Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions
Rapid development of biotechnology has led to the generation of vast amounts of multi-omics data, necessitating the advancement of bioinformatics and artificial intelligence to enable computational modeling to diagnose and predict clinical outcome. Both conventional machine learning and new deep lea...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2022.1050439/full |
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| author | Cristina Baciu Cherry Xu Cherry Xu Mouaid Alim Mouaid Alim Khairunnadiya Prayitno Mamatha Bhat Mamatha Bhat Mamatha Bhat |
| author_facet | Cristina Baciu Cherry Xu Cherry Xu Mouaid Alim Mouaid Alim Khairunnadiya Prayitno Mamatha Bhat Mamatha Bhat Mamatha Bhat |
| author_sort | Cristina Baciu |
| collection | DOAJ |
| description | Rapid development of biotechnology has led to the generation of vast amounts of multi-omics data, necessitating the advancement of bioinformatics and artificial intelligence to enable computational modeling to diagnose and predict clinical outcome. Both conventional machine learning and new deep learning algorithms screen existing data unbiasedly to uncover patterns and create models that can be valuable in informing clinical decisions. We summarized published literature on the use of AI models trained on omics datasets, with and without clinical data, to diagnose, risk-stratify, and predict survivability of patients with non-malignant liver diseases. A total of 20 different models were tested in selected studies. Generally, the addition of omics data to regular clinical parameters or individual biomarkers improved the AI model performance. For instance, using NAFLD fibrosis score to distinguish F0-F2 from F3-F4 fibrotic stages, the area under the curve (AUC) was 0.87. When integrating metabolomic data by a GMLVQ model, the AUC drastically improved to 0.99. The use of RF on multi-omics and clinical data in another study to predict progression of NAFLD to NASH resulted in an AUC of 0.84, compared to 0.82 when using clinical data only. A comparison of RF, SVM and kNN models on genomics data to classify immune tolerant phase in chronic hepatitis B resulted in AUC of 0.8793–0.8838 compared to 0.6759–0.7276 when using various serum biomarkers. Overall, the integration of omics was shown to improve prediction performance compared to models built only on clinical parameters, indicating a potential use for personalized medicine in clinical setting. |
| first_indexed | 2024-04-11T06:45:38Z |
| format | Article |
| id | doaj.art-4502b332f2074bd6976b9890ed670557 |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-04-11T06:45:38Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-4502b332f2074bd6976b9890ed6705572022-12-22T04:39:22ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122022-11-01510.3389/frai.2022.10504391050439Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictionsCristina Baciu0Cherry Xu1Cherry Xu2Mouaid Alim3Mouaid Alim4Khairunnadiya Prayitno5Mamatha Bhat6Mamatha Bhat7Mamatha Bhat8Ajmera Transplant Program, University Health Network, Toronto, ON, CanadaAjmera Transplant Program, University Health Network, Toronto, ON, CanadaFaculty of Health Sciences, McMaster University, Hamilton, ON, CanadaAjmera Transplant Program, University Health Network, Toronto, ON, CanadaDepartments of Computer Science and Cell and System Biology, University of Toronto, Toronto, ON, CanadaAjmera Transplant Program, University Health Network, Toronto, ON, CanadaAjmera Transplant Program, University Health Network, Toronto, ON, CanadaDivision of Gastroenterology and Hepatology, University Health Network and University of Toronto, Toronto, ON, CanadaToronto General Research Institute, University Health Network, Toronto, ON, CanadaRapid development of biotechnology has led to the generation of vast amounts of multi-omics data, necessitating the advancement of bioinformatics and artificial intelligence to enable computational modeling to diagnose and predict clinical outcome. Both conventional machine learning and new deep learning algorithms screen existing data unbiasedly to uncover patterns and create models that can be valuable in informing clinical decisions. We summarized published literature on the use of AI models trained on omics datasets, with and without clinical data, to diagnose, risk-stratify, and predict survivability of patients with non-malignant liver diseases. A total of 20 different models were tested in selected studies. Generally, the addition of omics data to regular clinical parameters or individual biomarkers improved the AI model performance. For instance, using NAFLD fibrosis score to distinguish F0-F2 from F3-F4 fibrotic stages, the area under the curve (AUC) was 0.87. When integrating metabolomic data by a GMLVQ model, the AUC drastically improved to 0.99. The use of RF on multi-omics and clinical data in another study to predict progression of NAFLD to NASH resulted in an AUC of 0.84, compared to 0.82 when using clinical data only. A comparison of RF, SVM and kNN models on genomics data to classify immune tolerant phase in chronic hepatitis B resulted in AUC of 0.8793–0.8838 compared to 0.6759–0.7276 when using various serum biomarkers. Overall, the integration of omics was shown to improve prediction performance compared to models built only on clinical parameters, indicating a potential use for personalized medicine in clinical setting.https://www.frontiersin.org/articles/10.3389/frai.2022.1050439/fullartificial intelligencemachine learningomics dataliver diseaseclinical outcome prediction |
| spellingShingle | Cristina Baciu Cherry Xu Cherry Xu Mouaid Alim Mouaid Alim Khairunnadiya Prayitno Mamatha Bhat Mamatha Bhat Mamatha Bhat Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions Frontiers in Artificial Intelligence artificial intelligence machine learning omics data liver disease clinical outcome prediction |
| title | Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions |
| title_full | Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions |
| title_fullStr | Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions |
| title_full_unstemmed | Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions |
| title_short | Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions |
| title_sort | artificial intelligence applied to omics data in liver diseases enhancing clinical predictions |
| topic | artificial intelligence machine learning omics data liver disease clinical outcome prediction |
| url | https://www.frontiersin.org/articles/10.3389/frai.2022.1050439/full |
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