Title :
Machine learning predictions of cancer driver mutations
Author :
Jordan, E. Joseph ; Radhakrishnan, Ravi
Author_Institution :
Biochem. & Mol. Biophys. Grad. Group, Univ. of Pennsylvania, Philadelphia, PA, USA
Abstract :
A method to predict the activation status of kinase domain mutations in cancer is presented. This method, which makes use of the machine learning technique support vector machines (SVM), has applications to cancer treatment, as well as numerous other diseases that involve kinase misregulation.
Keywords :
bioinformatics; cancer; cellular biophysics; enzymes; genetics; learning (artificial intelligence); medical computing; patient treatment; support vector machines; tumours; SVM; activation status prediction; cancer driver mutations; cancer treatment; disease treatment; kinase domain mutations; kinase misregulation; machine learning predictions; machine learning technique; support vector machines; Accuracy; Bioinformatics; Genomics; Support vector machines;
Conference_Titel :
In Silico Oncology and Cancer Investigation (IARWISOCI), 2014 6th International Advanced Research Workshop on
DOI :
10.1109/IARWISOCI.2014.7034632
