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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

Onah, Daniel; (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. (pp. pp. 281-291). SCITEPRESS - Science and Technology Publications: Valletta, Malta. Green open access

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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.

Type: Proceedings paper
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
Event: 14th International Conference on Knowledge Discovery and Information Retrieval
Dates: 24 Oct 2022 - 26 Oct 2022
ISBN: 2184-3228
ISBN-13: 978-989-758-614-9
Open access status: An open access version is available from UCL Discovery
DOI: 10.5220/0011559100003335
Publisher version: https://doi.org/10.5220/0011559100003335
Language: English
Additional information: This is an open access article under the CC BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Gene, Disease, Phenotype, Prediction, Mathematical Model, Machine Learning, Decision Tree, Search Engine, Visualization
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities > Dept of Information Studies
URI: https://discovery.ucl.ac.uk/id/eprint/10158708
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