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Computational and structural biotechnology journal
2021;19 5762-5790.

AI applications in functional genomics.

Claudia Caudai, Antonella Galizia, Filippo Geraci, Loredana Le Pera, Veronica Morea, Emanuele Salerno, Allegra Via, Teresa Colombo
Author Information
  1. Claudia Caudai: CNR, Institute of Information Science and Technologies "A. Faedo" (ISTI), Pisa, Italy.
  2. Antonella Galizia: CNR, Institute of Applied Mathematics and Information Technologies (IMATI), Genoa, Italy.
  3. Filippo Geraci: CNR, Institute for Informatics and Telematics (IIT), Pisa, Italy.
  4. Loredana Le Pera: CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Bari, Italy.
  5. Veronica Morea: CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy.
  6. Emanuele Salerno: CNR, Institute of Information Science and Technologies "A. Faedo" (ISTI), Pisa, Italy.
  7. Allegra Via: CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy.
  8. Teresa Colombo: CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy.
PMID: 34765093 DOI: 10.1016/j.csbj.2021.10.009

Abstract

We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.

Keywords

Artificial intelligence Deep learning Epigenomics Epitranscriptomics Functional genomics Genomics Machine learning Metabolomics Proteomics Transcriptomics

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