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Deep Learning in Bioinformatics and Biomedicine - ScienceDirect
Source: sciencedirect.com
Article summary:
1. This special issue provides a collection of recent progress in the studies of deep learning in Bioinformatics and biomedicine.
2. It includes eleven articles that discuss various applications of deep learning, such as identifying native-like protein–ligand complexes, predicting drug sensitivity associated microRNAs, genome-phenome association prediction, single-cell sequencing data clustering, automatic sleep stage scoring, and automated International Classification of Diseases (ICD) coding.
3. The papers demonstrate the potential of deep learning to facilitate virtual screening for drugs and protein–ligand interaction identification during docking, establish relationships between anticancer drug sensitivity and microRNA efficiently, refine large deletions for both linked-reads and short-read whole genome sequencing data, explore nonlinear relationships between molecules and predict genome-phenome associations by fusing heterogeneous molecular networks and diverse attributes of nodes (i.e., genes, miRNAs and pathways), cluster scRNA-seq data while simultaneously learning cell low-dimensional representation, integrate clinical evidence with deep neural networks to improve models’ performance and interpretation, capture complex structures within large datasets, improve the performance of automatic sleep stage scoring by introducing physiologically meaningful learning sub-networks, extract robust text representations from its pre-trained embedding layer for automated International Classification of Diseases (ICD) coding.
Article analysis:
This article is a comprehensive overview of recent advances in deep learning in bioinformatics and biomedicine. The article is well written with clear explanations on each topic discussed. The authors provide detailed descriptions on the various applications of deep learning discussed in the paper as well as their potential benefits. Furthermore, they provide references to relevant research papers which adds credibility to their claims.
The article does not appear to be biased or one sided as it presents both sides equally without any promotional content or partiality towards any particular point of view. Additionally, it does not appear to have any unsupported claims or missing points of consideration as all claims are backed up by evidence from relevant research papers.
The only potential issue with this article is that it does not explore counterarguments or possible risks associated with using deep learning in bioinformatics and biomedicine which could be addressed in future versions of this article.