E. Inoue, S. Murakami, T. Fujiki, Takuya Yoshihiro, Atsushi Takemoto, Haruka Ikegami, Kazuya Matsumoto, Masaru Nakagawa
: In this study, we propose a new method to predict three-way interactions among proteins based on corre- lation coe ffi cient of protein expression profiles. Although three-way interactions have not been studied well, this kind of interactions are important to understand the system of life. Previous studies reported the three-way interactions that based on switching mechanisms, in which a property or an expression level of a protein switches the mechanism of interactions between other two proteins. In this paper, we proposed a new method to predict three-way interactions based on the model in which A and B work together to e ff ect on the expression level of C . We present the algorithm to predict the combinations of three proteins that have the three-way interaction, and evaluate it using our real proteome data.
{"title":"Predicting Three-way Interactions of Proteins from Expression Profiles Based on Correlation Coefficient","authors":"E. Inoue, S. Murakami, T. Fujiki, Takuya Yoshihiro, Atsushi Takemoto, Haruka Ikegami, Kazuya Matsumoto, Masaru Nakagawa","doi":"10.2197/IPSJTBIO.5.34","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.5.34","url":null,"abstract":": In this study, we propose a new method to predict three-way interactions among proteins based on corre- lation coe ffi cient of protein expression profiles. Although three-way interactions have not been studied well, this kind of interactions are important to understand the system of life. Previous studies reported the three-way interactions that based on switching mechanisms, in which a property or an expression level of a protein switches the mechanism of interactions between other two proteins. In this paper, we proposed a new method to predict three-way interactions based on the model in which A and B work together to e ff ect on the expression level of C . We present the algorithm to predict the combinations of three proteins that have the three-way interaction, and evaluate it using our real proteome data.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.5.34","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The functional sites of multidomain proteins are often found at the interfaces of two or more domains. Therefore, the spatial arrangement of the domains is essential in understanding the functional mechanisms of multidomain proteins. However, an experimental determination of the whole structure of a multidomain protein is often difficult due to flexibility in inter-domain arrangement. We have developed a score function, named DINE, to detect probable docking poses generated in a rigid-body docking simulation. This score function takes into account the binding energy, information about the domain interfaces of homologous proteins, and the end-to-end distance spanned by the domain linker. We have examined the performance of DINE on 55 non-redundant known structures of two-domain proteins. In the results, the near-native docking poses were scored within the top 10 in 65.5% of the test cases. DINE scored the near-native poses higher in comparison with an existing domain assembly method, which also used binding energy and linker distance restraints. The results demonstrate that the domain-interface restraints of DINE are quite efficient in selecting near-native domain assemblies.
{"title":"DINE: A Novel Score Function for Modeling Multidomain Protein Structures with Domain Linker and Interface Restraints","authors":"Satoru Hirako, M. Shionyu","doi":"10.2197/IPSJTBIO.5.18","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.5.18","url":null,"abstract":"The functional sites of multidomain proteins are often found at the interfaces of two or more domains. Therefore, the spatial arrangement of the domains is essential in understanding the functional mechanisms of multidomain proteins. However, an experimental determination of the whole structure of a multidomain protein is often difficult due to flexibility in inter-domain arrangement. We have developed a score function, named DINE, to detect probable docking poses generated in a rigid-body docking simulation. This score function takes into account the binding energy, information about the domain interfaces of homologous proteins, and the end-to-end distance spanned by the domain linker. We have examined the performance of DINE on 55 non-redundant known structures of two-domain proteins. In the results, the near-native docking poses were scored within the top 10 in 65.5% of the test cases. DINE scored the near-native poses higher in comparison with an existing domain assembly method, which also used binding energy and linker distance restraints. The results demonstrate that the domain-interface restraints of DINE are quite efficient in selecting near-native domain assemblies.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.5.18","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dysregulation of epigenetic mechanisms has been implicated in the pathogenesis of Alzheimer's disease (AD). It has been shown that epigenetic status in promoter regions can alter levels of gene expressions, but their influence on correlated expressions of genes and its dependency on the disease are unclear. Using publicly available microarray and DNA methylation data, this article infer how correlation in gene expression in non-demented (ND) and AD brain may be influenced by genomic promoter methylation. Pearson correlation coefficients of gene expression levels between each of 123 known hypomethylated genes and all other genes in the microarray dataset were calculated, and the mean absolute coefficients were obtained as an overall strength of gene expression correlation of the hypomethylated gene. The distribution of the mean absolute coefficients showed that the hypomethylated genes can be divided into two, by the mean coefficients above or below 0.15. The division of the hypomethylated genes by the mean coefficients was more evident in AD brain than in ND brain. On the other hand, hypermethylated genes had a single dominant group, and the majority of them had the mean coefficient below 0.15. These results suggest that the lower the DNA methylation, the higher the correlation of gene expression levels with the other genes in microarray data. The strength of gene expression correlation was also calculated between known AD risk genes and all other genes in microarray data. It was found that AD risk genes were more likely to have the mean absolute correlation coefficients above 0.15 in AD brain, when the evidence for their association with AD was strong, suggesting the link between DNA methylation and AD. In conclusion DNA methylation status is intimately associated with correlated gene expression, particularly in AD brain.
{"title":"Analysis of Correlation between Gene Expression and Aberrant Epigenetic Status in Alzheimer's Disease Brain","authors":"K. Yano","doi":"10.2197/IPSJTBIO.5.2","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.5.2","url":null,"abstract":"Dysregulation of epigenetic mechanisms has been implicated in the pathogenesis of Alzheimer's disease (AD). It has been shown that epigenetic status in promoter regions can alter levels of gene expressions, but their influence on correlated expressions of genes and its dependency on the disease are unclear. Using publicly available microarray and DNA methylation data, this article infer how correlation in gene expression in non-demented (ND) and AD brain may be influenced by genomic promoter methylation. Pearson correlation coefficients of gene expression levels between each of 123 known hypomethylated genes and all other genes in the microarray dataset were calculated, and the mean absolute coefficients were obtained as an overall strength of gene expression correlation of the hypomethylated gene. The distribution of the mean absolute coefficients showed that the hypomethylated genes can be divided into two, by the mean coefficients above or below 0.15. The division of the hypomethylated genes by the mean coefficients was more evident in AD brain than in ND brain. On the other hand, hypermethylated genes had a single dominant group, and the majority of them had the mean coefficient below 0.15. These results suggest that the lower the DNA methylation, the higher the correlation of gene expression levels with the other genes in microarray data. The strength of gene expression correlation was also calculated between known AD risk genes and all other genes in microarray data. It was found that AD risk genes were more likely to have the mean absolute correlation coefficients above 0.15 in AD brain, when the evidence for their association with AD was strong, suggesting the link between DNA methylation and AD. In conclusion DNA methylation status is intimately associated with correlated gene expression, particularly in AD brain.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.5.2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
De Novo ligand design is an automatic fragment-based design of molecules within a protein binding site of a known structure. A Bayesian Optimization Algorithm (BOA), a meta-heuristic algorithm, is introduced to join predocked fragments with a user-supplied list of fragments. A novel feature proposed is the simultaneous optimization of force field energy and a term enforcing 3D-overlap to known binding mode(s). The performance of the algorithm is tested on Liver X receptors (LXRs) using a library of about 14, 000 fragments and the binding mode of a known heterocyclic phenyl acetic acid to bias the design. We further introduce the use of GPU (Graphics Processing Unit) to overcome the excessive time required in evaluating each possible fragment combination. We show how the GPU utilization enables experimenting larger fragment sets and target receptors for more complex instances. The results show how the nVidia's Tesla C2050 GPU was utilized to enable the generation of complex agonists effectively. In fact, eight of the 1, 809 molecules designed for LXRs are found in the ZINC database of commercially available compounds.
De Novo配体设计是一种在已知结构的蛋白质结合位点内自动进行基于片段的分子设计。引入了一种元启发式算法——贝叶斯优化算法(BOA)，将预先停靠的片段与用户提供的片段列表连接起来。提出的一个新特征是同时优化力场能量和一项强制3d重叠到已知的结合模式。算法的性能在肝脏X受体(LXRs)上进行了测试，使用了大约14000个片段的文库和已知杂环苯乙酸的结合模式来偏倚设计。我们进一步介绍了GPU(图形处理单元)的使用，以克服评估每个可能的碎片组合所需的过多时间。我们展示了GPU利用率如何能够为更复杂的实例实验更大的片段集和目标受体。结果显示了如何利用nVidia的Tesla C2050 GPU有效地生成复杂的激动剂。事实上，1809种为LXRs设计的分子中有8种是在ZINC的商业化合物数据库中发现的。
{"title":"A GPU Accelerated Fragment-based De Novo Ligand Design by a Bayesian Optimization Algorithm","authors":"M. Wahib, Asim Munawar, M. Munetomo, K. Akama","doi":"10.2197/IPSJTBIO.5.7","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.5.7","url":null,"abstract":"De Novo ligand design is an automatic fragment-based design of molecules within a protein binding site of a known structure. A Bayesian Optimization Algorithm (BOA), a meta-heuristic algorithm, is introduced to join predocked fragments with a user-supplied list of fragments. A novel feature proposed is the simultaneous optimization of force field energy and a term enforcing 3D-overlap to known binding mode(s). The performance of the algorithm is tested on Liver X receptors (LXRs) using a library of about 14, 000 fragments and the binding mode of a known heterocyclic phenyl acetic acid to bias the design. We further introduce the use of GPU (Graphics Processing Unit) to overcome the excessive time required in evaluating each possible fragment combination. We show how the GPU utilization enables experimenting larger fragment sets and target receptors for more complex instances. The results show how the nVidia's Tesla C2050 GPU was utilized to enable the generation of complex agonists effectively. In fact, eight of the 1, 809 molecules designed for LXRs are found in the ZINC database of commercially available compounds.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.5.7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Shinozaki, Toshinao Iwaki, Shiqiao Du, M. Sekijima, S. Furui
Three-dimensional structure prediction of a molecule can be modeled as a minimum energy search problem in a potential landscape. Popular ab initio structure prediction approaches based on this formalization are the Monte Carlo methods represented by the Metropolis method. However, their prediction performance degrades for larger molecules such as proteins since the search space is exponential to the number of atoms. In order to search the exponential space more efficiently, we propose a new method modeling the potential landscape as a factor graph. The key ideas are slicing the factor graph based on the maximum distance of bonded atoms to convert it to a linear structured graph, and the utilization of the max-sum search algorithm combined with samplings. It is referred to as Slice Chain Max-Sum and it has an advantage that the search is efficient because the graph is linear. Experiments are performed using polypeptides having 50 to 300 amino acid residues. It has been shown that the proposed method is computationally more efficient than the Metropolis method for large molecules.
{"title":"Distance-based Factor Graph Linearization and Sampled Max-sum Algorithm for Efficient 3D Potential Decoding of Macromolecules","authors":"T. Shinozaki, Toshinao Iwaki, Shiqiao Du, M. Sekijima, S. Furui","doi":"10.2197/IPSJTBIO.4.34","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.4.34","url":null,"abstract":"Three-dimensional structure prediction of a molecule can be modeled as a minimum energy search problem in a potential landscape. Popular ab initio structure prediction approaches based on this formalization are the Monte Carlo methods represented by the Metropolis method. However, their prediction performance degrades for larger molecules such as proteins since the search space is exponential to the number of atoms. In order to search the exponential space more efficiently, we propose a new method modeling the potential landscape as a factor graph. The key ideas are slicing the factor graph based on the maximum distance of bonded atoms to convert it to a linear structured graph, and the utilization of the max-sum search algorithm combined with samplings. It is referred to as Slice Chain Max-Sum and it has an advantage that the search is efficient because the graph is linear. Experiments are performed using polypeptides having 50 to 300 amino acid residues. It has been shown that the proposed method is computationally more efficient than the Metropolis method for large molecules.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.4.34","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68501989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accelerated by the technological advances in the biomedical domain, the size of its literature has been growing very rapidly. As a consequence, it is not feasible for individual researchers to comprehend and synthesize all the information related to their interests. Therefore, it is conceivable to discover hidden knowledge, or hypotheses, by linking fragments of information independently described in the literature. In fact, such hypotheses have been reported in the literature mining community; some of which have even been corroborated by experiments. This paper mainly focuses on hypothesis ranking and investigates an approach to identifying reasonable ones based on semantic similarities between events which lead to respective hypotheses. Our assumption is that hypotheses generated from semantically similar events are more reasonable. We developed a prototype system called, Hypothesis Explorer, and conducted evaluative experiments through which the validity of our approach is demonstrated in comparison with those based on term frequencies, often adopted in the previous work.
{"title":"Hypothesis Ranking Based on Semantic Event Similarities","authors":"Taiki Miyanishi, Kazuhiro Seki, K. Uehara","doi":"10.2197/IPSJTBIO.4.9","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.4.9","url":null,"abstract":"Accelerated by the technological advances in the biomedical domain, the size of its literature has been growing very rapidly. As a consequence, it is not feasible for individual researchers to comprehend and synthesize all the information related to their interests. Therefore, it is conceivable to discover hidden knowledge, or hypotheses, by linking fragments of information independently described in the literature. In fact, such hypotheses have been reported in the literature mining community; some of which have even been corroborated by experiments. This paper mainly focuses on hypothesis ranking and investigates an approach to identifying reasonable ones based on semantic similarities between events which lead to respective hypotheses. Our assumption is that hypotheses generated from semantically similar events are more reasonable. We developed a prototype system called, Hypothesis Explorer, and conducted evaluative experiments through which the validity of our approach is demonstrated in comparison with those based on term frequencies, often adopted in the previous work.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.4.9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The importance of non-coding RNAs and their informatics tools has grown for a decade due to a drastic increase of known non-coding RNAs. RNA sequence alignment is one of the most important technologies in such informatics tools. Recently, we have proposed a multi-objective genetic algorithm, Cofolga2mo, for obtaining an approximate set of weak Pareto optimal solutions for global pairwise RNA sequence alignment, where a sequence similarity and a secondary structure contribution are taken into account as objective functions. In the present study, we have developed a web server for obtaining RNA sequence alignments by Cofolga2mo and for assisting the decision making from the alignments. Furthermore, we introduced an index for reducing the number of alignments output by Cofolga2mo. As a result, we successfully reduced the maximum number of alignments for an input RNA sequence pair from fifty to ten without a significant loss of accurate alignments. By using the BRAliBase 2.1 benchmark dataset, we show that a set of alignments output by Cofolga2mo for an input RNA sequence pair, which has at most ten alignments, includes an accurate alignment compared to those of the previous mono-objective RNA sequence alignment programs.
{"title":"A Web Server for Multi-objective Pairwise RNA Sequence Alignment with an Index for Selecting Accurate Alignments","authors":"A. Taneda","doi":"10.2197/IPSJTBIO.4.2","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.4.2","url":null,"abstract":"The importance of non-coding RNAs and their informatics tools has grown for a decade due to a drastic increase of known non-coding RNAs. RNA sequence alignment is one of the most important technologies in such informatics tools. Recently, we have proposed a multi-objective genetic algorithm, Cofolga2mo, for obtaining an approximate set of weak Pareto optimal solutions for global pairwise RNA sequence alignment, where a sequence similarity and a secondary structure contribution are taken into account as objective functions. In the present study, we have developed a web server for obtaining RNA sequence alignments by Cofolga2mo and for assisting the decision making from the alignments. Furthermore, we introduced an index for reducing the number of alignments output by Cofolga2mo. As a result, we successfully reduced the maximum number of alignments for an input RNA sequence pair from fifty to ten without a significant loss of accurate alignments. By using the BRAliBase 2.1 benchmark dataset, we show that a set of alignments output by Cofolga2mo for an input RNA sequence pair, which has at most ten alignments, includes an accurate alignment compared to those of the previous mono-objective RNA sequence alignment programs.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.4.2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Yoshida, Kinji Kimura, Naoki Yoshida, Junko Tanaka, Y. Miwa
We sometimes meet an experiment in which its rate constants cannot be determined in this experiment only; in this case, it is called an underdetermined experiment. One of methods to overcome underdetermination is to combine results of multiple experiments. Multiple experiments give rise to a large number of parameters and variables to analyze, and usually even have a complicated solution with multiple solutions, which situation is unknown to us beforehand. These two difficulties: underdetermination and multiple solutions, lead to confusion as to whether rate constants can intrinsically be determined through experiment or not. In order to analyze such experiments, we use ‘prime ideal decomposition’ to decompose a solution into simpler solutions. It is, however, hard to decompose a set of polynomials with a large number of parameters and variables. Exemplifying a bio-imaging problem, we propose one tip and one technique using ‘resultant’ from a biological viewpoint.
{"title":"Algebraic Approaches to Underdetermined Experiments in Biology","authors":"H. Yoshida, Kinji Kimura, Naoki Yoshida, Junko Tanaka, Y. Miwa","doi":"10.2197/IPSJTBIO.3.62","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.3.62","url":null,"abstract":"We sometimes meet an experiment in which its rate constants cannot be determined in this experiment only; in this case, it is called an underdetermined experiment. One of methods to overcome underdetermination is to combine results of multiple experiments. Multiple experiments give rise to a large number of parameters and variables to analyze, and usually even have a complicated solution with multiple solutions, which situation is unknown to us beforehand. These two difficulties: underdetermination and multiple solutions, lead to confusion as to whether rate constants can intrinsically be determined through experiment or not. In order to analyze such experiments, we use ‘prime ideal decomposition’ to decompose a solution into simpler solutions. It is, however, hard to decompose a set of polynomials with a large number of parameters and variables. Exemplifying a bio-imaging problem, we propose one tip and one technique using ‘resultant’ from a biological viewpoint.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.3.62","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Array CGH is a useful technology for detecting copy number aberrations in genome-wide scale. We study the problem of detecting differentially aberrant genomic regions in two or more groups of CGH arrays and estimating the statistical significance of those regions. An important property of array CGH data is that there are spatial correlations among probes, and we need to take this fact into consideration when we develop a computational algorithm for array CGH data analysis. In this paper we first discuss three difficult issues underlying this problem, and then introduce nearest-neighbor multivariate test in order to alleviate these difficulties. Our proposed approach has three advantages. First, it can incorporate the spatial correlation among probes. Second, genomic regions with different sizes can be analyzed in a common ground. And finally, the computational cost can be considerably reduced with the use of a simple trick. We demonstrate the effectiveness of our approach through an application to previously published array CGH data set on 75 malignant lymphoma patients.
{"title":"Differentially Aberrant Region Detection in Array CGH Data Based on Nearest Neighbor Classification Performance","authors":"Yuta Ishikawa, I. Takeuchi","doi":"10.2197/IPSJTBIO.3.70","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.3.70","url":null,"abstract":"Array CGH is a useful technology for detecting copy number aberrations in genome-wide scale. We study the problem of detecting differentially aberrant genomic regions in two or more groups of CGH arrays and estimating the statistical significance of those regions. An important property of array CGH data is that there are spatial correlations among probes, and we need to take this fact into consideration when we develop a computational algorithm for array CGH data analysis. In this paper we first discuss three difficult issues underlying this problem, and then introduce nearest-neighbor multivariate test in order to alleviate these difficulties. Our proposed approach has three advantages. First, it can incorporate the spatial correlation among probes. Second, genomic regions with different sizes can be analyzed in a common ground. And finally, the computational cost can be considerably reduced with the use of a simple trick. We demonstrate the effectiveness of our approach through an application to previously published array CGH data set on 75 malignant lymphoma patients.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.3.70","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Kitamura, Tomomi Kimiwada, J. Maruyama, T. Kaburagi, Takashi Matsumoto, K. Wada
A Monte Carlo based algorithm is proposed to predict gene regulatory network structure of mouse nuclear receptor superfamily, about which little is known although those genes are believed to be related with several difficult diseases. The gene expression data is regarded as sample vector trajectories from a stochastic dynamical system on a graph. The problem is formulated within a Bayesian framework where the graph prior distribution is assumed to follow a Zipf distribution. Appropriateness of a graph is evaluated by the graph posterior mean. The algorithm is implemented with the Exchange Monte Carlo method. After validation against synthesized data, an attempt is made to use the algorithm for predicting network structure of the target, the mouse nuclear receptor superfamily. Several remarks are made on the feasibility of the predicted network from a biological viewpoint.
{"title":"Monte Carlo-based Mouse Nuclear Receptor Superfamily Gene Regulatory Network Prediction: Stochastic Dynamical System on Graph with Zipf Prior","authors":"Y. Kitamura, Tomomi Kimiwada, J. Maruyama, T. Kaburagi, Takashi Matsumoto, K. Wada","doi":"10.2197/IPSJTBIO.3.24","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.3.24","url":null,"abstract":"A Monte Carlo based algorithm is proposed to predict gene regulatory network structure of mouse nuclear receptor superfamily, about which little is known although those genes are believed to be related with several difficult diseases. The gene expression data is regarded as sample vector trajectories from a stochastic dynamical system on a graph. The problem is formulated within a Bayesian framework where the graph prior distribution is assumed to follow a Zipf distribution. Appropriateness of a graph is evaluated by the graph posterior mean. The algorithm is implemented with the Exchange Monte Carlo method. After validation against synthesized data, an attempt is made to use the algorithm for predicting network structure of the target, the mouse nuclear receptor superfamily. Several remarks are made on the feasibility of the predicted network from a biological viewpoint.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.3.24","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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