Proceedings of the 2nd International Conference on Database Theory, 1988
The composition and decomposition of datalog program mappings are investigated for the purpose of... more The composition and decomposition of datalog program mappings are investigated for the purpose of optimization. Composability is studied using an algebraic axiom system. Also considered are (i) the decomposability of a subclass of linear recursive programs into single-rule programs; (ii) the structure of the class of prime programs; and the factorizability of bounded recursive programs into single-rule primes.
Selected Papers from the Fourth International Workshop on Foundations of Models and Languages For Data and Objects Modelling Database Dynamics, Oct 19, 1992
Journal of Bioinformatics and Computational Biology, Dec 1, 2005
It is commonly believed that suitable analysis of microarray gene expression profile data can lea... more It is commonly believed that suitable analysis of microarray gene expression profile data can lead to better understanding of diseases, and better ways to diagnose and treat diseases. To achieve those goals, it is of interest to discover the gene interaction networks, and perhaps even pathways, underlying given diseases from such data. In this paper, we consider methods for efficiently discovering highly differentiative gene groups (HDGG), which may provide insights on gene interaction networks. HDGGs are groups of genes which completely or nearly completely characterize the diseased or normal tissues. Discovering HDGGs is challenging, due to the high dimensionality of the data. Our methods are based on the novel concept of gene clubs. A gene club consists of a set of genes having high potential to be interactive with each other. The methods can (i) efficiently discover signature HDGGs which completely characterize the diseased and the normal tissues respectively, (ii) find strongest or near strongest HDGGs containing any given gene, and (iii) find much stronger HDGGs than previous methods. As part of the experimental evaluation, the methods are applied to colon, prostate, ovarian, and breast cancer, and leukemia and so on. Some of the genes in the extracted signature HDGGs have known biological functions, and some have attracted little attention in biology and medicine. We hope that appropriate study on them can lead to medical breakthroughs. Some HDGGs for colon and prostate cancers are listed here. The website listed below contains HDGGs for the other cancers. HDGG is implemented in C++ and runs on Unix or Windows platform. The code is available at: http://www.cs.wright.edu/~gdong/hdgg/.
Proceedings of the 2nd International Conference on Database Theory, 1988
The composition and decomposition of datalog program mappings are investigated for the purpose of... more The composition and decomposition of datalog program mappings are investigated for the purpose of optimization. Composability is studied using an algebraic axiom system. Also considered are (i) the decomposability of a subclass of linear recursive programs into single-rule programs; (ii) the structure of the class of prime programs; and the factorizability of bounded recursive programs into single-rule primes.
Selected Papers from the Fourth International Workshop on Foundations of Models and Languages For Data and Objects Modelling Database Dynamics, Oct 19, 1992
Journal of Bioinformatics and Computational Biology, Dec 1, 2005
It is commonly believed that suitable analysis of microarray gene expression profile data can lea... more It is commonly believed that suitable analysis of microarray gene expression profile data can lead to better understanding of diseases, and better ways to diagnose and treat diseases. To achieve those goals, it is of interest to discover the gene interaction networks, and perhaps even pathways, underlying given diseases from such data. In this paper, we consider methods for efficiently discovering highly differentiative gene groups (HDGG), which may provide insights on gene interaction networks. HDGGs are groups of genes which completely or nearly completely characterize the diseased or normal tissues. Discovering HDGGs is challenging, due to the high dimensionality of the data. Our methods are based on the novel concept of gene clubs. A gene club consists of a set of genes having high potential to be interactive with each other. The methods can (i) efficiently discover signature HDGGs which completely characterize the diseased and the normal tissues respectively, (ii) find strongest or near strongest HDGGs containing any given gene, and (iii) find much stronger HDGGs than previous methods. As part of the experimental evaluation, the methods are applied to colon, prostate, ovarian, and breast cancer, and leukemia and so on. Some of the genes in the extracted signature HDGGs have known biological functions, and some have attracted little attention in biology and medicine. We hope that appropriate study on them can lead to medical breakthroughs. Some HDGGs for colon and prostate cancers are listed here. The website listed below contains HDGGs for the other cancers. HDGG is implemented in C++ and runs on Unix or Windows platform. The code is available at: http://www.cs.wright.edu/~gdong/hdgg/.
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