Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033123
Yu Wang, Lin Gao, Zhe Chen
Characterization and identification of protein complexes in protein-protein interaction (PPI) networks is important in understanding cellular processes. With the core-attachment concept, a novel core-attachment algorithm is proposed by characterizing the protein complex core from the perspective of edges. We reinvite a protein complex core to be a set of closely interrelated edges rather than a set of interrelated proteins. We first identify the edges must belong to a core, and then partition these edges to extract cores. After that, we select the attachments for each complex core to form a protein complex. Finally, we evaluate the performance of our algorithm by applying it on two different yeast PPI networks. The experimental results show that our algorithm outperforms the MCL, CPM, CoAch in terms of number of precisely predicted protein complexes, localization as well as GO semantic similarity. Our proposed method is validated as an effective algorithm in identifying protein complexes and can provide more insights for future biological study. It proves that edge community is a better topological characterization of protein complex.
{"title":"An edge based core-attachment method to detect protein complexes in PPI networks","authors":"Yu Wang, Lin Gao, Zhe Chen","doi":"10.1109/ISB.2011.6033123","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033123","url":null,"abstract":"Characterization and identification of protein complexes in protein-protein interaction (PPI) networks is important in understanding cellular processes. With the core-attachment concept, a novel core-attachment algorithm is proposed by characterizing the protein complex core from the perspective of edges. We reinvite a protein complex core to be a set of closely interrelated edges rather than a set of interrelated proteins. We first identify the edges must belong to a core, and then partition these edges to extract cores. After that, we select the attachments for each complex core to form a protein complex. Finally, we evaluate the performance of our algorithm by applying it on two different yeast PPI networks. The experimental results show that our algorithm outperforms the MCL, CPM, CoAch in terms of number of precisely predicted protein complexes, localization as well as GO semantic similarity. Our proposed method is validated as an effective algorithm in identifying protein complexes and can provide more insights for future biological study. It proves that edge community is a better topological characterization of protein complex.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129508863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033172
Yong Wang, Qiao-feng Wu, Cheng Chen, Xian-Zhong Yan, S. Yu, Xiang-Sun Zhang, F. Liang
Identifying biomarkers for acupuncture treatment is crucial to understand the mechanism of acupuncture effect at molecular level. In this study, we investigate the metabolic profiles of acupuncture treatment on several meridian points in human. To identify the subsets of metabolites that best characterize the acupuncture effect for each meridian point, a linear programming based model is proposed to identify biomarkers from the high-dimensional metabolic data. Specifically, we use nearest centroid as prototype to simultaneously minimize the number of selected features and leave-one-out cross validation error of the classifier. As a result, we reveal novel metabolite biomarkers for acupuncture treatment. Our result demonstrates that metabolic profiling might be a promising method to investigating the molecular mechanism of acupuncture. Comparison with other existing methods shows the efficiency and effectiveness of our new method. In addition, the method proposed in this paper is general and can be used in other high-dimensional applications, such as cancer genomics.
{"title":"Identifying biomarkers for acupuncture treatment via an optimization model","authors":"Yong Wang, Qiao-feng Wu, Cheng Chen, Xian-Zhong Yan, S. Yu, Xiang-Sun Zhang, F. Liang","doi":"10.1109/ISB.2011.6033172","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033172","url":null,"abstract":"Identifying biomarkers for acupuncture treatment is crucial to understand the mechanism of acupuncture effect at molecular level. In this study, we investigate the metabolic profiles of acupuncture treatment on several meridian points in human. To identify the subsets of metabolites that best characterize the acupuncture effect for each meridian point, a linear programming based model is proposed to identify biomarkers from the high-dimensional metabolic data. Specifically, we use nearest centroid as prototype to simultaneously minimize the number of selected features and leave-one-out cross validation error of the classifier. As a result, we reveal novel metabolite biomarkers for acupuncture treatment. Our result demonstrates that metabolic profiling might be a promising method to investigating the molecular mechanism of acupuncture. Comparison with other existing methods shows the efficiency and effectiveness of our new method. In addition, the method proposed in this paper is general and can be used in other high-dimensional applications, such as cancer genomics.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126376981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033179
Rui Liu, Luonan Chen, K. Aihara
Many evidences suggested that during the progression of complex diseases, the deteriorations are generally not smooth but abrupt, which may cause a critical transition from one state to another at a tipping point, corresponding to a bifurcation of the dynamical system for the underlying organism. A pre-disease state is assumed to exist before reaching the tipping point between a normal state and a disease state. Since the predisease state is defined as a limit of the normal state, which represents an early-warning signal of the disease, it is crucial to identify such a state so that remedial actions can be executed to avoid the abrupt transition to the disease state. Although most complex diseases are model free, and usually only small samples are available due to clinical limitations, we propose that an index called the network transition entropy (NTE) may serving as an early-warning indicator for predicting the critical transition. Although the theoretical deviation is based on the dynamical network biomarker (DNB), the application of NTE is DNB free.
{"title":"The early warning signal of complex diseases based on the network transition entropy","authors":"Rui Liu, Luonan Chen, K. Aihara","doi":"10.1109/ISB.2011.6033179","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033179","url":null,"abstract":"Many evidences suggested that during the progression of complex diseases, the deteriorations are generally not smooth but abrupt, which may cause a critical transition from one state to another at a tipping point, corresponding to a bifurcation of the dynamical system for the underlying organism. A pre-disease state is assumed to exist before reaching the tipping point between a normal state and a disease state. Since the predisease state is defined as a limit of the normal state, which represents an early-warning signal of the disease, it is crucial to identify such a state so that remedial actions can be executed to avoid the abrupt transition to the disease state. Although most complex diseases are model free, and usually only small samples are available due to clinical limitations, we propose that an index called the network transition entropy (NTE) may serving as an early-warning indicator for predicting the critical transition. Although the theoretical deviation is based on the dynamical network biomarker (DNB), the application of NTE is DNB free.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128907609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033146
Liu Hong, J. Lei
A general shape equation for the local regular structure of biomolecular chains at the equilibrium state is established. It predicts a general relationship between the structural curvature and torsion, which only concerns about the elastic property of the molecular chain, and is independent of variable conformations of real biomolecules. Solutions corresponding to α-helix and β-hairpin in proteins, helical DNA, as well as spiral molecules are discussed, which show a fairly well agreement with experimental data.
{"title":"A general shape equation for local regular structure of biomolecular chains","authors":"Liu Hong, J. Lei","doi":"10.1109/ISB.2011.6033146","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033146","url":null,"abstract":"A general shape equation for the local regular structure of biomolecular chains at the equilibrium state is established. It predicts a general relationship between the structural curvature and torsion, which only concerns about the elastic property of the molecular chain, and is independent of variable conformations of real biomolecules. Solutions corresponding to α-helix and β-hairpin in proteins, helical DNA, as well as spiral molecules are discussed, which show a fairly well agreement with experimental data.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128588600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033171
X. Zhuo
Mathematical models have been used to understand the factors that govern infectious disease progression in viral infections. Two basic models of within-host viral infection, proposed by Nowak et. al. and Perelson et. al. respectively, have been widely used in the studies of HBV and HIV infections. However, the loss term of viral particles when it enters the target cells are both ignored by these two models. Leenheer and Smith provided a general virus dynamic model with the loss term of viral particles, which make the above two basic models only be special cases. But the basic reproduction numbers of all above models are proportional to the number of total cells of the host's organ prior to the infection(when used for HBV infection) or the normal target cell level(when used for HIV infection). On the other hand, the global asymptotically stable condition of the endemic equilibrium about Leenheer and Smith's model is related to the initial value of the growth function of uninfected cell. In this paper, we formulate an amended Leenheer and Smith's model with standard incidence, the basic reproduction numbers were no more dependent on the number of total cells of the host's organ. If the basic reproduction number of virus is less than one, the infection-free equilibrium is globally asymptotically stable and the virus is cleared; if the basic reproduction number is great than one, then the virus persist in the host, and solutions approach either an endemic equilibrium or a periodic orbit. The periodic orbit can be ruled out in some cases but not in general. The globally asymptotically stable condition of the endemic equilibrium is only determined by the model parameters.
{"title":"Global analysis of a general HBV infection model","authors":"X. Zhuo","doi":"10.1109/ISB.2011.6033171","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033171","url":null,"abstract":"Mathematical models have been used to understand the factors that govern infectious disease progression in viral infections. Two basic models of within-host viral infection, proposed by Nowak et. al. and Perelson et. al. respectively, have been widely used in the studies of HBV and HIV infections. However, the loss term of viral particles when it enters the target cells are both ignored by these two models. Leenheer and Smith provided a general virus dynamic model with the loss term of viral particles, which make the above two basic models only be special cases. But the basic reproduction numbers of all above models are proportional to the number of total cells of the host's organ prior to the infection(when used for HBV infection) or the normal target cell level(when used for HIV infection). On the other hand, the global asymptotically stable condition of the endemic equilibrium about Leenheer and Smith's model is related to the initial value of the growth function of uninfected cell. In this paper, we formulate an amended Leenheer and Smith's model with standard incidence, the basic reproduction numbers were no more dependent on the number of total cells of the host's organ. If the basic reproduction number of virus is less than one, the infection-free equilibrium is globally asymptotically stable and the virus is cleared; if the basic reproduction number is great than one, then the virus persist in the host, and solutions approach either an endemic equilibrium or a periodic orbit. The periodic orbit can be ruled out in some cases but not in general. The globally asymptotically stable condition of the endemic equilibrium is only determined by the model parameters.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130990981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033143
Songyot Nakariyakul, Zhiping Liu, Luonan Chen
The PDZ domain is one of the largest families of protein domains that are involved in targeting and routing specific proteins in signaling pathways. PDZ domains mediate protein-protein interactions by binding the C-terminal peptides of their target proteins. Using the dipeptide feature encoding, we develop a PDZ domain interaction predictor using a support vector machine that achieves a high accuracy rate of 82.49%. Since most of the dipeptide compositions are redundant and irrelevant, we propose a new hybrid feature selection technique to select only a subset of these compositions that are useful for interaction prediction. Our experimental results show that only approximately 25% of dipeptide features are needed and that our method increases the accuracy by 3%. The selected dipeptide features are analyzed and shown to have important roles on specificity pattern of PDZ domains.
{"title":"Protein interaction prediction for mouse pdz domains using dipeptide composition features","authors":"Songyot Nakariyakul, Zhiping Liu, Luonan Chen","doi":"10.1109/ISB.2011.6033143","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033143","url":null,"abstract":"The PDZ domain is one of the largest families of protein domains that are involved in targeting and routing specific proteins in signaling pathways. PDZ domains mediate protein-protein interactions by binding the C-terminal peptides of their target proteins. Using the dipeptide feature encoding, we develop a PDZ domain interaction predictor using a support vector machine that achieves a high accuracy rate of 82.49%. Since most of the dipeptide compositions are redundant and irrelevant, we propose a new hybrid feature selection technique to select only a subset of these compositions that are useful for interaction prediction. Our experimental results show that only approximately 25% of dipeptide features are needed and that our method increases the accuracy by 3%. The selected dipeptide features are analyzed and shown to have important roles on specificity pattern of PDZ domains.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130948113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033170
Yoshinori Fukasawa, R. Leung, S. Tsui, P. Horton
Protein sub-cellular localization is a central problem in understanding cell biology and has been the focus of intense research. In order to predict localization from amino acid sequence a myriad of features have been tried: including amino acid composition, sequence similarity, the presence of certain motifs or domains, and many others.
{"title":"Evolutionary sequence divergence predicts protein sub-cellular localization signals","authors":"Yoshinori Fukasawa, R. Leung, S. Tsui, P. Horton","doi":"10.1109/ISB.2011.6033170","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033170","url":null,"abstract":"Protein sub-cellular localization is a central problem in understanding cell biology and has been the focus of intense research. In order to predict localization from amino acid sequence a myriad of features have been tried: including amino acid composition, sequence similarity, the presence of certain motifs or domains, and many others.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127307560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033158
Naifang Su, Yufu Wang, M. Qian, Minghua Deng
Gene regulation is a key factor in gaining a full understanding of molecular biology. microRNA (miRNA), a novel class of non-coding RNA, has recently been found to be one crucial class of post-transactional regulators, and play important parts in cancer. One essential step to understand the regulatory effect of miRNAs is the reliable prediction of their target mRNAs. Typically, the predictions are solely based on sequence information, which unavoidably have high false detection rates. Here we develop a new algorithm called HCTarget, which predict miRNA targets by integrating the typical algorithm and the paired expression profiles of miRNA and mRNA. HCTarget formulates a linear model to characterize the relationship between mRNA and miRNA, and use a Markov Chain Monto Carlo algorithm to learn the target probabilities. When applying HCtarget to the expression data in multiple myeloma, we predict target genes for ten cancer related miRNAs. The experimental verification and a loss of function study of hsa-miR-16 validate our predictions. Compared with the previous approaches, our target sets have increased functional enrichment. Meanwhile, our predicted target pair hsa-miR-19b and SULF1 plays an important role in multiple myeloma. Therefore, HCtarget is a reliable and effective approach to predict miRNA target genes, and could improve our comprehensive understanding of gene regulation.
{"title":"Predicting MicroRNA targets by integrating sequence and expression data in cancer","authors":"Naifang Su, Yufu Wang, M. Qian, Minghua Deng","doi":"10.1109/ISB.2011.6033158","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033158","url":null,"abstract":"Gene regulation is a key factor in gaining a full understanding of molecular biology. microRNA (miRNA), a novel class of non-coding RNA, has recently been found to be one crucial class of post-transactional regulators, and play important parts in cancer. One essential step to understand the regulatory effect of miRNAs is the reliable prediction of their target mRNAs. Typically, the predictions are solely based on sequence information, which unavoidably have high false detection rates. Here we develop a new algorithm called HCTarget, which predict miRNA targets by integrating the typical algorithm and the paired expression profiles of miRNA and mRNA. HCTarget formulates a linear model to characterize the relationship between mRNA and miRNA, and use a Markov Chain Monto Carlo algorithm to learn the target probabilities. When applying HCtarget to the expression data in multiple myeloma, we predict target genes for ten cancer related miRNAs. The experimental verification and a loss of function study of hsa-miR-16 validate our predictions. Compared with the previous approaches, our target sets have increased functional enrichment. Meanwhile, our predicted target pair hsa-miR-19b and SULF1 plays an important role in multiple myeloma. Therefore, HCtarget is a reliable and effective approach to predict miRNA target genes, and could improve our comprehensive understanding of gene regulation.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123685134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-19DOI: 10.1109/ISB.2011.6033116
Shuyun Jiao, Yanbo Wang, Bo Yuan, P. Ao
Background: The accumulation of deleterious mutations of a population directly contributes to the fate as to how long the population would exist. Muller's ratchet provides a quantitative framework to study the effect of accumulation. Adaptive landscape as a powerful concept in system biology provides a handle to describe complex and rare biological events. In this article we study the evolutionary process of a population exposed to Muller's ratchet from the new viewpoint of adaptive landscape which allows us estimate the single click of the ratchet starting with an intuitive understanding.
{"title":"Kinetics of muller's ratchet from adaptive landscape viewpoint","authors":"Shuyun Jiao, Yanbo Wang, Bo Yuan, P. Ao","doi":"10.1109/ISB.2011.6033116","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033116","url":null,"abstract":"Background: The accumulation of deleterious mutations of a population directly contributes to the fate as to how long the population would exist. Muller's ratchet provides a quantitative framework to study the effect of accumulation. Adaptive landscape as a powerful concept in system biology provides a handle to describe complex and rare biological events. In this article we study the evolutionary process of a population exposed to Muller's ratchet from the new viewpoint of adaptive landscape which allows us estimate the single click of the ratchet starting with an intuitive understanding.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123397940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-01DOI: 10.1109/isb.2011.6033141
Melvin Zhang, Hon Wai Leong
{"title":"Identifying positional homologs as bidirectional best hits of sequence and gene context similarity","authors":"Melvin Zhang, Hon Wai Leong","doi":"10.1109/isb.2011.6033141","DOIUrl":"https://doi.org/10.1109/isb.2011.6033141","url":null,"abstract":"","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117346721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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