Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033150
Wen Li, W. Ching, Lu-Bin Cui
Modeling genetic regulatory networks is an important research issue in systems biology. Many mathematical models have been proposed, and among these models, Boolean Network (BN) and its extension Probabilistic Boolean Network (PBN) are popular. In this paper we consider the problem constructing PBNs with gene perturbations. We propose a modified Newton's method to get the gene perturbation probability of the captured problem. Numerical experiments are given to demonstrate both effectiveness and efficiency of our proposed method.
{"title":"A modified newton's method for inverse problem of Probabilistic Boolean Networks with gene perturbations","authors":"Wen Li, W. Ching, Lu-Bin Cui","doi":"10.1109/ISB.2011.6033150","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033150","url":null,"abstract":"Modeling genetic regulatory networks is an important research issue in systems biology. Many mathematical models have been proposed, and among these models, Boolean Network (BN) and its extension Probabilistic Boolean Network (PBN) are popular. In this paper we consider the problem constructing PBNs with gene perturbations. We propose a modified Newton's method to get the gene perturbation probability of the captured problem. Numerical experiments are given to demonstrate both effectiveness and efficiency of our proposed method.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"39 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":"132173362","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}
Recently, we have identified 39 candidates of active regulatory networks for the diabetes progression in Goto-Kakizaki (GK) rat by using the network screening, which were well consistent with the previous knowledge of regulatory relationship between transcription factors (TFs) and their regulated genes. In addition, we have developed a computational procedure for identifying transcriptional master regulators (MRs) related to special biological phenomena, such as diseases, in conjunction of the network screening and inference. Here, we apply our procedure to identify the MR candidates for diabetes progression in GK rat. First, active TF-gene relationships for three periods in GK rat were detected by the network screening and the network inference, in consideration of TFs with specificity and coverage, and finally only 5 TFs were identified as the candidates of MRs. The limited number of the candidates of MRs promises to perform experiments to verify them.
{"title":"Identification of master regulator candidates for diabetes progression in Goto-Kakizaki Rat by a computational procedure","authors":"Shigeru Saito, Yidan Sun, Zhiping Liu, Yong Wang, Xiao Han, Huarong Zhou, Luonan Chen, K. Horimoto","doi":"10.1109/ISB.2011.6033155","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033155","url":null,"abstract":"Recently, we have identified 39 candidates of active regulatory networks for the diabetes progression in Goto-Kakizaki (GK) rat by using the network screening, which were well consistent with the previous knowledge of regulatory relationship between transcription factors (TFs) and their regulated genes. In addition, we have developed a computational procedure for identifying transcriptional master regulators (MRs) related to special biological phenomena, such as diseases, in conjunction of the network screening and inference. Here, we apply our procedure to identify the MR candidates for diabetes progression in GK rat. First, active TF-gene relationships for three periods in GK rat were detected by the network screening and the network inference, in consideration of TFs with specificity and coverage, and finally only 5 TFs were identified as the candidates of MRs. The limited number of the candidates of MRs promises to perform experiments to verify them.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"217 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":"133582701","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}
Time-series gene expression data analysis plays an important role in bioinformatics. In this paper, we propose a biclustering method to detect local expression patterns in time-series gene expression data by performing clustering on both gene and time dimensions. Our method aims to find gene subsets which show coherent expression profiles in some time subsets which have a consecutive order in a bicluster. Specifically, our temporal biclustering method is composed of a discretization procedure and a follow-up sequence alignment, which can identify similar local expression profiles and further reveal coherent local relations such as complementary and time-lagged coherence. We apply our method to yeast cell cycle data, and find several biologically important biclusters.
{"title":"Detecting coherent local patterns from time series gene expression data by a temporal biclustering method","authors":"Jibin Qu, Xiang-Sun Zhang, Ling-Yun Wu, Yong Wang, Luonan Chen","doi":"10.1109/ISB.2011.6033184","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033184","url":null,"abstract":"Time-series gene expression data analysis plays an important role in bioinformatics. In this paper, we propose a biclustering method to detect local expression patterns in time-series gene expression data by performing clustering on both gene and time dimensions. Our method aims to find gene subsets which show coherent expression profiles in some time subsets which have a consecutive order in a bicluster. Specifically, our temporal biclustering method is composed of a discretization procedure and a follow-up sequence alignment, which can identify similar local expression profiles and further reveal coherent local relations such as complementary and time-lagged coherence. We apply our method to yeast cell cycle data, and find several biologically important biclusters.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"96 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":"113969499","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.2011.6033113
Bo Zhang, Xiao-qin Wang, Hanying Chen, Qiusheng Zheng, Xin Li
The dual role of Resveratrol (Rsv) in cell apoptosis was recently reported by its anti/pro-oxidant activities. The involvement of ROS and GSH was thus investigated in Rsv-induced HeLa cell apoptosis. Rsv, higher than 10µM, elevated the intracellular ROS but reduced O2•− and GSH levels. ROS scavengers (Tempol, catalase) could not inhibit the apoptosis. Treatment with GSH modulators DTT or BSO were resulted up-regulation or down-regualtion GSH levels, but both enhanced Rsv-induced HeLa cell apoptosis. However, BSO could not prevent the DTT+Rsv treated HeLa cells from apoptosis. Further, Rsv-induced HeLa cell apoptosis was accompanied by activation of caspase 3 but not caspase 9, neither did the loss of mitochondrial membrane potential. Conclusively, the changes of ROS by Rsv were not tightly correlated with apoptosis in HeLa cells. However, intracellular GSH levels are partially related to Rsv-induced HeLa cell apoptosis via a mitochondrial independent manner.
{"title":"The role of GSH depletion in Resveratrol induced HeLa cell apoptosis","authors":"Bo Zhang, Xiao-qin Wang, Hanying Chen, Qiusheng Zheng, Xin Li","doi":"10.1109/ISB.2011.6033113","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033113","url":null,"abstract":"The dual role of Resveratrol (Rsv) in cell apoptosis was recently reported by its anti/pro-oxidant activities. The involvement of ROS and GSH was thus investigated in Rsv-induced HeLa cell apoptosis. Rsv, higher than 10µM, elevated the intracellular ROS but reduced O2•− and GSH levels. ROS scavengers (Tempol, catalase) could not inhibit the apoptosis. Treatment with GSH modulators DTT or BSO were resulted up-regulation or down-regualtion GSH levels, but both enhanced Rsv-induced HeLa cell apoptosis. However, BSO could not prevent the DTT+Rsv treated HeLa cells from apoptosis. Further, Rsv-induced HeLa cell apoptosis was accompanied by activation of caspase 3 but not caspase 9, neither did the loss of mitochondrial membrane potential. Conclusively, the changes of ROS by Rsv were not tightly correlated with apoptosis in HeLa cells. However, intracellular GSH levels are partially related to Rsv-induced HeLa cell apoptosis via a mitochondrial independent manner.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"19 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":"114789425","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.2011.6033161
X. Ren, Yong Wang, Luonan Chen, Xiang-Sun Zhang
With the development of high-throughput technologies, e.g. microarrays and the second generation sequencing technologies, gene expression profiles have been applied widely to characterize the functional states of various samples at different conditions. This is especially important for clinical biomarker identification that is vital to the understanding of the pathogenesis of a certain disease and the subsequent therapies. Because of the complexity of multi-gene disorders, a single biomarker or a set of separate biomarkers often fails to discriminate the samples correctly. Moreover, biomarker identification and class assignment of diseases are intrinsically linked while the current solutions to these two tasks are generally separated. Motivated by these issues, we give out a novel model based on linear programming in this study to simultaneously identify the most meaningful biomarkers and classify accurately the disease types for patients. Results on a few real data sets suggest the effectiveness and advantages of our method.
{"title":"A linear programming model for identifying non-redundant biomarkers based on gene expression profiles","authors":"X. Ren, Yong Wang, Luonan Chen, Xiang-Sun Zhang","doi":"10.1109/ISB.2011.6033161","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033161","url":null,"abstract":"With the development of high-throughput technologies, e.g. microarrays and the second generation sequencing technologies, gene expression profiles have been applied widely to characterize the functional states of various samples at different conditions. This is especially important for clinical biomarker identification that is vital to the understanding of the pathogenesis of a certain disease and the subsequent therapies. Because of the complexity of multi-gene disorders, a single biomarker or a set of separate biomarkers often fails to discriminate the samples correctly. Moreover, biomarker identification and class assignment of diseases are intrinsically linked while the current solutions to these two tasks are generally separated. Motivated by these issues, we give out a novel model based on linear programming in this study to simultaneously identify the most meaningful biomarkers and classify accurately the disease types for patients. Results on a few real data sets suggest the effectiveness and advantages of our method.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"66 2 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":"116582325","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.2011.6033118
Jing Zhao, Jie Chen, Tinghong Yang, Petter Holme
Complex chronic diseases are usually not caused by changes in a single causal gene but by an unbalanced regulating network resulting from the dysfunctions of multiple genes or their products. Therefore, network based systems approach can be helpful for the identification of candidate genes related to complex diseases and their relationships. The Axial spondyloarthropathy (SpA) is a group of chronic inflammatory joint diseases that mainly affects the spine and the sacroiliac joints, yet, the pathogenesis of SpA remains largely unknown. In this paper, we conducted a networked systems study on the pathogenesis of SpA. We integrated data related to SpA, from the OMIM database, proteomics and microarray experiments of SpA, to prioritize SpA candidate disease genes in the context of human protein interactome. Based on the top ranked SpA related genes, we constructed a PPI network and identified potential pathways associated with SpA. The PPI network and pathways reflect the well-known knowledge of SpA, i.e., immune mediated inflammation, as well as imbalanced bone modeling caused new bone formation and bone loss. This study may facilitate our understanding of the SpA pathogenesis from the perspective of network systems.
{"title":"Pathogenesis of axial spondyloarthropathy in a network perspective","authors":"Jing Zhao, Jie Chen, Tinghong Yang, Petter Holme","doi":"10.1109/ISB.2011.6033118","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033118","url":null,"abstract":"Complex chronic diseases are usually not caused by changes in a single causal gene but by an unbalanced regulating network resulting from the dysfunctions of multiple genes or their products. Therefore, network based systems approach can be helpful for the identification of candidate genes related to complex diseases and their relationships. The Axial spondyloarthropathy (SpA) is a group of chronic inflammatory joint diseases that mainly affects the spine and the sacroiliac joints, yet, the pathogenesis of SpA remains largely unknown. In this paper, we conducted a networked systems study on the pathogenesis of SpA. We integrated data related to SpA, from the OMIM database, proteomics and microarray experiments of SpA, to prioritize SpA candidate disease genes in the context of human protein interactome. Based on the top ranked SpA related genes, we constructed a PPI network and identified potential pathways associated with SpA. The PPI network and pathways reflect the well-known knowledge of SpA, i.e., immune mediated inflammation, as well as imbalanced bone modeling caused new bone formation and bone loss. This study may facilitate our understanding of the SpA pathogenesis from the perspective of network systems.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"98 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":"126095459","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}
The microbes in the world's oceans are most abundant organisms on earth, playing an important role in the maintenance the balance of marine ecology. However, little knowledge of ecological interdependencies is known due to the limitation of current method for large-scale data and narrow surveys done for marine microbes while microbe exhibited significant inter-lineage associations naturally. Here we present a similarity network-based method to represent and analyze potential interactions among the marine microbes based on the 16S rRNA sequences. A set of parameters such as network degrees, short path, clustering coefficient and so on, are computed to characterize the similarity network topology. A few core sub networks (or network motifs) were found which show that microbe in the marine environment has a cluster propensity and evolutionary relatedness, meanwhile, the variable of network motif also indicated that the microbial diversity has a regional difference. These results show the network-based methods are effective for advance understanding the complexity and function of the marine microbial community after experiment technical.
{"title":"A similarity network approach for analyzing the marine microbial diversity","authors":"Wei Chen, Yong-mei Cheng, Shaowu Zhang, Li-yang Hao, Peng Ding","doi":"10.1109/ISB.2011.6033153","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033153","url":null,"abstract":"The microbes in the world's oceans are most abundant organisms on earth, playing an important role in the maintenance the balance of marine ecology. However, little knowledge of ecological interdependencies is known due to the limitation of current method for large-scale data and narrow surveys done for marine microbes while microbe exhibited significant inter-lineage associations naturally. Here we present a similarity network-based method to represent and analyze potential interactions among the marine microbes based on the 16S rRNA sequences. A set of parameters such as network degrees, short path, clustering coefficient and so on, are computed to characterize the similarity network topology. A few core sub networks (or network motifs) were found which show that microbe in the marine environment has a cluster propensity and evolutionary relatedness, meanwhile, the variable of network motif also indicated that the microbial diversity has a regional difference. These results show the network-based methods are effective for advance understanding the complexity and function of the marine microbial community after experiment technical.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"27 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":"129974578","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.2011.6033120
Xiaoke Ma, Lin Gao
Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in protein complexes. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, fail to take into account the inherence organization within protein complex and the roles of edges. To investigate the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. By using the concept of bridgeness, a reliable virtual network is constructed, in which each maximal clique corresponds to a core. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. Finally, a comprehensive comparison between the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. The experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms and analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, implying that the role of interactions is a critical and promising factor in extracting protein complexes.
{"title":"Detecting protein complexes in PPI networks: The roles of interactions","authors":"Xiaoke Ma, Lin Gao","doi":"10.1109/ISB.2011.6033120","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033120","url":null,"abstract":"Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in protein complexes. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, fail to take into account the inherence organization within protein complex and the roles of edges. To investigate the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. By using the concept of bridgeness, a reliable virtual network is constructed, in which each maximal clique corresponds to a core. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. Finally, a comprehensive comparison between the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. The experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms and analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, implying that the role of interactions is a critical and promising factor in extracting protein complexes.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"146 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":"122684337","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.2011.6033115
Mingjun Wang, Hongbin Shen, T. Akutsu, Jiangning Song
Single amino acid polymorphisms (SAPs) are the most abundant form of known genetic variations associated with human diseases. It is of great interest to study the sequence-structure-function relationship underlying SAPs. In this work, we collected the human variant data from three databases and divided them into three categories, i.e. cancer somatic mutations (CSM), Mendelian disease-related variant (SVD) and neutral polymorphisms (SVP). We built support vector machine (SVM) classifiers to predict these three classes of SAPs, using the optimal features selected by a random forest algorithm. Consequently, 280 sequence-derived and structural features were initially extracted from the curated datasets from which 18 optimal candidate features were further selected by random forest. Furthermore, we performed a stepwise feature selection to select characteristic sequence and structural features that are important for predicting each SAPs class. As a result, our predictors achieved a prediction accuracy (ACC) of 84.97, 96.93, 86.98 and 88.24%, for the three classes, CSM, SVD and SVP, respectively. Performance comparison with other previously developed tools such as SIFT, SNAP and Polyphen2 indicates that our method provides a favorable performance with higher Sensitivity scores and Matthew's correlation coefficients (MCC). These results indicate that the prediction performance of SAPs classifiers can be effectively improved by feature selection. Moreover, division of SAPs into three respective categories and construction of accurate SVM-based classifiers for each class provides a practically useful way for investigating the difference between Mendelian disease-related variants and cancer somatic mutations.
{"title":"Predicting functional impact of single amino acid polymorphisms by integrating sequence and structural features","authors":"Mingjun Wang, Hongbin Shen, T. Akutsu, Jiangning Song","doi":"10.1109/ISB.2011.6033115","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033115","url":null,"abstract":"Single amino acid polymorphisms (SAPs) are the most abundant form of known genetic variations associated with human diseases. It is of great interest to study the sequence-structure-function relationship underlying SAPs. In this work, we collected the human variant data from three databases and divided them into three categories, i.e. cancer somatic mutations (CSM), Mendelian disease-related variant (SVD) and neutral polymorphisms (SVP). We built support vector machine (SVM) classifiers to predict these three classes of SAPs, using the optimal features selected by a random forest algorithm. Consequently, 280 sequence-derived and structural features were initially extracted from the curated datasets from which 18 optimal candidate features were further selected by random forest. Furthermore, we performed a stepwise feature selection to select characteristic sequence and structural features that are important for predicting each SAPs class. As a result, our predictors achieved a prediction accuracy (ACC) of 84.97, 96.93, 86.98 and 88.24%, for the three classes, CSM, SVD and SVP, respectively. Performance comparison with other previously developed tools such as SIFT, SNAP and Polyphen2 indicates that our method provides a favorable performance with higher Sensitivity scores and Matthew's correlation coefficients (MCC). These results indicate that the prediction performance of SAPs classifiers can be effectively improved by feature selection. Moreover, division of SAPs into three respective categories and construction of accurate SVM-based classifiers for each class provides a practically useful way for investigating the difference between Mendelian disease-related variants and cancer somatic mutations.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"19 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":"123276277","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}
Combination of different agents is widely used clinically to combat complex diseases with improved therapy and decreased side effects. It is necessary to understand the underlying mechanisms of drug combinations. In this work, we proposed a network-based approach to investigate drug combinations. Our results showed that the agents in an effective combination tend to have more similar therapeutic effects and more interaction partners in a ‘drug-cocktail network’ than random combination networks. Based on our results, we further developed a statistical model termed as Drug Combination Predictor (DCPred) by using the topological features of the drug-cocktail network, and assessed its prediction performance by making full use of a well-prepared dataset containing all known effective drug combinations extracted from the Drug Combination Database (DCDB). As a result, our model achieved the overall best AUC (Area Under the Curve) score of 0.92. Our findings provide useful insights into the underlying rules of effective drug combinations and offer important clues as to how to accelerate the discovery process of new combination drugs in the future.
{"title":"Exploring drug combinations in a drug-cocktail network","authors":"Ke-Jia Xu, Fuyan Hu, Jiangning Song, Xingming Zhao","doi":"10.1109/ISB.2011.6033183","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033183","url":null,"abstract":"Combination of different agents is widely used clinically to combat complex diseases with improved therapy and decreased side effects. It is necessary to understand the underlying mechanisms of drug combinations. In this work, we proposed a network-based approach to investigate drug combinations. Our results showed that the agents in an effective combination tend to have more similar therapeutic effects and more interaction partners in a ‘drug-cocktail network’ than random combination networks. Based on our results, we further developed a statistical model termed as Drug Combination Predictor (DCPred) by using the topological features of the drug-cocktail network, and assessed its prediction performance by making full use of a well-prepared dataset containing all known effective drug combinations extracted from the Drug Combination Database (DCDB). As a result, our model achieved the overall best AUC (Area Under the Curve) score of 0.92. Our findings provide useful insights into the underlying rules of effective drug combinations and offer important clues as to how to accelerate the discovery process of new combination drugs in the future.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"7 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":"125308366","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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