1
|
Ghosh S, Zhao X, Alim M, Brudno M, Bhat M. Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment. Gut 2024:gutjnl-2023-331740. [PMID: 39174307 DOI: 10.1136/gutjnl-2023-331740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 07/24/2024] [Indexed: 08/24/2024]
Abstract
Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.
Collapse
Affiliation(s)
- Soumita Ghosh
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Xun Zhao
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, Ontario, Canada
| | - Mouaid Alim
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Michael Brudno
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Vector Institute of Artificial Intelligence, Toronto, Ontario, Canada
| | - Mamatha Bhat
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Division of Gastroenterology, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada
| |
Collapse
|
2
|
Prada-Luengo I, Schuster V, Liang Y, Terkelsen T, Sora V, Krogh A. N-of-one differential gene expression without control samples using a deep generative model. Genome Biol 2023; 24:263. [PMID: 37974217 PMCID: PMC10655485 DOI: 10.1186/s13059-023-03104-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023] Open
Abstract
Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can identify the closest normal representation for a given disease sample. This enables control-free, single-sample differential expression analysis. In breast cancer, we demonstrate how our approach selects marker genes and outperforms a state-of-the-art method. Furthermore, significant genes identified by the model are enriched in driver genes across cancers. Our results show that the in silico closest normal provides a more favorable comparison than control samples.
Collapse
Affiliation(s)
- Iñigo Prada-Luengo
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Viktoria Schuster
- Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Yuhu Liang
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Thilde Terkelsen
- Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Valentina Sora
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Anders Krogh
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
- Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark.
| |
Collapse
|
3
|
Gao F, Huang K, Xing Y. Artificial Intelligence in Omics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:811-813. [PMID: 36640826 PMCID: PMC10025753 DOI: 10.1016/j.gpb.2023.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 12/20/2022] [Accepted: 01/08/2023] [Indexed: 01/13/2023]
Affiliation(s)
- Feng Gao
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China.
| | - Kun Huang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA; IUPUI Fairbanks School of Public Health, Indianapolis, IN 46202, USA; Regenstrief Institute, Indianapolis, IN 46202, USA.
| | - Yi Xing
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| |
Collapse
|