Search PubMed⌕ Search

Biomedical subjects

Satoru Miyano

Publications and source records attributed to Satoru Miyano.

48 records · Page 3Linked to original sources
PubMed2003

Genomic Object Net: I. A platform for modelling and simulating biopathways.

Genomic Object Net (GON) 1.0 is a software package for creating models and simulations of biopathways. Its core architecture employs the notion of a hybrid functional Petri net with extension (HFPNe). HFPNe can seamlessly handle discrete and continuous objects and events while keeping the model components themselves simple. With the feature and graphical model editor, biopathways can be modelled intuitively and simulated on GON. The subsequent output of the simulation results can be evaluated in customised views on GON Visualizer by writing an XML file. Additionally, GON provides a tool to transform biopathway models in KEGG and BioCyc to the GON XML files for modelling and simulation. The tool avoids a lot of tedious work by users, enabling them to focus on the biological model.

Cell Physiological Phenomena↗
PubMed2003

Finding optimal gene networks using biological constraints.

The accurate estimation of gene networks from gene expression measurements is a major challenge in the field of Bioinformatics. Since the problem of estimating gene networks is NP-hard and exhibits a search space of super-exponential size, researchers are using heuristic algorithms for this task. However, little can be said about the accuracy of heuristic estimations. In order to overcome this problem, we present a general approach to reduce the search space to a biologically meaningful subspace and to find optimal solutions within the subspace in linear time. We show the effectiveness of this approach in application to yeast and Bacillus subtilis data.

Algorithms↗
PubMed2003

Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks.

We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Unfortunately, microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.

Bayes Theorem↗
PubMed2002

Association between single-nucleotide polymorphisms in selectin genes and immunoglobulin A nephropathy.

Although intensive efforts have been undertaken to elucidate the genetic background of immunoglobulin A nephropathy (IgAN), genetic factors associated with the pathogenesis of this disease are still not well understood. We designed a case-control association study that was based on linkage disequilibrium among single-nucleotide polymorphisms (SNPs) in the selectin gene cluster on chromosome 1q24-25, and we found two SNPs in the E-selectin gene (SELE8 and SELE13) and six SNPs in the L-selectin gene (SELL1, SELL4, SELL5, SELL6, SELL10, and SELL11) that were significantly associated with IgAN in Japanese patients. All eight SNPs were in almost complete linkage disequilibrium. SELE8 and SELL10 caused amino acid substitutions from His to Tyr and from Pro to Ser (chi2=9.02, P=.0026, odds ratio = 2.73 [95% confidence interval [CI] 1.38--5.38] for His-to-Tyr substitutions; chi2=17.4, P=.000031, odds ratio = 3.61 [95% CI 1.91--6.83] for Pro-to-Ser substitutions), and SELL1 could affect promoter activity of the L-selectin gene (chi2=19.5, P=.000010, odds ratio = 3.77 [95% CI 2.02--7.05]). The TGT haplotype at these three loci was associated significantly with IgAN (chi2=18.67, P=.000016, odds ratio = 1.88 [95% CI 1.41--2.51]). Our results suggest that these eight SNPs in selectin genes may be useful for screening populations susceptible to the IgAN phenotype that involves interstitial infiltration.

Case-Control Studies↗
PubMed2002

Single-nucleotide polymorphisms in the class II region of the major histocompatibility complex in Japanese patients with immunoglobulin A nephropathy.

Immunoglobulin A nephropathy (IgAN) is a form of chronic glomerulonephritis of unknown etiology and pathogenesis. Immunogenetic studies have not conclusively indicated that human leukocyte antigen (HLA) is involved. As a first step in investigating a possible relationship between HLA class II genes and IgAN, we analyzed the extent of linkage disequilibrium (LD) in this region of chromosome 6p21.3 in a Japanese test population and found extended LD blocks within the class II locus. We designed a case-control association study of single-nucleotide polymorphisms (SNPs) in each of those LD blocks, and determined that SNPs located in the HLA- DRA gene were significantly associated with an increased risk of IgAN ( P = 0.000001, odds ratio = 1.91 [95% confidence interval 1.46-2.49]); SNPs in other LD blocks were not. Our data imply that some haplotype of the HLA- DRA locus has an important role in the development of IgAN in Japanese patients.

Adult↗
PubMed2002

Extensive feature detection of N-terminal protein sorting signals.

MOTIVATION: The prediction of localization sites of various proteins is an important and challenging problem in the field of molecular biology. TargetP, by Emanuelsson et al. (J. Mol. Biol., 300, 1005-1016, 2000) is a neural network based system which is currently the best predictor in the literature for N-terminal sorting signals. One drawback of neural networks, however, is that it is generally difficult to understand and interpret how and why they make such predictions. In this paper, we aim to generate simple and interpretable rules as predictors, and still achieve a practical prediction accuracy. We adopt an approach which consists of an extensive search for simple rules and various attributes which is partially guided by human intuition. RESULTS: We have succeeded in finding rules whose prediction accuracies come close to that of TargetP, while still retaining a very simple and interpretable form. We also discuss and interpret the discovered rules.

Amino Acid Sequence↗
PubMed2002

Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression.

We propose a new method for constructing genetic network from gene expression data by using Bayesian networks. We use nonparametric regression for capturing nonlinear relationships between genes and derive a new criterion for choosing the network in general situations. In a theoretical sense, our proposed theory and methodology include previous methods based on Bayes approach. We applied the proposed method to the S. cerevisiae cell cycle data and showed the effectiveness of our method by comparing with previous methods.

Bayes Theorem↗
PubMed2002

A string pattern regression algorithm and its application to pattern discovery in long introns.

We present a new approach to pattern discovery called string pattern regression, where we are given a data set that consists of a string attribute and an objective numerical attribute. The problem is to find the best string pattern that divides the data set in such a way that the distribution of the numerical attribute values of the set for which the pattern matches the string attribute, is most distinct, with respect to some appropriate measure, from the distribution of the numerical attribute values of the set for which the pattern does not match the string attribute. By solving this problem, we are able to discover, at the same time, a subset of the data whose objective numerical attributes are significantly different from rest of the data, as well as the splitting rule in the form of a string pattern that is conserved in the subset. Although the problem can be solved in linear time for the substring pattern class, the problem is NP-hard in the general case (i.e. more complex patterns), and we present an exact but efficient branch-and-bound algorithm which is applicable to various pattern classes. We apply our algorithm to intron sequences of human, mouse, fly, and zebrafish, and show the practicality of our approach and algorithm. We also discuss possible extensions of our algorithm, as well as promising applications, such as microarray gene expression data.

Algorithms↗
PubMed2002

Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.

We propose a new statistical method for constructing genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.

Artificial Intelligence↗
PubMedDate not supplied

An O(N2) algorithm for discovering optimal Boolean pattern pairs.

We consider the problem of finding the optimal combination of string patterns, which characterizes a given set of strings that have a numeric attribute value assigned to each string. Pattern combinations are scored based on the correlation between their occurrences in the strings and the numeric attribute values. The aim is to find the combination of patterns which is best with respect to an appropriate scoring function. We present an O(N2) time algorithm for finding the optimal pair of substring patterns combined with Boolean functions, where N is the total length of the sequences. The algorithm looks for all possible Boolean combinations of the patterns, e.g., patterns of the form p and not q, which indicates that the pattern pair is considered to occur in a given string s, if p occurs in s, AND q does NOT occur in s. An efficient implementation using suffix arrays is presented, and we further show that the algorithm can be adapted to find the best k-pattern Boolean combination in O(Nk) time. The algorithm is applied to mRNA sequence data sets of moderate size combined with their turnover rates for the purpose of finding regulatory elements that cooperate, complement, or compete with each other in enhancing and/or silencing mRNA decay.

3' Untranslated Regions↗