Predicting DNA mutations during cancer evolution.

Bio-systems are inherently complex information processing systems. Their physiological complexities limit the formulation and testing of a hypothesis for their behaviour. Our goal here was to test a computational framework utilising published data from a longitudinal study of patients with acute myeloid leukaemia (AML), whose DNA from both normal and malignant tissues were subjected to NGS analysis at various points in time. By processing the sequencing data before relapse time, we tested our framework by predicting the regions of the genome to be mutated at relapse time and, later, by comparing our results with the actual regions that showed mutations (discovered by genome sequencing at relapse time). After a detailed statistical analysis, the resulting correlation coefficient (degree of matching of proposed framework with real data) is 0.9816 ± 0.009 at 95% confidence interval. This high performance from our proposed framework opens new research opportunities for bioinformatics researchers and clinical doctors.