コンピュータサイエンスとシステム生物学のジャーナル

Many applications of clustering require the use of normalized data, such as text data or mass spectra mining data. The K –Means Clustering Algorithm is one of the most widely used clustering algorithm which works on greedy approach. Major problems with the traditional K mean clustering is generation of empty clusters and more computations required to make the group of clusters. To overcome this problem we proposed an Algorithm namely Entropy Based Means Clustering Algorithm. The proposed Algorithm produces normalized cluster centers, hence highly useful for text data or massive data. The proposed algorithm shows better performance when compared with traditional K Mean Clustering Algorithm in mining data in terms of reducing time, seed predications and avoiding Empty Clusters.

The knowledge of the molecular basis of carcinogenesis has helped to discover new, less toxic chemotherapy agents. At present, considerable attention has been focused on identifying the molecular level interactions of naturally occurring Terpene based substances, capable of inhibiting target enzymes. CDKs enzymes are known as cell regulators in eukaryotic cell cycle. In finding new anti-cancer agents, CDKs are used as target enzymes, particular among them are CDK2 enzymes. Computer based Chem-office and Autodock molecular modeling tools used to understand the ways with which Terpene based natural products interacts with Cyclin-dependent kinase 2 (CDK2). Using in-silico techniques, the binding energy between ligands and receptor enzyme are calculated in the form of ΔG in Kcal.mol-1. The reported binding energies for series of molecules are ranging from -7.96 to -16.62 Kcal.mol-1. The negative docking energies and a few hydrogen bonds between ligand and receptor enzyme support the affinity of Terpene based compounds with selected enzyme. Number of hydroxyl groups present in ligand enhances the interaction strength and stability of complex. The finding confirms the affinity of Terpene based natural products as CDK2 inhibitor.

Studying mutations in promoter sequences has revolutionized molecular genetics by taking into account changes in the sequence which facilitate functional changes. Our knowledge of such changes can be furthered by tracking these changes as they occur after each base pair mutation. Although these mutations cannot be repeated or directly observed throughout molecular evolution, they can be modeled to give a feel of the dynamics of how regulatory elements are formed through time. This article presents PromMute, the graphical promoter mutation simulation, designed to model the appearance of transcription factor binding sites in promoters through single base pair mutations. It is capable of tracking the formation of a number of transcription factor binding sites, either from yeast, or supplied by the user through a number of generations applying natural selection. The program is compared to existing programs such as ev and PPE (Probability of Promoter Evolution). Different kinds of sample test simulations were done with the program, including studying the number of generations needed for the appearance of a given motif due to random mutations as well as the dynamics of motif turnover. PromMute is capable of modelling the transcription factor binding sites and scoring them more realistically. The sample test cases presented in the article show that longer transcription factor binding sites take a longer time to form, and that such motifs are also more prone to deformation by back mutations. The program is also useful for researchers who wish to study motif turnover of their own specified motifs.