Guest Editorial: Deep Learning For Genomics

The six papers in this special section focus on deep learning for genomics. Thanks to the development of high-throughput technologies, a huge amount of omics data is being produced relative to DNA and RNA sequences and (and also) abundance at individual subject or even at individual cell level. In p...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 19; no. 1; pp. 95 - 96
Main Authors: Di Camillo, Barbara, Nicosia, Giuseppe
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Bioinformatics
Computer vision
Deep learning
Deoxyribonucleic acid
DNA
Gene sequencing
Genomics
Learning algorithms
Machine learning
Natural language processing
Nucleotide sequence
Protein engineering
RNA
Special issues and sections
ISSN:1545-5963, 1557-9964
Online Access:Get full text
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Summary:The six papers in this special section focus on deep learning for genomics. Thanks to the development of high-throughput technologies, a huge amount of omics data is being produced relative to DNA and RNA sequences and (and also) abundance at individual subject or even at individual cell level. In particular, the genomics field is rich in data thanks to the rapid reduction in the cost of genetic sequencing. On the other hand, deep learning is transforming the field of many machine learning applications, such as computer vision and natural language processing, by effectively leveraging on big amount of data and is now emerging as a promising approach for many genomics modeling tasks. The scope of this special section is to discuss novel algorithms, methodologies and applications of deep learning to genomic studies with focus on their potentialities and challenges.
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ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2021.3080094