Increasing the odds of hit identification in screening is of significance for drug discovery. The odds for finding a hit are closely related either to the diversity of libraries or to the availability of focused libraries. There are no truly diverse libraries and it is difficult to design focused libraries without sufficient information. Hence it is helpful to consider alternative approaches that can enhance the odds using existing libraries. Multiple members of a protein family have been considered collectively in inhibitor design, on the basis of the correlation between protein families and ligands derived from specific compound classes. Such a correlation has been exploited in various drug discovery studies and a general receptor-homolog-based screening scheme may be devised. The feasibility of such a scheme in enhancing the odds of hit identification is discussed. / Singapore-MIT Alliance (SMA)

Thesis (Ph. D.)--Oregon State University, 1970. / Typescript (photocopy). Includes bibliographical references (leaf 49). Also available on the World Wide Web.

The homology search problem and the gene finding problem are two fundamental problems in bioinformatics. The homology search problem is to find the homologous regions of two biological sequences; the gene finding problem is to find all the genes in both strands of a genomic sequence. Recently, gene finding research has demonstrated that homology search results can be used to improve the accuracy of gene finding. By combining the two problems, we define a new problem called the homologous gene finding problem. The homologous gene finding problem is to find homologous genes of a query gene in a target genomic sequence. Consequently, we present a new homologous gene finding algorithm in this thesis. We borrow the idea of gene mapping and alignment algorithms, and apply existing seed-based homology search algorithms and hidden Markov model-based (HMM-based) gene finding algorithms to solve the homologous gene finding problem. After we find high-scoring segment pairs (HSPs) between the query gene and the target genomic sequence, we locate target regions that we believe contain a gene homologous to the query gene. Then, we extend existing HMM-based gene finding algorithms to find homologous gene candidates. To improve the accuracy of homologous gene finding, we train a HMM to be biased toward the query gene. We also introduce a new coding sequence (CDS) length penalty as a measure of how the CDS lengths of the query gene and its homologous gene vary to further improve the accuracy. We use the new CDS length penalty together with our enhanced Viterbi algorithm and our flexible finish condition to improve the speed of homologous gene fining without harming the accuracy. Finally, we use protein alignment to pick and rank the best homologous gene candidates. In this thesis, we also describe several experiments to evaluate and support our homologous gene finding algorithm.

The homology search problem and the gene finding problem are two fundamental problems in bioinformatics. The homology search problem is to find the homologous regions of two biological sequences; the gene finding problem is to find all the genes in both strands of a genomic sequence. Recently, gene finding research has demonstrated that homology search results can be used to improve the accuracy of gene finding. By combining the two problems, we define a new problem called the homologous gene finding problem. The homologous gene finding problem is to find homologous genes of a query gene in a target genomic sequence. Consequently, we present a new homologous gene finding algorithm in this thesis. We borrow the idea of gene mapping and alignment algorithms, and apply existing seed-based homology search algorithms and hidden Markov model-based (HMM-based) gene finding algorithms to solve the homologous gene finding problem. After we find high-scoring segment pairs (HSPs) between the query gene and the target genomic sequence, we locate target regions that we believe contain a gene homologous to the query gene. Then, we extend existing HMM-based gene finding algorithms to find homologous gene candidates. To improve the accuracy of homologous gene finding, we train a HMM to be biased toward the query gene. We also introduce a new coding sequence (CDS) length penalty as a measure of how the CDS lengths of the query gene and its homologous gene vary to further improve the accuracy. We use the new CDS length penalty together with our enhanced Viterbi algorithm and our flexible finish condition to improve the speed of homologous gene fining without harming the accuracy. Finally, we use protein alignment to pick and rank the best homologous gene candidates. In this thesis, we also describe several experiments to evaluate and support our homologous gene finding algorithm.

We use computational homology to characterize the geometry of complicated time-dependent patterns. Homology provides very basic topological (geometrical) information about the patterns, such as the number of components (pieces) and the number of holes. For 3-dimensional patterns it also provides the number of voids. We apply these techniques to patterns generated by experiments on spiral defect chaos, as well as to numerically simulated patterns in the Cahn-Hilliard theory of phase separation and on spiral wave patterns in excitable media. These techniques allow us to distinguish patterns at different parameter values, to detect complicated dynamics through the computation of positive Lyapunov exponents and entropies, to compare experimental data with numerical simulations, to quantify boundary effects on finite size domains, among other things.

Thesis (M. Sc.)--University of Hong Kong, 1966. / Typewritten.

Thesis (Ph. D.)--Lehigh University, 2003. / Includes vita. Includes bibliographical references (leaves 39-41).

published_or_final_version / Mathematics / Master / Master of Philosophy

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