Konstantina Kourou

TL;DR: Given the growing trend on the application of ML methods in cancer research, this work presents here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

TL;DR: The results confirm the validity of the proposed framework towards the automated and fast identification of outliers, inconsistencies, and highly-correlated and duplicated terms, as well as, the successful matching of more than 85% of the pSS-related medical terms in both cohorts, yielding more accurate, relevant, and consistent clinical data.

TL;DR: This study identifies the genes that act as regulators and mediate the activity of transcription factors that have been found in all promoters of differentially expressed gene sets and identified the features that can accurately classify the samples into tumors and controls using a DBN-based classification approach.

TL;DR: In this article, the authors present the most indicative studies with respect to the ML algorithms and data used in cancer research and provide a thorough examination of the clinical scenarios with regards to disease diagnosis, patient classification and cancer prognosis and survival.

TL;DR: A methodology for predicting oral cancer recurrence using dynamic Bayesian networks that takes into consideration time series gene expression data in order to predict a disease recurrence is proposed.