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6. Clique-based data mining for related genes in a biomedical database

Author

Tomita Etsuji, Yonemori Chikara, Matsunaga Tsutomu, and Muramatsu Masaaki

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. There is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. Here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph. Results We constructed a graph whose nodes were gene or disease pages, and edges were the hyperlink connections between those pages in the Online Mendelian Inheritance in Man (OMIM) database. We obtained over 20,000 sets of related genes (called 'gene modules') by enumerating cliques computationally. The modules included genes in the same family, genes for proteins that form a complex, and genes for components of the same signaling pathway. The results of experiments using 'metabolic syndrome'-related gene modules show that the gene modules can be used to get a coherent holistic picture helpful for interpreting relations among genes. Conclusion We presented a data mining approach extracting related genes by enumerating cliques. The extracted gene sets provide a holistic picture useful for comprehending complex disease mechanisms.

Published
2009
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16. WALCOM: Algorithms and Computation : 9th International Workshop, WALCOM 2015, Dhaka, Bangladesh, February 26-28, 2015. Proceedings / edited by M. Sohel Rahman, Etsuji Tomita.

Author

Rahman, M. Sohel. editor., Tomita, Etsuji. editor., SpringerLink (Online service), Rahman, M. Sohel. editor., Tomita, Etsuji. editor., and SpringerLink (Online service)

Abstract

This book constitutes the thoroughly refereed conference proceedings of the 9th International Workshop on Algorithms and Computation, WALCOM 2015, held in Dhaka, Bangladesh, in February 2015. The 26 revised full papers presented together with 3 invited talks were carefully reviewed and selected from 85 submissions. The papers are organized in topical sections on approximation algorithms, data structures and algorithms, computational geometry, combinatorial algorithms, distributed and online algorithms, graph drawing and algorithms, combinatorial problems and complexity, and graph enumeration and algorithms.

Published
2015

33. Learning Boolean functions in AC0 on attribute and classification noise—Estimating an upper bound on attribute and classification noise

Author

Miyata, Akinobu, Tarui, Jun, and Tomita, Etsuji

Subjects
AC0, Attribute noise, Classification noise, Learning, Boolean functions
Abstract

We study a procedure for estimating an upper bound of an unknown noise factor in the frequency domain. A learning algorithm using a Fourier transformation method was originally given by Linial, Mansour and Nisan. While Linial, Mansour and Nisan assumed that the learning algorithm estimates Fourier coefficients from noiseless data, Bshouty, Jackson, and Tamon, and also Ohtsuki and Tomita extended the algorithm to ones that are robust for noisy data. The noise process that we consider is as follows: for an example 〈x,f(x)〉, where x∈{0,1}n,f(x)∈{−1,1}, each bit of x and f(x) gets flipped independently with probability η during a learning process. The previous learning algorithms for noisy data all assume that the noise factor η or an upper bound of η is known in advance. The learning algorithm proposed in this paper works without this assumption. We estimate an upper bound of the noise factor by evaluating a noisy power spectrum in the frequency domain and by using a sampling trick. Combining this procedure with Ohtsuki and Tomita’s algorithm, we obtain a quasi-polynomial-time learning algorithm that can cope with noise without knowing any information about the noise in advance.

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40. Multiple methods for protein side chain packing using maximum weight cliques.

Author

Brown JB, Dukka Bahadur KC, Tomita E, and Akutsu T

Subjects
Algorithms, Amino Acids chemistry, Models, Chemical, Models, Molecular, Predictive Value of Tests, Protein Conformation, Computational Biology methods, Oligopeptides chemistry, Proteins chemistry
Abstract

In this paper, we present several methods for computing a solution to the protein side chain packing problem, with all methods having a common solution approach of breaking the polymer into subpolymers and using maximum edge weight cliques to prune the search space for the optimal side chain packing. We characterize the graph sizes generated for each method and compare their prediction accuracies. These methods are demonstrated for computing proteins up to approximately 8000 residues. In addition, we update a result published previously.

Published
2006

41. Point matching under non-uniform distortions and protein side chain packing based on an efficient maximum clique algorithm.

Author

Dukka BK, Akutsu T, Tomita E, Seki T, and Fujiyama A

Subjects
Animals, Computational Biology methods, Electrophoresis, Gel, Two-Dimensional methods, Humans, Sequence Alignment methods, Algorithms, Data Interpretation, Statistical, Protein Structure, Tertiary, Sequence Analysis, Protein methods
Abstract

We developed maximum clique-based algorithms for spot matching for two-dimensional gel electrophoresis images, protein structure alignment and protein side-chain packing, where these problems are known to be NP-hard. Algorithms based on direct reductions to the maximum clique can find optimal solutions for instances of size (the number of points or residues) up to 50-150 using a standard PC. We also developed pre-processing techniques to reduce the sizes of graphs. Combined with some heuristics, many realistic instances can be solved approximately.

Published
2002

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