www.ietdl.org Published in IET Systems Biology Received on 12th February 2014 Accepted on 12th February 2014 doi: 10.1049/iet-syb.2014.0005
Special Issue: Part 1: Network Biology in Translational Bioinformatics and Systems Biology ISSN 1751-8849
Editorial Part 1: Network biology in translational bioinformatics and systems biology Identifying driver mutations as well as disease genes that are responsible for various disorders is the key to understand the molecular mechanisms underlying diseases in translational bioinformatics. At the same time, the knowledge about drug targets can help design compounds with better efficacy. Recently, the availability of various types of molecular networks makes it possible to identify disease genes or drug targets from systematic perspectives. In this special issue, we reported the recent progress on computational approaches that have been developed to predict disease genes and drug targets. It is known that many diseases are caused because of certain genetic mutations. Therefore, the identification of loci that are responsible for diseases is the key to understand how the diseases initiate and develop. Liu et al. proposed a new approach in their paper entitled, “Comprehensive study of tumor SNP array data reveals significant driver aberrations and disrupted signaling pathways in human hepatocellular cancer”, to identify the significant driver aberrations as well as disrupted signaling pathways in human hepatocellular cancer, where part of their results have been validated. Wu et al. developed a novel approach to associate non-synonymous single nucleotide polymorphisms(nsSNPs) with corresponding diseases based on the integration of multiple similarity networks, in their paper, “Inferring nsSNP-disease associations via integration of multiple similarity networks”. Based on the principle of ‘guilt by association’, Zhao et al. proposed a novel approach to prioritize disease genesin the context of the molecular interactome by taking into account their expression dynamics and topological characteristics, in their paper entitled, “Degree-adjusted algorithm for prioritization of candidate disease genes from gene expression and protein interactome”. Instead of single genes, some diseases happen due to the dysreulation of certain molecular pathways. Zhang et al. developed a new method to identify those pathways related to breast cancer metastasis through the modules extracted from molecular networks in their paper, “Discovery of significant pathways in breast cancer metastasis via module extraction and comparison”.
IET Syst. Biol., 2014, Vol. 8, Iss. 2, p. 23 doi: 10.1049/iet-syb.2014.0005
Despite the disease genes providing insights into the mechanism that underlie diseases, it is challenging to translate this knowledge into therapies. On the other hand, it is straightforward to design optimal compounds with desired effects if the therapeutic targets whose inhibition can prevent the diseases are known. In their paper, “In silico identification of potential targets and drugs for non small cell lung cancer”, Ng et al. presented a systematic strategy to identify potential targets and drugs that may work for non-small cell lung cancer with the integration of various types of data, such as protein-protein interactions and protein complexes. Finally, in “MPGraph: Multi-view penalized graph clustering for predicting drug-target interactions”, Li proposed a new multi-view penalized graph model, namely MPGraph, to predict drug-protein interactions based on the gene expression profiles of the NCI-60 cell lines, and found some novel targets for 22 FDA approved drugs [6]. Xing-Ming Zhao received his Ph.D. degree from the University of Science and Technology of China in 2005. He was a postdoc fellow at the University of Tokyo during 2006-2008, and joined the Institute of Systems Biology at Shanghai University in 2008 as an Associate Professor. In 2012, he moved to the School of Electronics and Information Engineering, Tongji University. His research focuses on inference and analysis of molecular interaction networks, identification of signaling pathways, prediction of drug-protein interactions and drug combinations. He has published more than 50 journal papers, and is editorial board member of several journals.
XING-MING ZHAO Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China Email: [email protected]
23
& The Institution of Engineering and Technology 2014
Special Issue: Part 1: Network Biology in Translational Bioinformatics and Systems Biology ISSN 1751-8849
Editorial Part 1: Network biology in translational bioinformatics and systems biology Identifying driver mutations as well as disease genes that are responsible for various disorders is the key to understand the molecular mechanisms underlying diseases in translational bioinformatics. At the same time, the knowledge about drug targets can help design compounds with better efficacy. Recently, the availability of various types of molecular networks makes it possible to identify disease genes or drug targets from systematic perspectives. In this special issue, we reported the recent progress on computational approaches that have been developed to predict disease genes and drug targets. It is known that many diseases are caused because of certain genetic mutations. Therefore, the identification of loci that are responsible for diseases is the key to understand how the diseases initiate and develop. Liu et al. proposed a new approach in their paper entitled, “Comprehensive study of tumor SNP array data reveals significant driver aberrations and disrupted signaling pathways in human hepatocellular cancer”, to identify the significant driver aberrations as well as disrupted signaling pathways in human hepatocellular cancer, where part of their results have been validated. Wu et al. developed a novel approach to associate non-synonymous single nucleotide polymorphisms(nsSNPs) with corresponding diseases based on the integration of multiple similarity networks, in their paper, “Inferring nsSNP-disease associations via integration of multiple similarity networks”. Based on the principle of ‘guilt by association’, Zhao et al. proposed a novel approach to prioritize disease genesin the context of the molecular interactome by taking into account their expression dynamics and topological characteristics, in their paper entitled, “Degree-adjusted algorithm for prioritization of candidate disease genes from gene expression and protein interactome”. Instead of single genes, some diseases happen due to the dysreulation of certain molecular pathways. Zhang et al. developed a new method to identify those pathways related to breast cancer metastasis through the modules extracted from molecular networks in their paper, “Discovery of significant pathways in breast cancer metastasis via module extraction and comparison”.
IET Syst. Biol., 2014, Vol. 8, Iss. 2, p. 23 doi: 10.1049/iet-syb.2014.0005
Despite the disease genes providing insights into the mechanism that underlie diseases, it is challenging to translate this knowledge into therapies. On the other hand, it is straightforward to design optimal compounds with desired effects if the therapeutic targets whose inhibition can prevent the diseases are known. In their paper, “In silico identification of potential targets and drugs for non small cell lung cancer”, Ng et al. presented a systematic strategy to identify potential targets and drugs that may work for non-small cell lung cancer with the integration of various types of data, such as protein-protein interactions and protein complexes. Finally, in “MPGraph: Multi-view penalized graph clustering for predicting drug-target interactions”, Li proposed a new multi-view penalized graph model, namely MPGraph, to predict drug-protein interactions based on the gene expression profiles of the NCI-60 cell lines, and found some novel targets for 22 FDA approved drugs [6]. Xing-Ming Zhao received his Ph.D. degree from the University of Science and Technology of China in 2005. He was a postdoc fellow at the University of Tokyo during 2006-2008, and joined the Institute of Systems Biology at Shanghai University in 2008 as an Associate Professor. In 2012, he moved to the School of Electronics and Information Engineering, Tongji University. His research focuses on inference and analysis of molecular interaction networks, identification of signaling pathways, prediction of drug-protein interactions and drug combinations. He has published more than 50 journal papers, and is editorial board member of several journals.
XING-MING ZHAO Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China Email: [email protected]
23
& The Institution of Engineering and Technology 2014
