Cancer survival classification using integrated data sets and intermediate information
Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Authors: Shinuk Kim, Taesung Park, Mark Kon Source Type: research
More News: Bioinformatics | Cancer | Cancer & Oncology | Learning | Study | Universities & Medical Training