Year: 2018
Type: article
Abstract: A principal goal of precision medicine is to identify genomic factors that are predictive of outcomes in complex diseases, to provide better insight into their molecular mechanisms. Based on our current understanding, there are many genomic factors that are likely to be pathogenic in small subpopulations while being rare in the population as a whole. This research introduces a new machine learning method for discovering single nucleotide variants (SNVs), both common and rare, that in a given person are predictive of that person developing a disease or disease outcome. The new method described in this research constructs decision tree models, uses a Bayesian score to evaluate the models, and employs a person-specific search strategy to identify SNVs that are predictive in a subpopulation whose members are similar to the person of interest. This method, called the Personalized Decision Tree Algorithm (PDTA), works by constructing a decision tree model from the data and then identifying a path in the tree that has excellent prediction for the person of interest, or constructing a new path if none of the paths in the tree have excellent prediction. The PDTA was refined iteratively on synthetic data and was experimentally evaluated on five datasets. One of the datasets was synthetic, one was semi-synthetic, and three were biological datasets collected from patients with chronic pancreatitis that included one small genomic dataset, a whole exome dataset, and a whole exome dataset focused on patients with diabetes in chronic pancreatitis. The performance of the method was evaluated using area under the Receiver Operating Characteristic curve and F1 score, as well as the ability to retrieve known and unknown rare SNVs. The PDTA was found to be effective to varying degrees in the datasets that were evaluated, creating parsimonious genetic representations for patient-specific groups, with the potential to discover novel variants.
Author: Joyeeta Dutta‐Moscato
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2894436648