The Experts below are selected from a list of 273333 Experts worldwide ranked by ideXlab platform
Rui Jiang - One of the best experts on this subject based on the ideXlab platform.
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Prioritizing Protein Complexes implicated in human diseases by network optimization.
BMC systems biology, 2014Co-Authors: Yong Chen, Thibault Jacquemin, Shuyan Zhang, Rui JiangAbstract:The detection of associations between Protein Complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a Protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease Proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related Protein Complexes. We propose a method, MAXCOM, for the prioritization of candidate Protein Complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate Protein Complexes through a heterogeneous network that is constructed by combining Protein-Protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 Protein Complexes show that MAXCOM can rank 382 (70.87%) Protein Complexes at the top against Protein Complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze Protein Complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer. MAXCOM is an effective method for the discovery of disease-related Protein Complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple Proteins.
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Prioritizing Protein Complexes implicated in human diseases by network optimization
BMC Systems Biology, 2014Co-Authors: Yong Chen, Thibault Jacquemin, Shuyan Zhang, Rui JiangAbstract:Background: The detection of associations between Protein Complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a Protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease Proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related Protein Complexes. Results: We propose a method, MAXCOM, for the prioritization of candidate Protein Complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate Protein Complexes through a heterogeneous network that is constructed by combining Protein-Protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 Protein Complexes show that MAXCOM can rank 382 (70.87%) Protein Complexes at the top against Protein Complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze Protein Complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer. Conclusions: MAXCOM is an effective method for the discovery of disease-related Protein Complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple Proteins.
Xiujuan Lei - One of the best experts on this subject based on the ideXlab platform.
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detecting overlapping Protein Complexes in weighted ppi network based on overlay network chain in quotient space
BMC Bioinformatics, 2019Co-Authors: Jie Zhao, Xiujuan LeiAbstract:Protein Complexes are the cornerstones of many biological processes and gather them to form various types of molecular machinery that perform a vast array of biological functions. In fact, a Protein may belong to multiple Protein Complexes. Most existing Protein complex detection algorithms cannot reflect overlapping Protein Complexes. To solve this problem, a novel overlapping Protein Complexes identification algorithm is proposed. In this paper, a new clustering algorithm based on overlay network chain in quotient space, marked as ONCQS, was proposed to detect overlapping Protein Complexes in weighted PPI networks. In the quotient space, a multilevel overlay network is constructed by using the maximal complete subgraph to mine overlapping Protein Complexes. The GO annotation data is used to weight the PPI network. According to the compatibility relation, the overlay network chain in quotient space was calculated. The Protein Complexes are contained in the last level of the overlay network. The experiments were carried out on four PPI databases, and compared ONCQS with five other state-of-the-art methods in the identification of Protein Complexes. We have applied ONCQS to four PPI databases DIP, Gavin, Krogan and MIPS, the results show that it is superior to other five existing algorithms MCODE, MCL, CORE, ClusterONE and COACH in detecting overlapping Protein Complexes.
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mining overlapping Protein Complexes in ppi network based on granular computation in quotient space
International Conference on Intelligent Computing, 2018Co-Authors: Jie Zhao, Xiujuan LeiAbstract:Proteins Complexes play a critical role in many biological processes. The existing Protein complex detection algorithms are mostly cannot reflect the overlapping Protein Complexes. In this paper, a novel algorithm is proposed to detect overlapping Protein Complexes based on granular computation in quotient space. Firstly, problems are expressed by quotient space and different quotient space embodies the quotient set of different granular. Then the method estimates the relationship between particles to make up for the inadequacy of data in combination with the PPI data and Gene Ontology data, deals with the network based on quotient space theory. Graining the network to construct the quotient space and merging the particles layer by layer. The final Protein Complexes is obtained after purification. The experimental results on Saccharomyces cerevisiae and Homo sapiens turned out that the proposed method could exploit Protein Complexes more accurately and efficiently.
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topology potential based seed growth method to identify Protein Complexes on dynamic ppi data
Information Sciences, 2018Co-Authors: Xiujuan Lei, Yuchen Zhang, Shi Cheng, Witold PedryczAbstract:Protein Complexes are very important for investigating the characteristics of biological processes. Identifying Protein Complexes from ProteinProtein interaction (PPI) networks is one of the recent research endeavors. The critical step of the seed-growth algorithms used for identifying Protein Complexes from PPI networks is to detect seed nodes (Proteins) from which Protein Complexes are growing up in PPI networks. Topology potential was proposed to understand the evolution behavior and organizational principles of complex networks such as PPI networks. Furthermore, PPI networks are inherently dynamic in nature. In this study, we proposed a new seed-growing algorithm (called TP-WDPIN) for identifying Protein Complexes, which employs the concept of topology potential to detect significant Proteins and mine Protein Complexes from Weighted Dynamic PPI Networks. To investigate the performance of the method, the TP-WDPIN algorithm was applied to four PPI databases and compared the obtained results to those produced by six other competing algorithms. Experimental results have demonstrated that the proposed TP-WDPIN algorithm exhibits better performance than other methods such as MCODE, MCL, CORE, CSO, ClusterONE, COACH when experimenting with four PPI databases (DIP, Krogan, MIPS, Gavin).
Yong Chen - One of the best experts on this subject based on the ideXlab platform.
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Prioritizing Protein Complexes implicated in human diseases by network optimization.
BMC systems biology, 2014Co-Authors: Yong Chen, Thibault Jacquemin, Shuyan Zhang, Rui JiangAbstract:The detection of associations between Protein Complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a Protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease Proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related Protein Complexes. We propose a method, MAXCOM, for the prioritization of candidate Protein Complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate Protein Complexes through a heterogeneous network that is constructed by combining Protein-Protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 Protein Complexes show that MAXCOM can rank 382 (70.87%) Protein Complexes at the top against Protein Complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze Protein Complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer. MAXCOM is an effective method for the discovery of disease-related Protein Complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple Proteins.
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Prioritizing Protein Complexes implicated in human diseases by network optimization
BMC Systems Biology, 2014Co-Authors: Yong Chen, Thibault Jacquemin, Shuyan Zhang, Rui JiangAbstract:Background: The detection of associations between Protein Complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a Protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease Proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related Protein Complexes. Results: We propose a method, MAXCOM, for the prioritization of candidate Protein Complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate Protein Complexes through a heterogeneous network that is constructed by combining Protein-Protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 Protein Complexes show that MAXCOM can rank 382 (70.87%) Protein Complexes at the top against Protein Complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze Protein Complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer. Conclusions: MAXCOM is an effective method for the discovery of disease-related Protein Complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple Proteins.
John Markwell - One of the best experts on this subject based on the ideXlab platform.
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SOLUBILIZATION OF PHOTOSYNTHETIC PIGMENT-Protein Complexes
Photochemistry and Photobiology, 1991Co-Authors: Leonard Allgood, Robert D. Curtright, John MarkwellAbstract:Studies utilizing fractionation of photosynthetic pigment-Protein Complexes from the chloroplast thylakoid membrane often employ dodecylsulfate at a concentration of 10 mg mL−1 to disrupt membrane structure prior to electrophoretic fractionation of the Complexes. We investigated the effect of varying dodecylsulfate concentration on the solution/air interfacial surface tension in the absence and presence of the same concentrations of thylakoid membranes used by four different fractionation systems that have been commonly employed to fractionate photosynthetic pigment-Protein Complexes. Concentrations of dodecylsulfate in the range5–10 mg mL−1, normally utilized to treat thylakoids prior to fractionation, were effective in reducing the interfacial surface tension to levels equivalent to control solutions without added thylakoid membranes. However, thylakoid membranes treated with these concentrations of dodecylsulfate are not resolved into discrete pigment-Protein Complexes when subjected to electrophoresis on an agarose gel, and do not produce significant amounts of pigment-containing Complexes with a molecular size < 100 000 as measured by filtration with size-exclusion membranes. We conclude that many surfactant systems empirically developed to fractionate photosynthetic pigment-Protein Complexes may not fully solubilize the Complexes prior to the electrophoretic step.
Keiryn L Bennett - One of the best experts on this subject based on the ideXlab platform.
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systematic identification of Protein Complexes in saccharomyces cerevisiae by mass spectrometry
Nature, 2002Co-Authors: Yuen Ho, Albrecht Gruhler, Adrian Heilbut, Gary D Bader, Lynda Moore, Sallylin Adams, Anna Millar, Paul J Taylor, Keiryn L BennettAbstract:Systematic identification of Protein Complexes in Saccharomyces cerevisiae by mass spectrometry
