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Veronica J Vieland - One of the best experts on this subject based on the ideXlab platform.
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Exploiting Gene × Gene Interaction in linkage analysis
BMC Proceedings, 2007Co-Authors: Yungui Huang, Christopher W Bartlett, Alberto M. Segre, Jeffrey R O'connell, Lavonne A Mangin, Veronica J VielandAbstract:When two Genes interact to cause a clinically important phenotype, it would seem reasonable to expect that we could leverage genotypic information at one of the loci in order to improve our ability to detect the other. We were therefore interested in extending the posterior probability of linkage (PPL), a class of linkage statistics we have been developing over the past decade, in order to explicitly allow for Gene × Gene Interaction. In this report we utilize a new implementation of the PPL incorporating liability classes (LCs), which provide a direct parameterization of Gene × Gene Interaction by allowing the penetrances at the locus being evaluated to depend upon measured genotypes at a known locus. With knowledge of the Generating model for the simulated rheumatoid arthritis (RA) data, we selected two loci for examination: Locus A, which in Interaction with the HLA-DR antigen locus affects risk of the dichotomous RA phenotype; and Locus E, which in Interaction with DR affects quantitative levels of the anti-CCP phenotype. The data comprised nuclear families of two parents and an affected sib pair (ASP). Our results confirm theoretical work suggesting that Gene × Gene Interactions CANNOT be leveraged to improve linkage detection for dichotomous traits based on affecteds-only data structures. However, incorporation of DR-based LCs did lead to appreciably higher quantitative trait PPLs. This suggests that Gene × Gene Interactions could be effectively used in quantitative trait analyses even when families have been ascertained as ASPs for a related dichotomous trait.
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Exploiting Gene x Gene Interaction in linkage analysis.
BMC proceedings, 2007Co-Authors: Yungui Huang, Christopher W Bartlett, Alberto M. Segre, Jeffrey R O'connell, Lavonne Mangin, Veronica J VielandAbstract:When two Genes interact to cause a clinically important phenotype, it would seem reasonable to expect that we could leverage genotypic information at one of the loci in order to improve our ability to detect the other. We were therefore interested in extending the posterior probability of linkage (PPL), a class of linkage statistics we have been developing over the past decade, in order to explicitly allow for Gene x Gene Interaction. In this report we utilize a new implementation of the PPL incorporating liability classes (LCs), which provide a direct parameterization of Gene x Gene Interaction by allowing the penetrances at the locus being evaluated to depend upon measured genotypes at a known locus. With knowledge of the Generating model for the simulated rheumatoid arthritis (RA) data, we selected two loci for examination: Locus A, which in Interaction with the HLA-DR antigen locus affects risk of the dichotomous RA phenotype; and Locus E, which in Interaction with DR affects quantitative levels of the anti-CCP phenotype. The data comprised nuclear families of two parents and an affected sib pair (ASP). Our results confirm theoretical work suggesting that Gene x Gene Interactions CANNOT be leveraged to improve linkage detection for dichotomous traits based on affecteds-only data structures. However, incorporation of DR-based LCs did lead to appreciably higher quantitative trait PPLs. This suggests that Gene x Gene Interactions could be effectively used in quantitative trait analyses even when families have been ascertained as ASPs for a related dichotomous trait.
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Discussing Gene-Gene Interaction: warning--translating equations to English may result in jabberwocky.
Genetic epidemiology, 2007Co-Authors: Christopher W Bartlett, Veronica J Vieland, Jacquelaine Bartlett, Jordana T Bell, Samsiddhi Bhattacharjee, Françoise Clerget-darpoux, William S Bush, Todd L Edwards, Guimin Gao, Indrani HalderAbstract:Interest in mapping susceptibility alleles for complex diseases, which do not follow a classic single-Gene segregation pattern, has driven interest in methods that account for, or use information from one locus when mapping another. Our discussion group examined methods related to epistasis or Gene x Gene Interaction. The goal of modeling Gene x Gene Interaction varied across groups; some papers tried to detect Gene x Gene Interaction while others tried to exploit it to map Genes. Most of the 10 papers summarized here applied newly created or newly modified statistical methods related to Gene x Gene Interaction, while two groups primarily examined computational issues. As is often the case, comparisons are complicated by little overlap in the data used across the papers, and further complicated by the fact that the available data may not have been ideal for some Gene x Gene Interaction methods. However, the main difficulty in comparing and contrasting methods across the papers is the lack of a consistent statistical definition of Gene x Gene Interaction. But despite these issues, two clear trends emerged across the analyses: First, the methods for quantitative trait Gene x Gene Interaction appeared to perform very well, even in families initially ascertained as affected sib pairs; and second, dichotomous trait Gene x Gene Interaction methods failed to produce consistent results. The difficulty of using (primarily) affected sib pair data in a Gene x Gene Interaction analysis is explored.
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Discussing Gene-Gene Interaction: warning--translating equations to English may result in jabberwocky.
Genetic Epidemiology, 2007Co-Authors: Christopher W Bartlett, Veronica J VielandAbstract:Interest in mapping susceptibility alleles for complex diseases, which do not follow a classic single-Gene segregation pattern, has driven interest in methods that account for, or use information from one locus when mapping another. Our discussion group examined methods related to epistasis or Gene × Gene Interaction. The goal of modeling Gene × Gene Interaction varied across groups; some papers tried to detect Gene × Gene Interaction while others tried to exploit it to map Genes. Most of the 10 papers summarized here applied newly created or newly modified statistical methods related to Gene × Gene Interaction, while two groups primarily examined computational issues. As is often the case, comparisons are complicated by little overlap in the data used across the papers, and further complicated by the fact that the available data may not have been ideal for some Gene × Gene Interaction methods. However, the main difficulty in comparing and contrasting methods across the papers is the lack of a consistent statistical definition of Gene × Gene Interaction. But despite these issues, two clear trends emerged across the analyses: First, the methods for quantitative trait Gene × Gene Interaction appeared to perform very well, even in families initially ascertained as affected sib pairs; and second, dichotomous trait Gene × Gene Interaction methods failed to produce consistent results. The difficulty of using (primarily) affected sib pair data in a Gene × Gene Interaction analysis is explored. Genet. Epidemiol. 31(Suppl. 1)S61–S67, 2007. © 2007 Wiley-Liss, Inc.
Christopher W Bartlett - One of the best experts on this subject based on the ideXlab platform.
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Exploiting Gene × Gene Interaction in linkage analysis
BMC Proceedings, 2007Co-Authors: Yungui Huang, Christopher W Bartlett, Alberto M. Segre, Jeffrey R O'connell, Lavonne A Mangin, Veronica J VielandAbstract:When two Genes interact to cause a clinically important phenotype, it would seem reasonable to expect that we could leverage genotypic information at one of the loci in order to improve our ability to detect the other. We were therefore interested in extending the posterior probability of linkage (PPL), a class of linkage statistics we have been developing over the past decade, in order to explicitly allow for Gene × Gene Interaction. In this report we utilize a new implementation of the PPL incorporating liability classes (LCs), which provide a direct parameterization of Gene × Gene Interaction by allowing the penetrances at the locus being evaluated to depend upon measured genotypes at a known locus. With knowledge of the Generating model for the simulated rheumatoid arthritis (RA) data, we selected two loci for examination: Locus A, which in Interaction with the HLA-DR antigen locus affects risk of the dichotomous RA phenotype; and Locus E, which in Interaction with DR affects quantitative levels of the anti-CCP phenotype. The data comprised nuclear families of two parents and an affected sib pair (ASP). Our results confirm theoretical work suggesting that Gene × Gene Interactions CANNOT be leveraged to improve linkage detection for dichotomous traits based on affecteds-only data structures. However, incorporation of DR-based LCs did lead to appreciably higher quantitative trait PPLs. This suggests that Gene × Gene Interactions could be effectively used in quantitative trait analyses even when families have been ascertained as ASPs for a related dichotomous trait.
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Exploiting Gene x Gene Interaction in linkage analysis.
BMC proceedings, 2007Co-Authors: Yungui Huang, Christopher W Bartlett, Alberto M. Segre, Jeffrey R O'connell, Lavonne Mangin, Veronica J VielandAbstract:When two Genes interact to cause a clinically important phenotype, it would seem reasonable to expect that we could leverage genotypic information at one of the loci in order to improve our ability to detect the other. We were therefore interested in extending the posterior probability of linkage (PPL), a class of linkage statistics we have been developing over the past decade, in order to explicitly allow for Gene x Gene Interaction. In this report we utilize a new implementation of the PPL incorporating liability classes (LCs), which provide a direct parameterization of Gene x Gene Interaction by allowing the penetrances at the locus being evaluated to depend upon measured genotypes at a known locus. With knowledge of the Generating model for the simulated rheumatoid arthritis (RA) data, we selected two loci for examination: Locus A, which in Interaction with the HLA-DR antigen locus affects risk of the dichotomous RA phenotype; and Locus E, which in Interaction with DR affects quantitative levels of the anti-CCP phenotype. The data comprised nuclear families of two parents and an affected sib pair (ASP). Our results confirm theoretical work suggesting that Gene x Gene Interactions CANNOT be leveraged to improve linkage detection for dichotomous traits based on affecteds-only data structures. However, incorporation of DR-based LCs did lead to appreciably higher quantitative trait PPLs. This suggests that Gene x Gene Interactions could be effectively used in quantitative trait analyses even when families have been ascertained as ASPs for a related dichotomous trait.
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Discussing Gene-Gene Interaction: warning--translating equations to English may result in jabberwocky.
Genetic epidemiology, 2007Co-Authors: Christopher W Bartlett, Veronica J Vieland, Jacquelaine Bartlett, Jordana T Bell, Samsiddhi Bhattacharjee, Françoise Clerget-darpoux, William S Bush, Todd L Edwards, Guimin Gao, Indrani HalderAbstract:Interest in mapping susceptibility alleles for complex diseases, which do not follow a classic single-Gene segregation pattern, has driven interest in methods that account for, or use information from one locus when mapping another. Our discussion group examined methods related to epistasis or Gene x Gene Interaction. The goal of modeling Gene x Gene Interaction varied across groups; some papers tried to detect Gene x Gene Interaction while others tried to exploit it to map Genes. Most of the 10 papers summarized here applied newly created or newly modified statistical methods related to Gene x Gene Interaction, while two groups primarily examined computational issues. As is often the case, comparisons are complicated by little overlap in the data used across the papers, and further complicated by the fact that the available data may not have been ideal for some Gene x Gene Interaction methods. However, the main difficulty in comparing and contrasting methods across the papers is the lack of a consistent statistical definition of Gene x Gene Interaction. But despite these issues, two clear trends emerged across the analyses: First, the methods for quantitative trait Gene x Gene Interaction appeared to perform very well, even in families initially ascertained as affected sib pairs; and second, dichotomous trait Gene x Gene Interaction methods failed to produce consistent results. The difficulty of using (primarily) affected sib pair data in a Gene x Gene Interaction analysis is explored.
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Discussing Gene-Gene Interaction: warning--translating equations to English may result in jabberwocky.
Genetic Epidemiology, 2007Co-Authors: Christopher W Bartlett, Veronica J VielandAbstract:Interest in mapping susceptibility alleles for complex diseases, which do not follow a classic single-Gene segregation pattern, has driven interest in methods that account for, or use information from one locus when mapping another. Our discussion group examined methods related to epistasis or Gene × Gene Interaction. The goal of modeling Gene × Gene Interaction varied across groups; some papers tried to detect Gene × Gene Interaction while others tried to exploit it to map Genes. Most of the 10 papers summarized here applied newly created or newly modified statistical methods related to Gene × Gene Interaction, while two groups primarily examined computational issues. As is often the case, comparisons are complicated by little overlap in the data used across the papers, and further complicated by the fact that the available data may not have been ideal for some Gene × Gene Interaction methods. However, the main difficulty in comparing and contrasting methods across the papers is the lack of a consistent statistical definition of Gene × Gene Interaction. But despite these issues, two clear trends emerged across the analyses: First, the methods for quantitative trait Gene × Gene Interaction appeared to perform very well, even in families initially ascertained as affected sib pairs; and second, dichotomous trait Gene × Gene Interaction methods failed to produce consistent results. The difficulty of using (primarily) affected sib pair data in a Gene × Gene Interaction analysis is explored. Genet. Epidemiol. 31(Suppl. 1)S61–S67, 2007. © 2007 Wiley-Liss, Inc.
Subhasis Mukhopadhyay - One of the best experts on this subject based on the ideXlab platform.
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PReMI - Cross-Correlation and Evolutionary Biclustering: Extracting Gene Interaction Sub-networks
Lecture Notes in Computer Science, 2009Co-Authors: Ranajit Das, Sushmita Mitra, Subhasis MukhopadhyayAbstract:In this paper we present a simple and novel time-dependent cross-correlation-based approach for the extraction of simple Gene Interaction sub-networks from biclusters in temporal Gene expression microarray data. Preprocessing has been employed to retain those Gene Interaction pairs that are strongly correlated. The methodology was applied to public-domain data sets of Yeast and the experimental results were biologically validated based on standard databases and information available in the literature.
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Gene Interaction - An evolutionary biclustering approach
Information Fusion, 2009Co-Authors: Sushmita Mitra, Ranajit Das, Haider Banka, Subhasis MukhopadhyayAbstract:DNA Microarray experiments form a powerful tool for studying Gene expression patterns, in large scale. Sharing of the regulatory mechanism among Genes, in an organism, is predominantly responsible for their co-expression. Biclustering aims at finding a subset of similarly expressed Genes under a subset of experimental conditions. A small number of Genes participate in a cellular process of interest. Again, a Gene may be simultaneously involved in a number of cellular processes. In cellular environment, Genes interact among themselves to produce enzymes, metabolites, proteins, etc. responsible for a particular function(s). In this study, a simple and novel correlation-based approach is proposed to extract Gene Interaction networks from biclusters in microarray data. Local search strategy is employed to add (remove) relevant (irrelevant) Genes for finer tuning, in multi-objective biclustering framework. Preprocessing is done to preserve strongly correlated Gene Interaction pairs. Experimental results on time-series Gene expression data from Yeast are biologically validated using benchmark databases and literature.
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PReMI - Evolutionary biclustering with correlation for Gene Interaction networks
Lecture Notes in Computer Science, 1Co-Authors: Ranajit Das, Sushmita Mitra, Haider Banka, Subhasis MukhopadhyayAbstract:In this study, a novel rank correlation-based multiobjective evolutionary biclustering method is proposed to extract simple Gene Interaction networks from microarray data. Preprocessing helps to preserve those Gene Interaction pairs which are strongly correlated. Experimental results on time series Gene expression data from Yeast are biologically validated based on standard databases and information from literature.
Arzucan Ozgur - One of the best experts on this subject based on the ideXlab platform.
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Literature Mining and Ontology based Analysis of Host-Brucella Gene-Gene Interaction Network.
Frontiers in microbiology, 2015Co-Authors: İlknur Karadeniz, Junguk Hur, Arzucan OzgurAbstract:Brucella is an intracellular bacterium that causes chronic brucellosis in humans and various mammals. The identification of host-Brucella Interaction is crucial to understand host immunity against Brucella infection and Brucella pathoGenesis against host immune responses. Most of the information about the inter-species Interactions between host and Brucella Genes is only available in the text of the scientific publications. Many text-mining systems for extracting Gene and protein Interactions have been proposed. However, only a few of them have been designed by considering the peculiarities of host-pathogen Interactions. In this paper, we used a text mining approach for extracting host-Brucella Gene-Gene Interactions from the abstracts of articles in PubMed. The Gene-Gene Interactions here represent the Interactions between Genes and/or Gene products (e.g., proteins). The SciMiner tool, originally designed for detecting mammalian Gene/protein names in text, was extended to identify host and Brucella Gene/protein names in the abstracts. Next, sentence-level and abstract-level co-occurrence based approaches, as well as sentence-level machine learning based methods, originally designed for extracting intra-species Gene Interactions, were utilized to extract the Interactions among the identified host and Brucella Genes. The extracted Interactions were manually evaluated. A total of 46 host-Brucella Gene Interactions were identified and represented as an Interaction network. Twenty four of these Interactions were identified from sentence-level processing. Twenty two additional Interactions were identified when abstract-level processing was performed. The Interaction Network Ontology (INO) was used to represent the identified Interaction types at a hierarchical ontology structure. Ontological modeling of specific Gene-Gene Interactions demonstrates that host-pathogen Gene-Gene Interactions occur at experimental conditions which can be ontologically represented. Our results show that the introduced literature mining and ontology-based modeling approach are effective in retrieving and analyzing host-pathogen Gene-Gene Interaction networks.
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identifying Gene disease associations using centrality on a literature mined Gene Interaction network
Intelligent Systems in Molecular Biology, 2008Co-Authors: Arzucan Ozgur, Gunes Erkan, Dragomir R RadevAbstract:Motivation: Understanding the role of Genetics in diseases is one of the most important aims of the biological sciences. The completion of the Human Genome Project has led to a rapid increase in the number of publications in this area. However, the coverage of curated databases that provide information manually extracted from the literature is limited. Another challenge is that determining disease-related Genes requires laborious experiments. Therefore, predicting good candidate Genes before experimental analysis will save time and effort. We introduce an automatic approach based on text mining and network analysis to predict Gene-disease associations. We collected an initial set of known disease-related Genes and built an Interaction network by automatic literature mining based on dependency parsing and support vector machines. Our hypothesis is that the central Genes in this disease-specific network are likely to be related to the disease. We used the degree, eigenvector, betweenness and closeness centrality metrics to rank the Genes in the network. Results: The proposed approach can be used to extract known and to infer unknown Gene-disease associations. We evaluated the approach for prostate cancer. Eigenvector and degree centrality achieved high accuracy. A total of 95% of the top 20 Genes ranked by these methods are confirmed to be related to prostate cancer. On the other hand, betweenness and closeness centrality predicted more Genes whose relation to the disease is currently unknown and are candidates for experimental study. Availability: A web-based system for browsing the disease-specific Gene-Interaction networks is available at: http://gin.ncibi.org Contact: [email protected]
Ranajit Das - One of the best experts on this subject based on the ideXlab platform.
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PReMI - Cross-Correlation and Evolutionary Biclustering: Extracting Gene Interaction Sub-networks
Lecture Notes in Computer Science, 2009Co-Authors: Ranajit Das, Sushmita Mitra, Subhasis MukhopadhyayAbstract:In this paper we present a simple and novel time-dependent cross-correlation-based approach for the extraction of simple Gene Interaction sub-networks from biclusters in temporal Gene expression microarray data. Preprocessing has been employed to retain those Gene Interaction pairs that are strongly correlated. The methodology was applied to public-domain data sets of Yeast and the experimental results were biologically validated based on standard databases and information available in the literature.
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Gene Interaction - An evolutionary biclustering approach
Information Fusion, 2009Co-Authors: Sushmita Mitra, Ranajit Das, Haider Banka, Subhasis MukhopadhyayAbstract:DNA Microarray experiments form a powerful tool for studying Gene expression patterns, in large scale. Sharing of the regulatory mechanism among Genes, in an organism, is predominantly responsible for their co-expression. Biclustering aims at finding a subset of similarly expressed Genes under a subset of experimental conditions. A small number of Genes participate in a cellular process of interest. Again, a Gene may be simultaneously involved in a number of cellular processes. In cellular environment, Genes interact among themselves to produce enzymes, metabolites, proteins, etc. responsible for a particular function(s). In this study, a simple and novel correlation-based approach is proposed to extract Gene Interaction networks from biclusters in microarray data. Local search strategy is employed to add (remove) relevant (irrelevant) Genes for finer tuning, in multi-objective biclustering framework. Preprocessing is done to preserve strongly correlated Gene Interaction pairs. Experimental results on time-series Gene expression data from Yeast are biologically validated using benchmark databases and literature.
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PReMI - Evolutionary biclustering with correlation for Gene Interaction networks
Lecture Notes in Computer Science, 1Co-Authors: Ranajit Das, Sushmita Mitra, Haider Banka, Subhasis MukhopadhyayAbstract:In this study, a novel rank correlation-based multiobjective evolutionary biclustering method is proposed to extract simple Gene Interaction networks from microarray data. Preprocessing helps to preserve those Gene Interaction pairs which are strongly correlated. Experimental results on time series Gene expression data from Yeast are biologically validated based on standard databases and information from literature.