Our purpose is to investigate the apoptotic effect of NAV2729 in combination with Imatinib on CML cell line, K562. Inhibiting the release of TMVs, helps to inhibit leukemia. Tumor derived extracellular vesicles (TMVs) play an important role in the progression of the disease.
Imatinib as the first line treatment for CML is associated with side effects and resistant. CML therapy prevents the disease from spreading to more advanced stages and improve quality of life. After that, the algorithm is used to find clusters of a data set containing 93,000 objects, which are the centers of players' performance in about 4900 matches in different European leagues.Background: Chronic myeloid leukemia (CML) is a hematologic malignancy that affect hematopoietic stem cells. To show the effectiveness of the algorithm, it is tested on six synthetic data sets and its performance is compared with two other conventional clustering methods. In the first phase, the algorithm searches the solution space to find the number of clusters and, in the second phase, it finds the positions of the centroids.
The proposed method created using particle swarm optimization algorithm has two phases. In this article, an automatic big data clustering method, based on a swarm intelligence algorithm, is proposed to automatically cluster the data set of players' performance centers in different matches and extract different kinds of roles in football. Finding roles of players with the purpose of analyzing the performance of a team or making a meaningful comparison between players is crucial.
Analyzing football is hard because of its complexity, number of events in each match, and constant flow of circulation of the ball. So, developing methods and algorithms for analyzing team sports has become one of the most popular topics among data scientists. Recently, professional team sport organizations have invested their resources to analyze their own and opponents' performance.
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