Application of deep learning methods in biological networks. Issue 2 (2nd May 2020)

• Record Type: Journal Article
• Authors: Jin, Shuting; Zeng, Xiangxiang; Xia, Feng; Huang, Wei; Liu, Xiangrong
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 2(2021)
• Page Start: 1902
• Page End: 1917
• Publication Date: 2020-05-02
• Subjects: biomolecule -- biological information -- biological networks -- deep learning -- deep neural network -- graph neural network; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa043
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: The increase in biological data and the formation of various biomolecule interaction databases enable us to obtain diverse biological networks. These biological networks provide a wealth of raw materials for further understanding of biological systems, the discovery of complex diseases and the search for therapeutic drugs. However, the increase in data also increases the difficulty of biological networks analysis. Therefore, algorithms that can handle large, heterogeneous and complex data are needed to better analyze the data of these network structures and mine their useful information. Deep learning is a branch of machine learning that extracts more abstract features from a larger set of training data. Through the establishment of an artificial neural network with a network hierarchy structure, deep learning can extract and screen the input information layer by layer and has representation learning ability. The improved deep learning algorithm can be used to process complex and heterogeneous graph data structures and is increasingly being applied to the mining of network data information. In this paper, we first introduce the used network data deep learning models. After words, we summarize the application of deep learning on biological networks. Finally, we discuss the future development prospects of this field.