification, mRNA stability, chromatin structure, and signaling pathways by interacting with various biological molecules.[2] Most of these processes are related to a variety of human diseases, including cancers and neurodegenerative and neuromuscular diseases.[3,4] These discoveries have validated the potential of RNAs as therapeutic targets.[5] scientists have been excited about. 274 Molecular Therapy: Nucleic Acids Vol 14 March 2019 a 2018 The Authors.
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