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

Tanvi Singh¹, Girik Malik^1,2, Saloni Someshwar¹

¹Bioclues.org, Hyderabad 500072, India.

|December 23, 2022

Summary

This study introduces machine learning to analyze oral cancer gene data for better gingivobuccal cancer (GBC) identification. Early detection through algorithmic assessment of key genes can improve patient prognosis.

Keywords:

data mining gene prioritization genomic datasets machine learning oral cancer

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Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Delayed detection significantly worsens prognosis for oral cavity cancers.
Gene therapy advancements contrast with limited algorithmic assessment of oral cancer impedance.
Identifying key genes is crucial for early diagnosis and improved treatment outcomes.

Purpose of the Study:

To develop an algorithmic approach for assessing oral cancer gene datasets.
To identify and annotate viable attributes for gingivobuccal cancer (GBC) identification.
To apply machine learning methods for predicting GBC prognosis.

Main Methods:

Utilized NCBI's oral cancer datasets, including gene names, protein changes, and clinical significance.
Applied supervised and unsupervised machine learning algorithms to annotated gene attributes.
Focused on identifying key candidate attributes for GBC prognosis.

Main Results:

Machine learning methods successfully identified key attributes within oral cancer gene datasets.
The study highlights specific genes and attributes relevant to GBC prognosis.
Demonstrated the potential for automated identification of critical genes.

Conclusions:

Automated identification of key genes is vital for gingivobuccal cancer (GBC) detection.
This algorithmic approach can be extended to other oral cancer types.
Improved early detection through gene analysis can enhance patient outcomes.