MSCNE:Predict miRNA-Disease Associations Using Neural Network Based on Multi-Source Biological Information

Genwei Han; Zhufang Kuang; Lei Deng

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MSCNE:Predict miRNA-Disease Associations Using Neural Network Based on Multi-Source Biological Information

机译：MSCNE:Predict miRNA-Disease Associations Using Neural Network Based on Multi-Source Biological Information

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The important role of microRNA (miRNA) in human diseases has been confirmed by some studies. However, only using biological experiments has greater blindness, leading to higher experimental costs. In this paper a high-efficiency algorithm based on a variety of biological source information and applying a combination of a convolutional neural network (CNN) feature extractor and an extreme learning machine (ELM) classifier is proposed. Specifically, the semantic similarity of diseases, the gaussian interaction profile kernel similarity of the four biological information of miRNA, disease, long non-coding RNA (lncRNA) and environmental factors (EFs), and the similarities of miRNAs are fused together. Among them, miRNAs similarity is composed of miRNA target information, sequence information, family information, and function information. Then, the dimensionality of the data set is reduced by the autoencoder (AE). Finally, deep features are extracted through CNN, and then the association between miRNA and disease is predicted by ELM. The experimental results show that the average AUC value based on the multi-biological source information (MSCNE) model is 0.9630, which can reach higher performance than the other classic classifier, feature extractor mentioned and the other existing algorithms. The results show the MSCNE algorithm is effective to predict the correlation of miRNA-disease.

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《IEEE/ACM transactions on computational biology and bioinformatics》 |2022年第5期|2926-2937|共12页
作者
Genwei Han; Zhufang Kuang; Lei Deng;
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作者单位

School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China;

School of Computer Science and Engineering, Central South University, Changsha, China;

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原文格式 PDF
正文语种英语
中图分类生物信息论;
关键词
Diseases; Correlation; Prediction algorithms; Databases; Feature extraction; Convolutional neural networks; Kernel;

MSCNE:Predict miRNA-Disease Associations Using Neural Network Based on Multi-Source Biological Information

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