The Experts below are selected from a list of 211794 Experts worldwide ranked by ideXlab platform
Oliver Eulenstein - One of the best experts on this subject based on the ideXlab platform.
-
Exploring Biological Interaction networks with tailored weighted quasi-bicliques.
BMC bioinformatics, 2012Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein Interactions, the identification of regulatory mechanisms, and other Biological tasks. Due to the experimental and Biological complexity, their computational exploitation faces many algorithmic challenges. We introduce novel weighted quasi-biclique problems to identify functional modules in Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from Biological networks.
-
Exploring Biological Interaction networks with tailored weighted quasi-bicliques
BMC Bioinformatics, 2012Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Abstract Background Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein Interactions, the identification of regulatory mechanisms, and other Biological tasks. Due to the experimental and Biological complexity, their computational exploitation faces many algorithmic challenges. Results We introduce novel weighted quasi-biclique problems to identify functional modules in Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. Conclusions We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from Biological networks.
-
ISBRA - Mining Biological Interaction networks using weighted quasi-bicliques
Bioinformatics Research and Applications, 2011Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Biological network studies can provide fundamental insights into various Biological tasks including the functional characterization of genes and their products, the characterization of DNA-protein Interactions, and the identification of regulatory mechanisms. However, Biological networks are confounded with unreliable Interactions and are incomplete, and thus, their computational exploitation is fraught with algorithmic challenges. Here we introduce quasi-biclique problems to analyze Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also provide exact IP solutions that can compute moderately sized networks. We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from the network.
Wen-chieh Chang - One of the best experts on this subject based on the ideXlab platform.
-
Exploring Biological Interaction networks with tailored weighted quasi-bicliques.
BMC bioinformatics, 2012Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein Interactions, the identification of regulatory mechanisms, and other Biological tasks. Due to the experimental and Biological complexity, their computational exploitation faces many algorithmic challenges. We introduce novel weighted quasi-biclique problems to identify functional modules in Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from Biological networks.
-
Exploring Biological Interaction networks with tailored weighted quasi-bicliques
BMC Bioinformatics, 2012Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Abstract Background Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein Interactions, the identification of regulatory mechanisms, and other Biological tasks. Due to the experimental and Biological complexity, their computational exploitation faces many algorithmic challenges. Results We introduce novel weighted quasi-biclique problems to identify functional modules in Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. Conclusions We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from Biological networks.
-
ISBRA - Mining Biological Interaction networks using weighted quasi-bicliques
Bioinformatics Research and Applications, 2011Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Biological network studies can provide fundamental insights into various Biological tasks including the functional characterization of genes and their products, the characterization of DNA-protein Interactions, and the identification of regulatory mechanisms. However, Biological networks are confounded with unreliable Interactions and are incomplete, and thus, their computational exploitation is fraught with algorithmic challenges. Here we introduce quasi-biclique problems to analyze Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also provide exact IP solutions that can compute moderately sized networks. We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from the network.
Charles Delisi - One of the best experts on this subject based on the ideXlab platform.
-
VisANT: An Online Visualization and Analysis Tool for Biological Interaction Data
BMC bioinformatics, 2004Co-Authors: Joseph C. Mellor, Charles DelisiAbstract:New techniques for determining relationships between biomolecules of all types--genes, proteins, noncoding DNA, metabolites and small molecules--are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems. We present VisANT, an application for integrating biomolecular Interaction data into a cohesive, graphical interface. This software features a multi-tiered architecture for data flexibility, separating back-end modules for data retrieval from a front-end visualization and analysis package. VisANT is a freely available, open-source tool for researchers, and offers an online interface for a large range of published data sets on biomolecular Interactions, including those entered by users. This system is integrated with standard databases for organized annotation, including GenBank, KEGG and SwissProt. VisANT is a Java-based, platform-independent tool suitable for a wide range of Biological applications, including studies of pathways, gene regulation and systems biology. VisANT has been developed to provide interactive visual mining of Biological Interaction data sets. The new software provides a general tool for mining and visualizing such data in the context of sequence, pathway, structure, and associated annotations. Interaction and predicted association data can be combined, overlaid, manipulated and analyzed using a variety of built-in functions. VisANT is available at http://visant.bu.edu.
-
visant an online visualization and analysis tool for Biological Interaction data
BMC Bioinformatics, 2004Co-Authors: Joseph C. Mellor, Charles DelisiAbstract:Background New techniques for determining relationships between biomolecules of all types – genes, proteins, noncoding DNA, metabolites and small molecules – are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems.
Roland Krause - One of the best experts on this subject based on the ideXlab platform.
-
Exploring Biological Interaction networks with tailored weighted quasi-bicliques.
BMC bioinformatics, 2012Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein Interactions, the identification of regulatory mechanisms, and other Biological tasks. Due to the experimental and Biological complexity, their computational exploitation faces many algorithmic challenges. We introduce novel weighted quasi-biclique problems to identify functional modules in Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from Biological networks.
-
Exploring Biological Interaction networks with tailored weighted quasi-bicliques
BMC Bioinformatics, 2012Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Abstract Background Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein Interactions, the identification of regulatory mechanisms, and other Biological tasks. Due to the experimental and Biological complexity, their computational exploitation faces many algorithmic challenges. Results We introduce novel weighted quasi-biclique problems to identify functional modules in Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. Conclusions We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from Biological networks.
-
ISBRA - Mining Biological Interaction networks using weighted quasi-bicliques
Bioinformatics Research and Applications, 2011Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Biological network studies can provide fundamental insights into various Biological tasks including the functional characterization of genes and their products, the characterization of DNA-protein Interactions, and the identification of regulatory mechanisms. However, Biological networks are confounded with unreliable Interactions and are incomplete, and thus, their computational exploitation is fraught with algorithmic challenges. Here we introduce quasi-biclique problems to analyze Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also provide exact IP solutions that can compute moderately sized networks. We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from the network.
Sudheer Vakati - One of the best experts on this subject based on the ideXlab platform.
-
Exploring Biological Interaction networks with tailored weighted quasi-bicliques.
BMC bioinformatics, 2012Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein Interactions, the identification of regulatory mechanisms, and other Biological tasks. Due to the experimental and Biological complexity, their computational exploitation faces many algorithmic challenges. We introduce novel weighted quasi-biclique problems to identify functional modules in Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from Biological networks.
-
Exploring Biological Interaction networks with tailored weighted quasi-bicliques
BMC Bioinformatics, 2012Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Abstract Background Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein Interactions, the identification of regulatory mechanisms, and other Biological tasks. Due to the experimental and Biological complexity, their computational exploitation faces many algorithmic challenges. Results We introduce novel weighted quasi-biclique problems to identify functional modules in Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. Conclusions We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from Biological networks.
-
ISBRA - Mining Biological Interaction networks using weighted quasi-bicliques
Bioinformatics Research and Applications, 2011Co-Authors: Wen-chieh Chang, Sudheer Vakati, Roland Krause, Oliver EulensteinAbstract:Biological network studies can provide fundamental insights into various Biological tasks including the functional characterization of genes and their products, the characterization of DNA-protein Interactions, and the identification of regulatory mechanisms. However, Biological networks are confounded with unreliable Interactions and are incomplete, and thus, their computational exploitation is fraught with algorithmic challenges. Here we introduce quasi-biclique problems to analyze Biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include Biological Interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also provide exact IP solutions that can compute moderately sized networks. We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing Interactions from the network.