Detection of microRNA Clusters Associated with Prostate Cancer

MicroRNAs (miRNAs) are a class of small non- coding RNAs of 22 nucleotides which normally function as negative regulators of target mRNA expression at the posttran- scriptional level. miRNAs play a role for one or more target genes by suppressing in processes as growth, differentiation, proliferation and cell death. Recent evidence has shown that miRNA mutations or mis-expression correlate with various hu- man cancers and indicates that miRNAs can function as tumour suppressors and oncogenes. MicroRNAs have been shown to repress the expression of important cancer-related genes and might prove useful in the diagnosis and treatment of cancer. In this study, hierarchical microRNA clusters are obtained through microarray expression data in order to analyze the microRNA prostate cancer relationships. Clustering results are evaluated by their biological relevance. It is seen that such approach can be useful in detectitn relationships between microRNAs and diseases

Conformal and Contact Kinetic Dynamics and Their Geometrization

We propose a conformal generalization of the reversible Vlasov equation of kinetic plasma dynamics, called conformal kinetic theory. In order to arrive at this formalism, we start with the conformal Hamiltonian dynamics of particles and lift it to the dynamical formulation of the associated kinetic theory. The resulting theory represents a simple example of a geometric pathway from dissipative particle motion to dissipative kinetic motion. We also derive the kinetic equations of a continuum of particles governed by the contact Hamiltonian dynamics, which may be interpreted in the context of relativistic mechanics. Once again we start with the contact Hamiltonian dynamics and lift it to a kinetic theory, called contact kinetic dynamics. Finally, we project the contact kinetic theory to conformal kinetic theory so that they form a geometric hierarchy.Comment: Minor revision

Protein dizilimlerinin homoloji sezimi ve sınıflandırma amaçlı bilişimsel gösterimi.

Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by probabilistic suffix trees. New sequence representations described in the thesis are applied on three challenging problems of computational biology: remote homology detection, subcellular localization prediction, and solvent accessibility prediction, with some problem-specific modifications. Rigorous experiments are conducted on common benchmarking datasets, and a comparative analysis is performed between the new methods and the existing ones for each problem. On remote homology detection tests, all three methods achieve competitive accuracies with the state-of-the-art methods, while being much more efficient. A combination of new representations are used to devise a hybrid system, called PredLOC, for predicting subcellular localization of proteins and it is tested on two distinct eukaryotic datasets. To the best of author̕s knowledge, the accuracy achieved by PredLOC is the highest one ever reported on those datasets. The maximal unique match method is resulted with only a slight improvement in solvent accessibility predictions.Ph.D. - Doctoral Progra