Author:
Daniel F. O. Onah
Affiliation:
Department of Information Studies, University College London, London, U.K.
Keyword(s):
Gene, Disease, Phenotype, Prediction, Mathematical Model, Machine Learning, Decision Tree, Search Engine, Visualization.
Abstract:
This research investigates an aspect of precision medicine related to genes and their association with diseases.
Precision medicine is a growing area in medical science research. By definition precision medicine is an
approach that allows the selection of treatments that are most likely to help treat patients based on the genetic
understanding of their diseases. This approach proposes the customization of a medical model for healthcare,
treatment, medical decision making about genetic diseases and develop models that are tailored to individual
patient. There are readily available datasets provided by Genomics England related to diseases and the genes
that cause these diseases. This research presents a predictive technique that scores the possibilities of a mutated
gene causing a neurological phenotype. There are over a thousand genes associated with 26 subtypes of
neurological diseases as defined by Genomics England capturing genetic variation, gene structure and coexpression
network
. The gene prediction was performed with search algorithms and methods that sequentially
looped through the database for true match. Linear search algorithm was applied along index search method to
perform the prediction matching of gene(s) that are associated to the disease(s). The prediction algorithm was
formulated based on a Mathematical/probabilistic concept that was used to design the model for processing
the data-set ready for gene prediction. It became apparent that over half a million (> 500;000) genes were
predicted in this study that were associated to the neurological phenotype of the diseases in this research work.
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