EnuwaJGX: Machine Learning Gene Prediction Software Application Model - An Innovative Method to Precision Medicine and Predictive Analysis of Visualising Mutated Genes Associated to Neurological Phenotype of Diseases

Daniel F. O. Onah

2022

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.

Paper Citation

in Harvard Style

Onah D. (2022). EnuwaJGX: Machine Learning Gene Prediction Software Application Model - An Innovative Method to Precision Medicine and Predictive Analysis of Visualising Mutated Genes Associated to Neurological Phenotype of Diseases. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 281-291. DOI: 10.5220/0011559100003335

in Bibtex Style

@conference{kdir22,
author={Daniel F. O. Onah},
title={EnuwaJGX: Machine Learning Gene Prediction Software Application Model - An Innovative Method to Precision Medicine and Predictive Analysis of Visualising Mutated Genes Associated to Neurological Phenotype of Diseases},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={281-291},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011559100003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - EnuwaJGX: Machine Learning Gene Prediction Software Application Model - An Innovative Method to Precision Medicine and Predictive Analysis of Visualising Mutated Genes Associated to Neurological Phenotype of Diseases
SN - 978-989-758-614-9
AU - Onah D.
PY - 2022
SP - 281
EP - 291
DO - 10.5220/0011559100003335
PB - SciTePress