Cryptotanshinone (CPT) is one of the major liposoluble ingredients in Salvia miltiorrhiza which exerts antitumor activity on several types of cancers. However, the action mechanism of CPT remained to be clarified. The current study aimed to elucidate the antitumor mechanism of CPT based on the protein interaction network (PIN) analysis. A PIN of CPT was constructed with 244 nodes and 778 interactions, and was analyzed by Gene ontology (GO) enrichment analysis based on Markov Cluster algorithm (MCL). Two modules were found to be intimately associated with antitumor. Still further, the antitumor effect of CPT may be partly attributable to inhibiting the activation of the c-Src pathway and overexpression of EGFR, to mediating overexpression of PIAS and activation of EIF2AK3. Therefore, this study may shed new light on the antitumor mechanism and treatment of CPT.
{"title":"Antitumor mechanism research of cryptotanshinone by module-based network analysis","authors":"Shichao Zheng, Zhen-zhen Ren, Shi-feng Wang, Yan-ling Zhang, Yanjiang Qiao","doi":"10.1109/ISB.2014.6990427","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990427","url":null,"abstract":"Cryptotanshinone (CPT) is one of the major liposoluble ingredients in Salvia miltiorrhiza which exerts antitumor activity on several types of cancers. However, the action mechanism of CPT remained to be clarified. The current study aimed to elucidate the antitumor mechanism of CPT based on the protein interaction network (PIN) analysis. A PIN of CPT was constructed with 244 nodes and 778 interactions, and was analyzed by Gene ontology (GO) enrichment analysis based on Markov Cluster algorithm (MCL). Two modules were found to be intimately associated with antitumor. Still further, the antitumor effect of CPT may be partly attributable to inhibiting the activation of the c-Src pathway and overexpression of EGFR, to mediating overexpression of PIAS and activation of EIF2AK3. Therefore, this study may shed new light on the antitumor mechanism and treatment of CPT.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127500949","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 : 2014-12-18DOI: 10.1109/ISB.2014.6990423
Jin-Xing Liu, Chun-Xia Ma, Ying-Lian Gao, Jian Liu, C. Zheng
The significant advantage of sparse methods is to reduce the complicacy of genes expression data, which makes them easier to understand and interpret. In this paper, we propose a novel Class-information-based Sparse Non-negative Matrix Factorization (CISNMF) method which introduces the class information by the total scatter matrix. Firstly, the total scatter matrix is obtained via combining the between-class and within-class scatter matrices. Secondly, a new data matrix is constructed via singular values and left singular vectors which can be obtained via decomposing the total scatter matrix. Finally, we decompose the new data matrix by using sparse Non-negative Matrix Factorization and extract characteristic genes. In the end, results on gene expression data sets show that our method can extract more characteristic genes in response to abiotic stresses than conventional gene selection methods.
{"title":"A Class-information-based SNMF method for selecting characteristic genes","authors":"Jin-Xing Liu, Chun-Xia Ma, Ying-Lian Gao, Jian Liu, C. Zheng","doi":"10.1109/ISB.2014.6990423","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990423","url":null,"abstract":"The significant advantage of sparse methods is to reduce the complicacy of genes expression data, which makes them easier to understand and interpret. In this paper, we propose a novel Class-information-based Sparse Non-negative Matrix Factorization (CISNMF) method which introduces the class information by the total scatter matrix. Firstly, the total scatter matrix is obtained via combining the between-class and within-class scatter matrices. Secondly, a new data matrix is constructed via singular values and left singular vectors which can be obtained via decomposing the total scatter matrix. Finally, we decompose the new data matrix by using sparse Non-negative Matrix Factorization and extract characteristic genes. In the end, results on gene expression data sets show that our method can extract more characteristic genes in response to abiotic stresses than conventional gene selection methods.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129064188","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 : 2014-12-18DOI: 10.1109/ISB.2014.6990430
A. Arshad, M. Arshad, R. Abbasi, N. Ahmad, Christian M K Sieber
PITX3 belongs to a class of heomeodomain transcription factors involved in the development of dopaminergic neurons and ocular lens. Despite a great degree of homology, the mutation in human and mouse Pitx3 gene exhibit differences in the range and extent of phenotypic effects. The current study was designed to predict the effect of mutations in the mouse and human PITX3 gene using in silico tools. We used publically available bioinformatics tools to identify the secondary structure, functional domains, three-dimensional structure and DNA binding residues. Analysis of functional domains in the PITX3 revealed a lack of OAR domain in the G219fs mutation and in the mouse eyeless mutation. There was no difference in the functional motifs of the S13N and K111E mutation compared to the wild-type PITX3. However, an additional helix-turn-helix (HTH) domain is predicted in K111E mutation. Comparison of three-dimensional structures of the wild-type and mutant proteins did not show significant differences except 220delG. The eyeless mouse mutant protein exhibited a very different structure compared to the wild-type mouse Pitx3. Our results indicate that three-dimensional structure of the protein is a good predictor of the in vitro and in vivo behavior of the PITX3 protein and provides guidelines for performing the functional assays of the mutant proteins.
{"title":"In silico analysis of mutations in PITX3 gene","authors":"A. Arshad, M. Arshad, R. Abbasi, N. Ahmad, Christian M K Sieber","doi":"10.1109/ISB.2014.6990430","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990430","url":null,"abstract":"PITX3 belongs to a class of heomeodomain transcription factors involved in the development of dopaminergic neurons and ocular lens. Despite a great degree of homology, the mutation in human and mouse Pitx3 gene exhibit differences in the range and extent of phenotypic effects. The current study was designed to predict the effect of mutations in the mouse and human PITX3 gene using in silico tools. We used publically available bioinformatics tools to identify the secondary structure, functional domains, three-dimensional structure and DNA binding residues. Analysis of functional domains in the PITX3 revealed a lack of OAR domain in the G219fs mutation and in the mouse eyeless mutation. There was no difference in the functional motifs of the S13N and K111E mutation compared to the wild-type PITX3. However, an additional helix-turn-helix (HTH) domain is predicted in K111E mutation. Comparison of three-dimensional structures of the wild-type and mutant proteins did not show significant differences except 220delG. The eyeless mouse mutant protein exhibited a very different structure compared to the wild-type mouse Pitx3. Our results indicate that three-dimensional structure of the protein is a good predictor of the in vitro and in vivo behavior of the PITX3 protein and provides guidelines for performing the functional assays of the mutant proteins.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123621647","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 : 2014-12-18DOI: 10.1109/ISB.2014.6990757
Qian Zhu, Hongfang Liu, Yuji Zhang, Jiabei Wang
Traditional drug development is time and cost consuming process, conversely, drug repositioning is an emerging approach to discover novel usages of existing drugs with a better risk-versus-reward trade-off. Computational technology is playing a key role in drug repositioning to screening the best drug repositioning candidates from a large candidate library. Recent efforts made for computer aided drug repositioning are mostly focusing on applying/developing data mining algorithms against wild type of large scale of biomedical data. In this paper, we introduce a novel computational pipeline designed for drug repositioning candidate screening based on existing phenotypical association (disease-disease association) discovery and pathway enrichment analysis by exploring systems biology data relevant to the interested phenotypical association specifically. To demonstrate usability and evaluate efficacy of this novel pipeline, we successfully conducted a case study by identifying potential drug repositioning candidates for Alzheimer's disease (AD) based on the studied phenotypical association between cancer and AD.
{"title":"Evidence based computational drug repositioning candidate screening pipeline design: Case Study","authors":"Qian Zhu, Hongfang Liu, Yuji Zhang, Jiabei Wang","doi":"10.1109/ISB.2014.6990757","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990757","url":null,"abstract":"Traditional drug development is time and cost consuming process, conversely, drug repositioning is an emerging approach to discover novel usages of existing drugs with a better risk-versus-reward trade-off. Computational technology is playing a key role in drug repositioning to screening the best drug repositioning candidates from a large candidate library. Recent efforts made for computer aided drug repositioning are mostly focusing on applying/developing data mining algorithms against wild type of large scale of biomedical data. In this paper, we introduce a novel computational pipeline designed for drug repositioning candidate screening based on existing phenotypical association (disease-disease association) discovery and pathway enrichment analysis by exploring systems biology data relevant to the interested phenotypical association specifically. To demonstrate usability and evaluate efficacy of this novel pipeline, we successfully conducted a case study by identifying potential drug repositioning candidates for Alzheimer's disease (AD) based on the studied phenotypical association between cancer and AD.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117245220","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 : 2014-12-18DOI: 10.1109/ISB.2014.6990760
Gongxian Xu, Y. Liu, Chao Yu, Dan Su
This paper addresses the bi-objective optimization of continuous bio-dissimilation process of glycerol to 1, 3-propanediol. A bi-objective optimization model is firstly proposed to maximize the production rate of 1, 3-propanediol, simultaneously maximize the conversion rate of glycerol and ensure the bioprocess is operated under steady-state conditions. Then this bi-objective problem can be transformed into a sequence of single objective problems by using the weighted-sum and normal-boundary intersection methods respectively. Finally, these single objective problems are solved by an interior point method. The results show that the weighted-sum and normalboundary intersection methods can obtain the approximate Pareto-optimal set of the proposed bi-objective optimization problem.
{"title":"Bi-objective optimization of a continuous biological process","authors":"Gongxian Xu, Y. Liu, Chao Yu, Dan Su","doi":"10.1109/ISB.2014.6990760","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990760","url":null,"abstract":"This paper addresses the bi-objective optimization of continuous bio-dissimilation process of glycerol to 1, 3-propanediol. A bi-objective optimization model is firstly proposed to maximize the production rate of 1, 3-propanediol, simultaneously maximize the conversion rate of glycerol and ensure the bioprocess is operated under steady-state conditions. Then this bi-objective problem can be transformed into a sequence of single objective problems by using the weighted-sum and normal-boundary intersection methods respectively. Finally, these single objective problems are solved by an interior point method. The results show that the weighted-sum and normalboundary intersection methods can obtain the approximate Pareto-optimal set of the proposed bi-objective optimization problem.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132754070","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 : 2014-12-18DOI: 10.1109/ISB.2014.6990739
Zirui Zhang, Ke Chen, Hong-Qiang Wang
Pathway analysis plays an important role in exploring underlying connections between genomic data and complex diseases. In this paper, we propose a gene link-based method for identification of differentially expressed gene pathways. By viewing gene links in a pathway as a Markov chain, the proposed method first develops a gene link Markov chain model (MCM) and devises a Markov chain model-based classification rule to measure the biological importance of a gene link. Then, the expression difference of a pathway is estimated based on all the gene links in the pathway using the gene link MCM. The use of gene links, instead of individual genes, allows for exploring pathway topology that is crucial to pathway activity in cells. Results on two real-world gene expression data sets demonstrate that the effectiveness and efficiency of the proposed method in identifying differential gene pathways.
{"title":"A gene link-based method for identifying differential gene pathways","authors":"Zirui Zhang, Ke Chen, Hong-Qiang Wang","doi":"10.1109/ISB.2014.6990739","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990739","url":null,"abstract":"Pathway analysis plays an important role in exploring underlying connections between genomic data and complex diseases. In this paper, we propose a gene link-based method for identification of differentially expressed gene pathways. By viewing gene links in a pathway as a Markov chain, the proposed method first develops a gene link Markov chain model (MCM) and devises a Markov chain model-based classification rule to measure the biological importance of a gene link. Then, the expression difference of a pathway is estimated based on all the gene links in the pathway using the gene link MCM. The use of gene links, instead of individual genes, allows for exploring pathway topology that is crucial to pathway activity in cells. Results on two real-world gene expression data sets demonstrate that the effectiveness and efficiency of the proposed method in identifying differential gene pathways.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132007249","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 : 2014-12-18DOI: 10.1109/ISB.2014.6990747
Manli Zhou, Youxi Luo, Guoqin Mai, F. Zhou
Feature selection represents a major challenge in the biomedical data mining problem, and numerous algorithms have been proposed to select an optimal subset of features with the best classification performance. However, the existing algorithms do not take into account the vast amount of biomedical knowledge from the literature and experienced researchers. This work proposes a novel feature selection algorithm, cLP, with the optimized binary classification accuracy. The proposed algorithm incorporates the biomedical knowledge as constraints in the linear programming based optimization model. The experimental data shows that cLP outperforms the other feature selection algorithms, and its constrained version performs similarly well with the unconstrained version. Although theoretically constraints will reduce the classification model performance, our data shows that the constrained cLP sometimes even outperforms the unconstrained version. This suggests that besides the benefit of including biomedical knowledge in the model, the constrained cLP may also achieve better classification performance.
{"title":"cLP: Linear programming with biological constraints and its application in classification problems","authors":"Manli Zhou, Youxi Luo, Guoqin Mai, F. Zhou","doi":"10.1109/ISB.2014.6990747","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990747","url":null,"abstract":"Feature selection represents a major challenge in the biomedical data mining problem, and numerous algorithms have been proposed to select an optimal subset of features with the best classification performance. However, the existing algorithms do not take into account the vast amount of biomedical knowledge from the literature and experienced researchers. This work proposes a novel feature selection algorithm, cLP, with the optimized binary classification accuracy. The proposed algorithm incorporates the biomedical knowledge as constraints in the linear programming based optimization model. The experimental data shows that cLP outperforms the other feature selection algorithms, and its constrained version performs similarly well with the unconstrained version. Although theoretically constraints will reduce the classification model performance, our data shows that the constrained cLP sometimes even outperforms the unconstrained version. This suggests that besides the benefit of including biomedical knowledge in the model, the constrained cLP may also achieve better classification performance.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133746880","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 : 2014-12-18DOI: 10.1109/ISB.2014.6990738
Yongjin Kwon, K. Kang, C. Bae, Rebekah Jiyoung Cha
Physical activity is closely related to one's health status. Especially the intensity of physical activity is more important than other features for health benefits, which can be computed by the number of steps. With the advent of mobile devices, pedometer system can be implemented on mobile devices with their built-in sensors. However, due to the variety of types of platforms and devices, it is hard to ensure the consistency of step counting. In this paper, we propose a robust pedometer system for healthcare services, which ensures the consistent results of step counting upon heterogeneous platforms and multiple mobile devices. Based on the proposed system, we present the actual implementation of pedometer applications for different platforms and devices. We examine our implementation to verify that it is useful in real life with respect to the accuracy of step counting and battery consumption.
{"title":"Cross-platform and cross-device pedometer system designed for healthcare services","authors":"Yongjin Kwon, K. Kang, C. Bae, Rebekah Jiyoung Cha","doi":"10.1109/ISB.2014.6990738","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990738","url":null,"abstract":"Physical activity is closely related to one's health status. Especially the intensity of physical activity is more important than other features for health benefits, which can be computed by the number of steps. With the advent of mobile devices, pedometer system can be implemented on mobile devices with their built-in sensors. However, due to the variety of types of platforms and devices, it is hard to ensure the consistency of step counting. In this paper, we propose a robust pedometer system for healthcare services, which ensures the consistent results of step counting upon heterogeneous platforms and multiple mobile devices. Based on the proposed system, we present the actual implementation of pedometer applications for different platforms and devices. We examine our implementation to verify that it is useful in real life with respect to the accuracy of step counting and battery consumption.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131357484","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 : 2014-12-18DOI: 10.1109/ISB.2014.6990750
M. Hayashida, H. Koyano, T. Akutsu
Understanding of protein structures is important to find their functions. Many methods such as structural alignment, alignment-free similarity, and use of structural fragments have been developed for finding similar protein structures. In our previous study, we transformed protein structures into images each pixel of which represents the distance between the corresponding Cα atoms, and proposed similarity measures between two protein structures based on Kolmogorov complexity using image compression algorithms. In this paper, we examine efficient and effective image recognition techniques, SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features), which are invariant to image scaling, translation, and rotation, and partially invariant to affine or three-dimensional projection. We propose similarity based on SIFT and SURF, and apply it to classification of several protein structures. The results suggest that the similarity based on SURF outperforms several existing similarity measures including the compression-based similarity measures in our previous study, and that SIFT and SURF are useful for recognizing protein structures as well as objects in images.
{"title":"Measuring the similarity of protein structures using image local feature descriptors SIFT and SURF","authors":"M. Hayashida, H. Koyano, T. Akutsu","doi":"10.1109/ISB.2014.6990750","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990750","url":null,"abstract":"Understanding of protein structures is important to find their functions. Many methods such as structural alignment, alignment-free similarity, and use of structural fragments have been developed for finding similar protein structures. In our previous study, we transformed protein structures into images each pixel of which represents the distance between the corresponding Cα atoms, and proposed similarity measures between two protein structures based on Kolmogorov complexity using image compression algorithms. In this paper, we examine efficient and effective image recognition techniques, SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features), which are invariant to image scaling, translation, and rotation, and partially invariant to affine or three-dimensional projection. We propose similarity based on SIFT and SURF, and apply it to classification of several protein structures. The results suggest that the similarity based on SURF outperforms several existing similarity measures including the compression-based similarity measures in our previous study, and that SIFT and SURF are useful for recognizing protein structures as well as objects in images.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134646106","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 : 2014-12-18DOI: 10.1109/ISB.2014.6990745
Hongwei Chu, Xuezhong Zhou, Guangming Liu, Minghui Lv, Xiaofeng Zhou, Yiwei Wang, Lin Liu, Xing Li, P. Sun, Yizhun Zhu, Changkai Sun
Epilepsy is one of the common nervous system diseases and a complex brain disease that severely damages the life and health of humans. One-third of all epilepsy patients have medically intractable epilepsy (IE), for which anti-epileptic drugs are not effective. Therefore, discovery of potential drug targets is urgent and meaningful for better clinical solutions. Using the IE terms from Medical Subject Headings (MeSH) terminology, we integrated literature-based disease-gene relationships, which were extracted from the CoreMine PubMed search engine system, protein-protein interactions (PPI) and drug-target relationships from heterogeneous data sources, and used the network medicine approach to identify disease modules and detect enriched pathways. The potential drug targets and the underlying mechanisms were confirmed by chemical-protein interaction network and published literatures. Using 23 IE MeSH terms, we manually searched the CoreMine system to obtain 1,400 diseasegene associations, which had 871 distinct genes from the PubMed database. With the help of the PPI database (i.e. String 9), we mapped the genes to the PPI network and utilized the BGL community detection method to find 33 disease-related topological PPI modules that contain 640 proteins and 2,483 links. After that, we used the enrichment analysis method to obtain the PPI modules with pathway and gene ontology enrichment. Finally, we confirmed nine significant PPI modules that are considered as epilepsy disease modules with significant functional signatures. We combined genes with drugs in the DrugBank database to confirm the four proteins, MT-CYB, UQCRB, UQCRC1 and UQCRH, which would be potential drug targets for IE. The results of this study demonstrated that integrated network data sources and network-based approach are useful to understand the molecular mechanism and extract potential drug targets for IE.
{"title":"Network-based detection of disease modules and potential drug targets in intractable epilepsy","authors":"Hongwei Chu, Xuezhong Zhou, Guangming Liu, Minghui Lv, Xiaofeng Zhou, Yiwei Wang, Lin Liu, Xing Li, P. Sun, Yizhun Zhu, Changkai Sun","doi":"10.1109/ISB.2014.6990745","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990745","url":null,"abstract":"Epilepsy is one of the common nervous system diseases and a complex brain disease that severely damages the life and health of humans. One-third of all epilepsy patients have medically intractable epilepsy (IE), for which anti-epileptic drugs are not effective. Therefore, discovery of potential drug targets is urgent and meaningful for better clinical solutions. Using the IE terms from Medical Subject Headings (MeSH) terminology, we integrated literature-based disease-gene relationships, which were extracted from the CoreMine PubMed search engine system, protein-protein interactions (PPI) and drug-target relationships from heterogeneous data sources, and used the network medicine approach to identify disease modules and detect enriched pathways. The potential drug targets and the underlying mechanisms were confirmed by chemical-protein interaction network and published literatures. Using 23 IE MeSH terms, we manually searched the CoreMine system to obtain 1,400 diseasegene associations, which had 871 distinct genes from the PubMed database. With the help of the PPI database (i.e. String 9), we mapped the genes to the PPI network and utilized the BGL community detection method to find 33 disease-related topological PPI modules that contain 640 proteins and 2,483 links. After that, we used the enrichment analysis method to obtain the PPI modules with pathway and gene ontology enrichment. Finally, we confirmed nine significant PPI modules that are considered as epilepsy disease modules with significant functional signatures. We combined genes with drugs in the DrugBank database to confirm the four proteins, MT-CYB, UQCRB, UQCRC1 and UQCRH, which would be potential drug targets for IE. The results of this study demonstrated that integrated network data sources and network-based approach are useful to understand the molecular mechanism and extract potential drug targets for IE.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121018252","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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