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  • A bioinformatic method to predict chronic fatigue syndrome in prostate cancer patients

    April 21, 2015

    Mathematicians and engineers from the University of Oviedo develop an algorithm based on the patients' genomic analysis, which means a great advance to achieve personalized medicine.

    Researchers of the University of Oviedo Enrique de Andrés Galiana and Juan Luis Fernández Martínez

    How can mathematics contribute to fight cancer or find solutions for rare diseases? Researchers from the University of Oviedo have designed a new bioinformatic method to predict chronic fatigue syndrome in prostate cancer patients undergoing radiation therapy. The research group, led by Professor Juan Luis Fernández Martínez, has identified an algorithm that is predictive of chronic fatigue syndrome in prostate cancer patients, by analyzing their gene expression data. The work, developed in collaboration with doctors Leorey Saligan of the National Institute of Health in Washington, and Stephen J. Sonis, of the Brigham and Women's Hospital of Boston, alongside with the Dana-Farber Cancer Institute of the University of Harvard, has just been published in the Cancer Informatics Journal.

    The group of Inverse Problems, Optimization and Machine Learning is working on the design of algorithms and on the search for solutions that allow the application of personalized treatments and minimize potential toxicities resulting therefrom. These methods provide supporting systems to make medical decisions and try to reduce the uncertainty when dealing with a medical problem.

    "Personalized medicine is the basis for the future and we need reliable prediction tools based on the genome of each patient, as well as on other medical data for hospital use. We are determined to find solutions", Juan Luis Fernández Martínez explains.

    This new method helps minimize chronic fatigue risk in prostate cancer patients through the analysis with genetic signatures on a small scale, testing around thirty genes, before administering radiation therapy, in order to decide whether to use it or not. Knowing in advance to what extent the chronic fatigue syndrome is affecting the patient may provide doctors with tools to personalize treatments to the extent possible, and improve the quality of life of patients. The chronic fatigue syndrome affects the immune, nervous, cardiovascular and endocrine systems progressively.

    The journal Cancer Informatics has just published the results of the study, carried out in collaboration with scientists from the Dana-Farber Cancer Institute, University of Harvard and the National Institute of Health in Washington.

    This work leads to the study of the genetic bases involved in the chronic fatigue syndrome, their connection with different types of cancer and some neurological disorders, as well as to the establishment of new therapeutic targets.

    The multidisciplinary collaboration among engineers, mathematicians, computer experts, biochemists, biologists and doctors in medicine is fundamental to advance towards personalized treatments. The group of Inverse Problems, Optimization and Machine Learning is currently collaborating with other biomedical projects, such as the study of chronic lymphatic leukemia, Hodgkin lymphoma, nephrotic syndrome, pancreatic cancer, lung cancer, colon and gastric cancer, and also triple negative breast cancer. This research group has recently taken an important step for the establishment of collaboration activities to find treatments for rare diseases.

    Abstract

    Supervised Classification by Filter Methods and Recursive Feature Elimination Predicts Risk of Radiotherapy-Related Fatigue in Patients with Prostate Cancer

    http://www.la-press.com/supervised-classification-by-filter-methods-and-recursive-feature-elim-article-a4528-metrics

    Group of Inverse Problems, Optimization and Machine Learning

    Juan Luis Fernández Martínez, Enrique Juan de Andrés Galiana, María Zulima Fernández Muñiz, Doina Ana Cernea Corbeanu, Luis Mariano Pedruelo González, José Luis García Pallero, and Juan Carlos Beltrán Vargas.

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