Cancer scientists and mathematicians have found a way to simplify complicated biomolecular data about tumors, in principle making it way easier to prescribe the suitable treatment for a particular patient.
The new computational strategy transforms highly complicated information into a simplified format that focuses on patient-to-patient variation in the molecular signatures of cancer cells, in accordance with the researchers.
Genetic Makeup to Assist Physicians in Taking More Informed Decisions
Donald Geman, a professor in the Department of Applied Mathematics and Statistics who was senior author of the PNAS article said that the main point of this research paper was to introduce this methodology. He further adds that it also reports on some of the preliminary experiments by making use of the method in a bid to distinguish between closely related phenotypes of cancer.
The main challenge for doctors is that each of the primary form of cancer, such as prostrate or breast, might have numerous subtypes, each of which responds differently to a given treatment.
Knowing as much as possible about the impaired biological pathways and genetic makeup of a particular patient could assist the physicians in making more informed decisions about the prognosis and treatment, thereby adjusting them to the particular molecular profile.
Geman, who has earlier devoted many of his years for the improvement of computer vision technology, has been encouraged by the cancer-related project and hopes that it will served as a model for various other fruitful collaborations that involves advanced math and medicine.
This digital approach from scientists at the Johns Hopkins University has been detailed recently on Proceedings of the National Academy of Sciences, a science journal.