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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| Published in: | IEEE/ACM transactions on computational biology and bioinformatics Vol. 19; no. 1; pp. 95 - 96 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1545-5963, 1557-9964 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Nicosia, Giuseppe Di Camillo, Barbara |
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| SubjectTerms | 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 |
| Title | Guest Editorial: Deep Learning For Genomics |
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