Presenting a Meta-Heuristic Algorithm to Detect Regulatory Elements in the Genome of Breast Cancer Patients

Nowadays, in medical sciences, the amount of data on symptoms of people affected with various illnesses on one hand, and finding assistive techniques for the diagnosis of those diseases on the other, has been widespread. Consequently, the analysis and consideration of all factors involved in a disease are often challenging. Thus, a mechanized system to help discover the rules, identify patterns, and predict future events is absolutely needed. In this research, we intend to use a multi-objective algorithm to provide a method capable of detecting, extract sequences of variable-length from the genome, and count the interactions among them. In fact, these regulatory elements could play a significant role in the incidence and exacerbation of cancer.

In this research, a proposed method for the detection of regulatory elements in the genome of a breast cancer patient has been used. The proposed method is implemented in MATLAB software. Also, to measure the performance and effectiveness of the suggested method, the proposed algorithm is implemented on HiC dataset, regarding patients with breast cancer in two blood cells GM12878 and CD34+ introduced by Mifsud et al.

The results of implementing the proposed method are compared with the HiCUP method. The results show that the MSARE method has a better performance in detecting regulatory elements compared to the HiCUP method.

Experimental studies have shown that the two promoters BLC6 and HOTTIP discovered by the proposed method have had a significant effect on the incidence and severity of breast cancer in both blood cells GM12878 and CD34+.