Interdiscip Sci Comput Life Sci (2013) 5: 165–166 DOI: 10.1007/s12539-013-0175-8
Editorial: Special Issue on Computational Approaches for Extracting Knowledge from Biological Networks 1
Pietro Hiram Guzzi1 , Young-Rae Cho2 (Bioinformatics Laboratory, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy, [email protected]) 2 (Department of Computer Science, Baylor University, Waco, TX 76798, USA, Young-Rae [email protected])
The study of small biomolecular components, such as genes, proteins and non-coding nucleic acids (miRNAs and nc-RNAs), is now focusing on a “system-level” paradigm of their analysis. In such a paradigm, we have faced a huge amount of data sources to produce different kind of molecular information, multiple types of disseminated databases storing the results, and hence computational efforts to mine the data. The final results of this scenario are introduced by novel algorithms which are able to explicate both complex machinaries, such as pathways and modules, and single molecule functions in a focus and zoom fashion. The accomplishment of these tasks has been recognized in two main aspects. From a wet lab perspective, we have to mention the availability of relatively low cost and high-throughput technologies, such as Mass Spectrometry, Yeast-to-Hybrid and Microarrays. From an in silico lab perspective, we have to recall the advances of computational models and technologies which manage, store and mine the resultant genome-wide data. The commonly accepted computational techniques for modeling and analysis the genome-wide data are generally based on graph theory applicable to biomolecular networks. Typical examples include protein-protein interaction networks, gene regulatory networks and metabolic networks. A wide-range of computational methods, in particular, graph-theoretic approaches might be applied for effective analysis of the large-scale, complex biomolecular networks. Recently, the integration of semantics (information from Gene Ontology annotation data) has been demonstrated to be useful and biologically sound for advanced system-level analysis of biomolecules. Thus, there is an increasing tendency to integrate Gene Ontology for managing and mining the biomolecular data (as shown in Guzzi et al. and Price et al.). The articles in this special issue were selected from two workshops, 5th International Workshop on Biomolecular Network Analysis (IWBNA) and 1st Biological Network Analysis and Applications in Translational and Personalized Medicine (BNA-M), which were held in conjuction with ACM BCB 2012 and IEEE BIBM 2012, respectively. These articles were widely extended based on the discussions during the workshops. The two workshops aimed at elucidating the trend and contributions of current research and presenting novel directions for more accurate analysis, for instance, the integration of semantics or other biological resources. We briefly recall the main contributions of this special issue: Yao et al. propose a novel algorithm for prediction of protein complexes from genome-wide protein-protein interactions. This method detects a set of potential protein complexes by measuring relevancy between subgraphs. Roznovat and Ruskin present a graph-based model for the study of epigenetic of colon cancer. This model is based on genetic and epigenetic events observed at different stages of colon cancer, with a focus on the gene relationships and tumour pathways. Guzzi et al. introduce a semantic-based data repository, called OntoPIN. They show the effectiveness of managing and querying protein-protein interaction data based on ontology information.
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Interdiscip Sci Comput Life Sci (2013) 5: 165–166
Price et al. discuss the integration of semantics with protein-protein interaction. They give a wide survey of previous approaches in predicting protein complexes, and investigate the accuracy improvement by semantic integration. Alroobi et al. present the integration of multiple data sources into a single comprehesive network model. Their method focuses on the integration of protein-protein interactions and gene profiles across multiple genomes. Vizza et al. present the integration of different tools devoted to the analysis of neuroradiological images that may constitute a preliminar contribute to the development of network based tools for analysis of brain. Finally, Sarica et al. discuss the generation of questionaries that support biological and clinical research for collecting and integrating clinical and biological data. The realization of this special issue is to be credited to the contribution of many people. First of all, we would thank Editor-in-Chief and the editorial board of Interdisciplinary Sciences: Computational Life Science. We also thank the co-chairs and the program committee of two workshops, 5th IWBNA and 1st BNA-M.
Editorial: Special Issue on Computational Approaches for Extracting Knowledge from Biological Networks 1
Pietro Hiram Guzzi1 , Young-Rae Cho2 (Bioinformatics Laboratory, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy, [email protected]) 2 (Department of Computer Science, Baylor University, Waco, TX 76798, USA, Young-Rae [email protected])
The study of small biomolecular components, such as genes, proteins and non-coding nucleic acids (miRNAs and nc-RNAs), is now focusing on a “system-level” paradigm of their analysis. In such a paradigm, we have faced a huge amount of data sources to produce different kind of molecular information, multiple types of disseminated databases storing the results, and hence computational efforts to mine the data. The final results of this scenario are introduced by novel algorithms which are able to explicate both complex machinaries, such as pathways and modules, and single molecule functions in a focus and zoom fashion. The accomplishment of these tasks has been recognized in two main aspects. From a wet lab perspective, we have to mention the availability of relatively low cost and high-throughput technologies, such as Mass Spectrometry, Yeast-to-Hybrid and Microarrays. From an in silico lab perspective, we have to recall the advances of computational models and technologies which manage, store and mine the resultant genome-wide data. The commonly accepted computational techniques for modeling and analysis the genome-wide data are generally based on graph theory applicable to biomolecular networks. Typical examples include protein-protein interaction networks, gene regulatory networks and metabolic networks. A wide-range of computational methods, in particular, graph-theoretic approaches might be applied for effective analysis of the large-scale, complex biomolecular networks. Recently, the integration of semantics (information from Gene Ontology annotation data) has been demonstrated to be useful and biologically sound for advanced system-level analysis of biomolecules. Thus, there is an increasing tendency to integrate Gene Ontology for managing and mining the biomolecular data (as shown in Guzzi et al. and Price et al.). The articles in this special issue were selected from two workshops, 5th International Workshop on Biomolecular Network Analysis (IWBNA) and 1st Biological Network Analysis and Applications in Translational and Personalized Medicine (BNA-M), which were held in conjuction with ACM BCB 2012 and IEEE BIBM 2012, respectively. These articles were widely extended based on the discussions during the workshops. The two workshops aimed at elucidating the trend and contributions of current research and presenting novel directions for more accurate analysis, for instance, the integration of semantics or other biological resources. We briefly recall the main contributions of this special issue: Yao et al. propose a novel algorithm for prediction of protein complexes from genome-wide protein-protein interactions. This method detects a set of potential protein complexes by measuring relevancy between subgraphs. Roznovat and Ruskin present a graph-based model for the study of epigenetic of colon cancer. This model is based on genetic and epigenetic events observed at different stages of colon cancer, with a focus on the gene relationships and tumour pathways. Guzzi et al. introduce a semantic-based data repository, called OntoPIN. They show the effectiveness of managing and querying protein-protein interaction data based on ontology information.
166
Interdiscip Sci Comput Life Sci (2013) 5: 165–166
Price et al. discuss the integration of semantics with protein-protein interaction. They give a wide survey of previous approaches in predicting protein complexes, and investigate the accuracy improvement by semantic integration. Alroobi et al. present the integration of multiple data sources into a single comprehesive network model. Their method focuses on the integration of protein-protein interactions and gene profiles across multiple genomes. Vizza et al. present the integration of different tools devoted to the analysis of neuroradiological images that may constitute a preliminar contribute to the development of network based tools for analysis of brain. Finally, Sarica et al. discuss the generation of questionaries that support biological and clinical research for collecting and integrating clinical and biological data. The realization of this special issue is to be credited to the contribution of many people. First of all, we would thank Editor-in-Chief and the editorial board of Interdisciplinary Sciences: Computational Life Science. We also thank the co-chairs and the program committee of two workshops, 5th IWBNA and 1st BNA-M.
