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Bayesian Machine Learning and Its Potential Applications to the Genomic Study of Oral Oncology

¹ Department of Biostatistics, Boston University School of Public Health;
² Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine;
³ Harvard Partners Center for Genetics and Genomics, Harvard Medical School, 77 Avenue Louis Pasteur, NRB 255C, Boston, MA 02115;
⁴ Informatics Program, Children’s Hospital, Boston;

Correspondence: ^* corresponding author, marco_ramoni{at}harvard.edu

With the completion of the Human Genome Project and the growing computational challenges presented by the large amount of genomic data available today, machine learning is becoming an integral part of biomedical research and plays a major role in the emerging fields of bioinformatics and computational biology. This situation offers unparalleled opportunities and unprecedented challenges to machine learning research in general and to Bayesian learning methods in particular. This paper outlines some of the opportunities and the challenges of this endeavor, it describes where the efforts of "cracking the code of life" can most benefit from a Bayesian approach, and it identifies some potential applications of Bayesian machine learning methods to the genomic analysis of squamous cell carcinomas of the head and neck.

KEY WORDS: Functional genomics • bioinformatics • machine learning • Bayesian statistics • oral cancer • head and neck squamous cell carcinoma (HNSCC)

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