Department of Computer Science and Engineering
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Browsing Department of Computer Science and Engineering by Author "Ahmed, Rezwan"
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Item Algorithms for Mining the Coevolving Relational Motifs in Dynamic Networks(2014-03-24) Ahmed, Rezwan; Karypis, GeorgeComputational methods and tools that can efficiently and effectively analyze the temporal changes in dynamic complex relational networks enable us to gain significant insights regarding the entity relations and their evolution. This paper introduces a new class of dynamic graph patterns, referred to as coevolving relational motifs (CRMs), which are designed to identify recurring sets of entities whose relations change in a consistent way over time. Coevolving relational motifs can provide evidence to the existence of, possibly unknown, coordination mechanisms by identifying the relational motifs that evolve in a similar and highly conserved fashion. We developed an algorithm to efficiently analyze the frequent relational changes between the entities of the dynamic networks and capture all frequent coevolutions as CRMs. Our algorithm follows a depth-first exploration of the frequent CRM lattice and incorporates canonical labeling for redundancy elimination. Experimental results based on multiple real world dynamic networks show that the method is able to efficiently identify CRMs. In addition, a qualitative analysis of the results shows that the discovered patterns can be used as features to characterize the dynamic network.Item TOPTMH: Topology Predictor for Transmembrane Alpha-Helices(2008-02-13) Ahmed, Rezwan; Rangwala, Huzefa; Karypis, GeorgeAlpha-helical transmembrane proteins mediate many key biological processes and represent 20-30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods that predict their topology (transmembrane helical segments and their orientation) are essential in advancing the understanding of membrane proteins structures and functions. We developed a new topology prediction method for transmembrane helices called TOPTMH that combines a helix residue predictor with a helix segment identification method and determines the overall orientation using the positive-inside rule. The residue predictor is built using Support Vector Machines (SVM) that utilize evolutionary information in the form of PSI-BLAST generated sequence profiles to annotate each residue by its likelihood of being part of a helix segment. The helix segment identification method is built by combining the segments predicted by two Hidden Markov Models (HMM)one based on the SVM predictions and the other based on the hydrophobicity values of the sequences amino acids. This approach combines the power of SVM-based models to discriminate between the helical and non-helical residues with the power of HMMs to identify contiguous segments of helical residues that take into account the SVM predictions and the hydrophobicity values of neighboring residues. We present empirical results on two standard datasets and show that both the per-residue (Q2) and per-segment (Qok) scores obtained by TOPTMH are higher than those achieved by well-known methods such as Phobius and MEMSAT3. In addition, on an independent static benchmark, TOPTMH achieved the highest scores on high-resolution sequences (Q2 score of 84% and Qok score of 86%) against existing state-of-the-art systems while achieving low signal peptide error.