Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications
Аутори
Stanković, BiljanaKotur, Nikola
Nikčević, Gordana
Gašić, Vladimir
Zukić, Branka
Pavlović, Sonja
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to p...resent fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.
Кључне речи:
transcriptomics / prediction modeling / IBD / genomics / artificial intelligenceИзвор:
Genes, 2021, 12, 9Издавач:
- MDPI, Basel
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200042 (Универзитет у Београду, Институт за молекуларну генетику и генетичко инжењерство) (RS-MESTD-inst-2020-200042)
DOI: 10.3390/genes12091438
ISSN: 2073-4425
PubMed: 34573420
WoS: 000700712400001
Scopus: 2-s2.0-85115639402
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - JOUR AU - Stanković, Biljana AU - Kotur, Nikola AU - Nikčević, Gordana AU - Gašić, Vladimir AU - Zukić, Branka AU - Pavlović, Sonja PY - 2021 UR - https://imagine.imgge.bg.ac.rs/handle/123456789/1460 AB - Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials. PB - MDPI, Basel T2 - Genes T1 - Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications IS - 9 VL - 12 DO - 10.3390/genes12091438 ER -
@article{ author = "Stanković, Biljana and Kotur, Nikola and Nikčević, Gordana and Gašić, Vladimir and Zukić, Branka and Pavlović, Sonja", year = "2021", abstract = "Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.", publisher = "MDPI, Basel", journal = "Genes", title = "Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications", number = "9", volume = "12", doi = "10.3390/genes12091438" }
Stanković, B., Kotur, N., Nikčević, G., Gašić, V., Zukić, B.,& Pavlović, S.. (2021). Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications. in Genes MDPI, Basel., 12(9). https://doi.org/10.3390/genes12091438
Stanković B, Kotur N, Nikčević G, Gašić V, Zukić B, Pavlović S. Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications. in Genes. 2021;12(9). doi:10.3390/genes12091438 .
Stanković, Biljana, Kotur, Nikola, Nikčević, Gordana, Gašić, Vladimir, Zukić, Branka, Pavlović, Sonja, "Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications" in Genes, 12, no. 9 (2021), https://doi.org/10.3390/genes12091438 . .