Machine Learning Heuristics on Gingivobuccal Cancer Gene Datasets Reveals Key Candidate Attributes for Prognosis
Authors:
- Tanvi SinghTanvi Singh
- Girik MalikGirik Malik
- Saloni SomeshwarSaloni Someshwar
- Hien Thi Thu LeHien Thi Thu Le
- Rathnagiri PolavarapuRathnagiri Polavarapu
- Laxmi ChavaliLaxmi Chavali
- Jayaraman K. ValadiJayaraman K. Valadi
- Vijayaraghava Seshadri SundararajanVijayaraghava Seshadri Sundararajan
- Nidheesh MNidheesh M
- Kavi Kishor PbKavi Kishor Pb
- Prashanth N SuravajhalaPrashanth N Suravajhala
Preprint
- 24 October 2022
- preprint
- Published by MDPI AG in Preprints.org
Abstract
Delayed cancer detection is one of the common causes of poor prognosis in case of many cancers including the cancers of the oral cavity. Despite improvement and development of new and efficient gene therapy treatments, very little has been done to algorithmically assess the impedance of these carcinomas. In this work, we attempt to annotate viable attributes in oral cancer gene datasets for identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated to other forms of oral cancer detection.Keywords
All Related Versions
- Version 1, 2022-10-24, Preprints
- Published version: Genes, 13 (12), 2379.