The sine cosine algorithm (SCA), a recently proposed population-based optimization algorithm, is based on the use of sine and cosine trigonometric functions as operators to update the movements of the search agents. To optimize performance, different parameters on the SCA must be appropriately tuned. Setting such parameters is challenging because they permit the algorithm to escape from local optima and avoid premature convergence. The main drawback of the SCA is that the parameter setting only affects the exploitation of the prominent regions. However, the SCA has good exploration capabilities. This article presents an enhanced version of the SCA by merging it with particle swarm optimization (PSO). PSO exploits the search space better than the operators of the standard SCA. The proposed algorithm, called ASCA-PSO, has been tested over several unimodal and multimodal benchmark functions, which show its superiority over the SCA and other recent and standard meta-heuristic algorithms. Moreover, to verify the capabilities of the SCA, the SCA has been used to solve the real-world problem of a pairwise local alignment algorithm that tends to find the longest consecutive substrings between two biological sequences. Experimental results provide evidence of the good performance of the ASCA-PSO solutions in terms of accuracy and computational time.

Breast cancer is a heterogeneous disease comprising the estrogen receptor (ER)-positive luminal subtype which is subdivided into luminal A and luminal B and ER-negative breast cancer which includes the triple-negative subtype. This study has four aims: 1) to examine whether Minichromosome Maintenance (MCM)2, MCM4, and MCM6 can be used as markers to differentiate between luminal A and luminal B subtypes; 2) to study whether MCM2, MCM4, and MCM6 are highly expressed in triple-negative breast cancer, as there is an urgent need to search for surrogate markers in this aggressive subtype, for drug development purposes; 3) to compare the prognostic values of these markers in predicting relapse-free survival; and 4) to compare the three approaches used for scoring the protein expression of these markers by immunohistochemistry (IHC). MCM2, MCM4, MCM6, and MKI67 mRNA expression was first studied using in silico analysis of available breast cancer datasets. We next used IHC to evaluate their protein expression on tissue microarrays using three scoring methods. MCM2, MCM4, and MCM6 can help in distinction between luminal A and luminal B whose therapeutic management and clinical outcomes are different. MCM2, MCM4, MCM6, and Ki-67 are highly expressed in breast cancer of high histological grades that comprise clinically aggressive tumors such as luminal B, HER2-positive, and triple-negative subtypes. Low transcript expression of these markers is associated with increased probability of relapse-free survival. A positive relationship exists among the three scoring methods of each of the four markers. An independent validation cohort is needed to confirm their clinical utility.