Prediction of small molecule drug-miRNA associations based on GNNs and CNNs

MicroRNAs (miRNAs) play a crucial role in various biological processes and human diseases, and are considered as therapeutic targets for small molecules (SMs). Due to the time-consuming and expensive biological experiments required to validate SM-miRNA associations, there is an urgent need to develop new computational models to predict novel SM-miRNA associations. The rapid development of end-to-end deep learning models and the introduction of ensemble learning ideas provide us with new solutions. Based on the idea of ensemble learning, we integrate graph neural networks (GNNs) and convolutional neural networks (CNNs) to propose a miRNA and small molecule association prediction model (GCNNMMA). Firstly, we use GNNs to effectively learn the molecular structure graph data of small molecule drugs, while using CNNs to learn the sequence data of miRNAs. Secondly, since the black-box effect of deep learning models makes them difficult to analyze and interpret, we introduce attention mechanisms to address this issue. Finally, the neural attention mechanism allows the CNNs model to learn the sequence data of miRNAs to determine the weight of sub-sequences in miRNAs, and then predict the association between miRNAs and small molecule drugs. To evaluate the effectiveness of GCNNMMA, we implement two different cross-validation (CV) methods based on two different datasets. Experimental results show that the cross-validation results of GCNNMMA on both datasets are better than those of other comparison models. In a case study, Fluorouracil was found to be associated with five different miRNAs in the top 10 predicted associations, and published experimental literature confirmed that Fluorouracil is a metabolic inhibitor used to treat liver cancer, breast cancer, and other tumors. Therefore, GCNNMMA is an effective tool for mining the relationship between small molecule drugs and miRNAs relevant to diseases.

[1]  Xiangzheng Fu,et al.  NSRGRN: a network structure refinement method for gene regulatory network inference , 2023, Briefings Bioinform..

[2]  Li Peng,et al.  Predicting CircRNA-Disease Associations via Feature Convolution Learning With Heterogeneous Graph Attention Network , 2023, IEEE Journal of Biomedical and Health Informatics.

[3]  R. Nussinov,et al.  Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data , 2023, Cell reports methods.

[4]  Ying Liang,et al.  CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data , 2022, Briefings Bioinform..

[5]  Xiangzheng Fu,et al.  DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder , 2022, Briefings Bioinform..

[6]  Xing Chen,et al.  Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations , 2021, Briefings Bioinform..

[7]  Xing Chen,et al.  Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease , 2021, Briefings Bioinform..

[8]  Xing Chen,et al.  Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization , 2021, Briefings Bioinform..

[9]  Xiangxiang Zeng,et al.  Drug repositioning based on the heterogeneous information fusion graph convolutional network , 2021, Briefings Bioinform..

[10]  Xiaomei Zhang,et al.  Oral co-delivery nanoemulsion of 5-fluorouracil and curcumin for synergistic effects against liver cancer , 2020, Expert opinion on drug delivery.

[11]  Wen Zhu,et al.  CMF-Impute: an accurate imputation tool for single-cell RNA-seq data , 2020, Bioinform..

[12]  Jun Yin,et al.  SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association , 2019, RNA biology.

[13]  Xing Chen,et al.  Prediction of Small Molecule-MicroRNA Associations by Sparse Learning and Heterogeneous Graph Inference. , 2019, Molecular pharmaceutics.

[14]  Jia Qu,et al.  RFSMMA: A New Computational Model to Identify and Prioritize Potential Small Molecule-MiRNA Associations , 2019, J. Chem. Inf. Model..

[15]  Ana Kozomara,et al.  miRBase: from microRNA sequences to function , 2018, Nucleic Acids Res..

[16]  Evan Bolton,et al.  PubChem 2019 update: improved access to chemical data , 2018, Nucleic Acids Res..

[17]  Xing Chen,et al.  MicroRNA-small molecule association identification: from experimental results to computational models , 2018, Briefings Bioinform..

[18]  Xing Chen,et al.  Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction , 2018, Front. Pharmacol..

[19]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..

[20]  J. Beermann,et al.  Non-coding RNAs in Development and Disease: Background, Mechanisms, and Therapeutic Approaches. , 2016, Physiological reviews.

[21]  Feixiong Cheng,et al.  Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs , 2016, Oncotarget.

[22]  Jing Wang,et al.  Identifying novel associations between small molecules and miRNAs based on integrated molecular networks , 2015, Bioinform..

[23]  Yahui Chen,et al.  Convolutional Neural Network for Sentence Classification , 2015 .

[24]  P. Spuls,et al.  Photodynamic therapy versus topical imiquimod versus topical fluorouracil for treatment of superficial basal‐cell carcinoma: a single blind, non‐inferiority, randomised controlled trial: a critical appraisal , 2015, The British journal of dermatology.

[25]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[26]  Xia Li,et al.  SM2miR: a database of the experimentally validated small molecules' effects on microRNA expression , 2013, Bioinform..

[27]  E. Alli,et al.  Synergistic chemosensitivity of triple-negative breast cancer cell lines to poly(ADP-Ribose) polymerase inhibition, gemcitabine, and cisplatin. , 2010, Cancer research.

[28]  Fabrizio Costa,et al.  Fast Neighborhood Subgraph Pairwise Distance Kernel , 2010, ICML.

[29]  Fabian J Theis,et al.  PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes , 2010, Genome Biology.

[30]  Masahiro Mizoguchi,et al.  Enhanced expression of DNA topoisomerase II genes in human medulloblastoma and its possible association with etoposide sensitivity , 2007, Journal of Neuro-Oncology.

[31]  Xiaolong Wang,et al.  Sequence analysis Application of latent semantic analysis to protein remote homology detection , 2006 .

[32]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[33]  L. Dunkel,et al.  The Journal of Clinical Endocrinology & Metabolism Printed in U.S.A. Copyright © 2000 by The Endocrine Society Estradiol Acts as a Germ Cell Survival Factor in the , 2022 .

[34]  R. Ellison.  Clinical applications of the fluorinated pyrimidines. , 1961, The Medical clinics of North America.

[35]  OUP accepted manuscript , 2022, Briefings In Bioinformatics.

[36]  OUP accepted manuscript , 2021, Briefings In Bioinformatics.

[37]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[38]  K. Eichler,et al.  Transarterial chemoembolization (TACE) with mitomycin C and gemcitabine for liver metastases in breast cancer , 2009, European Radiology.

[39]  Xin Wang,et al.  Breast cancer resistance protein (BCRP/ABCG2) induces cellular resistance to HIV-1 nucleoside reverse transcriptase inhibitors. , 2003, Molecular pharmacology.