Machine Learning Heuristics on Gingivobuccal Cancer Gene Datasets Reveals Key Candidate Attributes for Prognosis

• Published in: Genes Vol. 13; no. 12; p. 2379
• Main Authors: Singh, Tanvi, Malik, Girik, Someshwar, Saloni, Le, Hien Thi Thu, Polavarapu, Rathnagiri, Chavali, Laxmi N, Melethadathil, Nidheesh, Sundararajan, Vijayaraghava Seshadri, Valadi, Jayaraman, Kavi Kishor, P B, Suravajhala, Prashanth
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16-12-2022; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Biomarkers; Cancer; Carcinoma; Clinical significance; Datasets; Decision making; Decision trees; Diagnosis; Experiments; Gene therapy; Genes; Genetic aspects; Genomics; Heuristic; Heuristics; Humans; Learning algorithms; Machine Learning; Medical prognosis; Mouth cancer; Mouth Neoplasms - diagnosis; Mouth Neoplasms - genetics; Oncogenes; Oral cancer; Oral cavity; Performance evaluation; Prognosis; Proteins; Regression analysis; Statistical methods; Support vector machines; Tumors; United States; genomic datasets; gene prioritization; machine learning; data mining; oral cancer
• ISSN: 2073-4425; 2073-4425
• DOI: 10.3390/genes13122379

Delayed cancer detection is one of the common causes of poor prognosis in the case of many cancers, including cancers of the oral cavity. Despite the improvement and development of new and efficient gene therapy treatments, very little has been carried out to algorithmically assess the impedance of these carcinomas. In this work, from attributes or NCBI's oral cancer datasets, viz. (i) name, (ii) gene(s), (iii) protein change, (iv) condition(s), clinical significance (last reviewed). We sought to train the number of instances emerging from them. Further, we attempt to annotate viable attributes in oral cancer gene datasets for the 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 in other forms of oral cancer detection.