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dc.contributor.authorOkwori, Michael
dc.contributor.authorEslami, Ali
dc.date.accessioned2022-04-18T20:41:00Z
dc.date.available2022-04-18T20:41:00Z
dc.date.issued2021-12-09
dc.identifier.citationM. Okwori and A. Eslami, "CGN-MPred: Cofunctional Gene Network-based Mutation Prediction from Exposure Conditions," 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, pp. 2451-2455, doi: 10.1109/BIBM52615.2021.9669373.en_US
dc.identifier.isbn978-1-6654-2982-5
dc.identifier.isbn978-1-6654-0126-5
dc.identifier.urihttps://soar.wichita.edu/handle/10057/23105
dc.identifier.urihttp://doi.org/10.1109/BIBM52615.2021.9669373
dc.descriptionClick on the DOI link to view this conference paper and video presentation (may not be free).en_US
dc.description.abstractThe prediction of gene mutation of bacteria when exposed to different conditions is beneficial in the development of drugs and vaccines. However, the existing prediction models are suboptimal in their performance. In this work, we propose a time-series approach where we model mutation occurrence sequentially. In our approach, the most connected cofunctional genes are predicted first, and used as additional input features in subsequent predictions. We train a neural network to predict gene mutations in Escherichia coli from the exposure conditions and the predicted state of the cofunctional genes. We test the performance on adaptive laboratory evolution experiments curated from the literature. Results show the cofunctional network-based features improved the predictive performance by a maximum 33.53% and 45.18% in the area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC), respectively. This study supports the feasibility of gene mutation prediction and demonstrates that the functional relationship between genes can be predicted first and then used as features to boost the predictability of its node genes.en_US
dc.description.sponsorship10.13039/100000001-National Science Foundationen_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2021
dc.subjectMutation predictionen_US
dc.subjectMachine learningen_US
dc.subjectFeature engineeringen_US
dc.subjectFunctional gene-networksen_US
dc.subjectData integrationen_US
dc.titleCGN-MPred: Cofunctional gene network-based mutation prediction from exposure conditionsen_US
dc.typeConference paperen_US
dc.rights.holder©2021 IEEEen_US


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