Pub Date : 2014-12-18DOI: 10.1109/ISB.2014.6990755
Jichun Chang, Ruiqi Wang
Inflammation is a critical part of tumour progression. But the regulatory mechanisms linking inflammation and cells transformation was less understood. A pathway linking inflammation to cell transformation which is maintained by a positive feedback loop involving NF-κB, Lin28, Let-7 and IL6 has been discovered. We extended the pathway,in which Myc and miR-17-92 microRNA cluster are added. Their roles are studied through the method of qualitative analysis. The result showed that both Myc and miR-17-92 microRNA cluster can promote the transformation. We have verified the the important elements of the pathway through sensitivity analysis.
{"title":"An epigenetic switch involving a positive feedback loop linking inflammation to cancer effected by Myc and miRNA-17-92 microRNA cluster","authors":"Jichun Chang, Ruiqi Wang","doi":"10.1109/ISB.2014.6990755","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990755","url":null,"abstract":"Inflammation is a critical part of tumour progression. But the regulatory mechanisms linking inflammation and cells transformation was less understood. A pathway linking inflammation to cell transformation which is maintained by a positive feedback loop involving NF-κB, Lin28, Let-7 and IL6 has been discovered. We extended the pathway,in which Myc and miR-17-92 microRNA cluster are added. Their roles are studied through the method of qualitative analysis. The result showed that both Myc and miR-17-92 microRNA cluster can promote the transformation. We have verified the the important elements of the pathway through sensitivity analysis.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"1 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":"129070130","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.2014.6990421
Pei Wang, Yuhuan Zhang, Jinhu Lu, Xinghuo Yu
Human housekeeping genes (HKGs) are widely expressed in various tissues, which involve in cell maintenance or sustaining cell function, and are often taken as experimental control and normalization references in gene expression experiments. Based on literature curation and up-to-date databases, we construct a large-scale human protein-protein interaction network (HPIN) and a HKGs subnetwork. Through the topological features of HKGs in the HPIN, we characterize the topological features of human HKGs. Our results indicate HKGs are with very large average degree, k-shell, betweeness, semilocal and eigenvector centralities, clustering coefficient, closeness, PageRank and motif centrality, which are all higher than that of the HPIN. Among the nine indexes, HKGs are with the average betweeness about 7 times larger than that for the HPIN, but they are also with the largest coefficient of variant (CV). The closeness of HKGs is with the smallest CV and very large median. Based on ROC analysis, we find most of the indexes and their compositions can be used to predict HKGs, with prediction accuracy around 80%. Especially, the prediction accuracy of the closeness can achieve as high as 82.36%. The investigations shed some lights on the characterization and identification of human functional genes, which have potential implications in systems biology and networked medicine.
{"title":"Topological characterization of housekeeping genes in human protein-protein interaction network","authors":"Pei Wang, Yuhuan Zhang, Jinhu Lu, Xinghuo Yu","doi":"10.1109/ISB.2014.6990421","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990421","url":null,"abstract":"Human housekeeping genes (HKGs) are widely expressed in various tissues, which involve in cell maintenance or sustaining cell function, and are often taken as experimental control and normalization references in gene expression experiments. Based on literature curation and up-to-date databases, we construct a large-scale human protein-protein interaction network (HPIN) and a HKGs subnetwork. Through the topological features of HKGs in the HPIN, we characterize the topological features of human HKGs. Our results indicate HKGs are with very large average degree, k-shell, betweeness, semilocal and eigenvector centralities, clustering coefficient, closeness, PageRank and motif centrality, which are all higher than that of the HPIN. Among the nine indexes, HKGs are with the average betweeness about 7 times larger than that for the HPIN, but they are also with the largest coefficient of variant (CV). The closeness of HKGs is with the smallest CV and very large median. Based on ROC analysis, we find most of the indexes and their compositions can be used to predict HKGs, with prediction accuracy around 80%. Especially, the prediction accuracy of the closeness can achieve as high as 82.36%. The investigations shed some lights on the characterization and identification of human functional genes, which have potential implications in systems biology and networked medicine.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"244 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":"114555017","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.2014.6990741
G. Hong, Hongdong Li, Wenjing Zhang, Zheng Guo, Beibei Chen, Hui Xu, L. Ao
Functional enrichment analysis is usually adopted after the identification of differentially expressed (DE) genes in studies focusing on cancer peripheral blood (PB) gene expression. However, whether the disturbed functional signals reflect the expression changes in blood cells or the cell population shifts under cancer condition remains unclear. By deconvolving the gene expression profiles of multiple cancer datasets, we showed that the proportion of myeloid-origin cells increased whereas the proportion of lymphoid-origin cells decreased in cancer PB. The DE genes between cancer PB samples and controls were highly consistent with DE genes between myeloid-origin and lymphoidorigin cells, indicating that cell population shifts contributed predominantly to the differential signals in cancer PB. All of the functional categories enriched for cancer PB DE genes were enriched for DE genes between myeloid-origin and lymphoidorigin cells, suggesting that functional signals in cancer PB probably reflect the changes of population shifts in blood cells, thus the enriched functional categories might not be able to reflect the cell type specific expression changes. Therefore, caution should be taken in translational biomarker discovery based on human PB gene expression profiles.
{"title":"Functional analysis of differential mRNAs in cancer peripheral blood: reflection of population shifts in myeloid-origin and lymphoid-origin cells","authors":"G. Hong, Hongdong Li, Wenjing Zhang, Zheng Guo, Beibei Chen, Hui Xu, L. Ao","doi":"10.1109/ISB.2014.6990741","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990741","url":null,"abstract":"Functional enrichment analysis is usually adopted after the identification of differentially expressed (DE) genes in studies focusing on cancer peripheral blood (PB) gene expression. However, whether the disturbed functional signals reflect the expression changes in blood cells or the cell population shifts under cancer condition remains unclear. By deconvolving the gene expression profiles of multiple cancer datasets, we showed that the proportion of myeloid-origin cells increased whereas the proportion of lymphoid-origin cells decreased in cancer PB. The DE genes between cancer PB samples and controls were highly consistent with DE genes between myeloid-origin and lymphoidorigin cells, indicating that cell population shifts contributed predominantly to the differential signals in cancer PB. All of the functional categories enriched for cancer PB DE genes were enriched for DE genes between myeloid-origin and lymphoidorigin cells, suggesting that functional signals in cancer PB probably reflect the changes of population shifts in blood cells, thus the enriched functional categories might not be able to reflect the cell type specific expression changes. Therefore, caution should be taken in translational biomarker discovery based on human PB gene expression profiles.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"26 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":"128312224","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.2014.6990431
Chenyang Shen, Shuqin Zhang, M. Ng
The interactions among different genes, proteins and other small molecules are becoming more and more significant and have been studied intensively nowadays. One general way that helps people understand these interactions is to analyze networks constructed from genes/proteins. In particular, module structure as a common property of most biological networks has drawn much attention of researchers from different fields. In most cases, biological networks can be corrupted by noise in the data and the corruption may cause mis-identification of module structure. Besides, some structure may be destroyed when improper experimental settings are built up. Thus module structure may be unstable when one single network is employed. In this paper, we consider employing multiple networks for consistent module detection in order to reduce the effect of noise and experimental setting. Instead of considering different networks separately, our idea is to combine multiple networks together by building them into tensor structure data. Then given any node as prior label information, tensor-based Markov chains are constructed iteratively for identification of the modules shared by the multiple networks. In addition, the proposed tensor-based Markov chain algorithm is capable of simultaneously evaluating the contribution from each network. It would be useful to measure the consistency of modules in the multiple networks. In the experiments, we test our method on two groups of gene co-expression networks from human beings. We also validate the modules identified by the proposed method.
{"title":"A tensor-based Markov chain method for module identification from multiple networks","authors":"Chenyang Shen, Shuqin Zhang, M. Ng","doi":"10.1109/ISB.2014.6990431","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990431","url":null,"abstract":"The interactions among different genes, proteins and other small molecules are becoming more and more significant and have been studied intensively nowadays. One general way that helps people understand these interactions is to analyze networks constructed from genes/proteins. In particular, module structure as a common property of most biological networks has drawn much attention of researchers from different fields. In most cases, biological networks can be corrupted by noise in the data and the corruption may cause mis-identification of module structure. Besides, some structure may be destroyed when improper experimental settings are built up. Thus module structure may be unstable when one single network is employed. In this paper, we consider employing multiple networks for consistent module detection in order to reduce the effect of noise and experimental setting. Instead of considering different networks separately, our idea is to combine multiple networks together by building them into tensor structure data. Then given any node as prior label information, tensor-based Markov chains are constructed iteratively for identification of the modules shared by the multiple networks. In addition, the proposed tensor-based Markov chain algorithm is capable of simultaneously evaluating the contribution from each network. It would be useful to measure the consistency of modules in the multiple networks. In the experiments, we test our method on two groups of gene co-expression networks from human beings. We also validate the modules identified by the proposed method.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"29 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":"133093213","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.2014.6990424
Lisha Liang, Yongmei Su
In this paper, an HIV-1 infection model with Beddington-DeAngelis infection rate and CTL immune response is investaged. We derive the basic reproduction number R0 for the viral infection model. By constructing suitable Lyapunov functionals and using LaSalle invariant principle for the delay differential equations, we find when R0 ≤ 1, the infection-free equilibrium is globally asymptotically stable. And if the CTL immune reproductive number R1 ≤ 1, the immune-free equilibrium and the endemic equilibrium are globally asymptotically stable.
{"title":"Global analysis of a delay virus dynamics model with Beddington-DeAngelis incidence rate and CTL immune response","authors":"Lisha Liang, Yongmei Su","doi":"10.1109/ISB.2014.6990424","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990424","url":null,"abstract":"In this paper, an HIV-1 infection model with Beddington-DeAngelis infection rate and CTL immune response is investaged. We derive the basic reproduction number R0 for the viral infection model. By constructing suitable Lyapunov functionals and using LaSalle invariant principle for the delay differential equations, we find when R0 ≤ 1, the infection-free equilibrium is globally asymptotically stable. And if the CTL immune reproductive number R1 ≤ 1, the immune-free equilibrium and the endemic equilibrium are globally asymptotically stable.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"23 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":"126980178","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.2014.6990429
Xiaoqian Huo, Ludi Jiang, Xi Chen, Yusu He, Yongqiang Yang, Yanling Zhang
NPC1L1, a protein localized in jejunal enterocytes, is critical for cholesterol absorption. As the receptor inhibitors are effective solutions for hyperlipidaemia, NPC1L1 receptor is becoming a hot spot in drug targets. In this study, pharmacophore modeling and molecular docking were combined to discover potential NPC1L1 inhibitors from traditional Chinese medicine. The best pharmacophore model, Hypo1, which was generated by 9 known inhibitors, comprised of two Hydrogen bond acceptor lipid and two Hydrophobic aromatic regions. And the active compounds hit rate (A%), identification index (N), and comprehensive evaluation index (CAI) are 100%, 3.852, and 3.852 respectively. Hypo1 was used to screen TCMD (version 2009) to identify potential inhibitors, which resulted in a hit list of 38 compounds with Lipinski's rule of five. In addition, docking was used to refine pharmacophore-based screening results by using ezetimibe as a reference. Then, 11 compounds with higher docking score than ezetimibe had been reserved. This paper provides a reliable utility for discovering natural NPC1L1 receptor inhibitors from traditional Chinese herbs.
{"title":"A combination of pharmacophore modeling, molecular docking and virtual screening for NPC1L1 receptor inhibitors from Chinese herbs","authors":"Xiaoqian Huo, Ludi Jiang, Xi Chen, Yusu He, Yongqiang Yang, Yanling Zhang","doi":"10.1109/ISB.2014.6990429","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990429","url":null,"abstract":"NPC1L1, a protein localized in jejunal enterocytes, is critical for cholesterol absorption. As the receptor inhibitors are effective solutions for hyperlipidaemia, NPC1L1 receptor is becoming a hot spot in drug targets. In this study, pharmacophore modeling and molecular docking were combined to discover potential NPC1L1 inhibitors from traditional Chinese medicine. The best pharmacophore model, Hypo1, which was generated by 9 known inhibitors, comprised of two Hydrogen bond acceptor lipid and two Hydrophobic aromatic regions. And the active compounds hit rate (A%), identification index (N), and comprehensive evaluation index (CAI) are 100%, 3.852, and 3.852 respectively. Hypo1 was used to screen TCMD (version 2009) to identify potential inhibitors, which resulted in a hit list of 38 compounds with Lipinski's rule of five. In addition, docking was used to refine pharmacophore-based screening results by using ezetimibe as a reference. Then, 11 compounds with higher docking score than ezetimibe had been reserved. This paper provides a reliable utility for discovering natural NPC1L1 receptor inhibitors from traditional Chinese herbs.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"330 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":"122836395","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.2014.6990756
Yuji Zhang
Monitoring the changes in gene expression patterns over time provides the distinct possibility of unraveling the mechanistic drivers characterizing cellular responses. Such time series gene expression data allow us to broadly “watch” the dynamics of the system. However, one challenge in the analysis of time series data is to establish and characterize the interplay between genes that are activated, deactivated or sustained in the context of a biological process or functional category. To address such challenges, novel algorithms are required to improve the interpretation of these data by integrating multi-source prior functional evidence. In this paper, we introduced a novel network-based approach to extract functional knowledge from time-dependent biological processes at a system level using time series mRNA deep sequencing data. First, a list of differentially expressed genes (DEGs) at each time point was identified. Second, GO terms that are enriched in each DEG list were identified. Third, the significance of interactions between DEGs in these GO terms at consecutive time points was measured. Finally, the significant interactions between DEGs in different GO terms were used to construct the interaction networks among GO terms between two consecutive time points, called GO networks. The proposed method was applied to investigate 1α, 25(OH)2D3-altered mechanisms in zebrafish embryo development. GO networks were constructed over 4 consecutive time points. Results suggest that biological processes such as cartilage development and one-carbon compound metabolic process are temporally regulated by 1α,25(OH)2D3. Such discoveries could not have been identified with canonical gene set enrichment analyses. These results demonstrate that the proposed approach can provide insight on the molecular mechanisms taking place in vertebrate embryo development upon treatment with 1α,25(OH)2D3. Our approach enables the monitoring of biological processes that can serve as a basis for generating new testable hypotheses. Such network-based integration approach can be easily extended to any temporal- or condition-dependent genomic data analyses.
{"title":"Network-based analysis of time series RNA-seq gene expression data by integrating the interactome and gene ontology information","authors":"Yuji Zhang","doi":"10.1109/ISB.2014.6990756","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990756","url":null,"abstract":"Monitoring the changes in gene expression patterns over time provides the distinct possibility of unraveling the mechanistic drivers characterizing cellular responses. Such time series gene expression data allow us to broadly “watch” the dynamics of the system. However, one challenge in the analysis of time series data is to establish and characterize the interplay between genes that are activated, deactivated or sustained in the context of a biological process or functional category. To address such challenges, novel algorithms are required to improve the interpretation of these data by integrating multi-source prior functional evidence. In this paper, we introduced a novel network-based approach to extract functional knowledge from time-dependent biological processes at a system level using time series mRNA deep sequencing data. First, a list of differentially expressed genes (DEGs) at each time point was identified. Second, GO terms that are enriched in each DEG list were identified. Third, the significance of interactions between DEGs in these GO terms at consecutive time points was measured. Finally, the significant interactions between DEGs in different GO terms were used to construct the interaction networks among GO terms between two consecutive time points, called GO networks. The proposed method was applied to investigate 1α, 25(OH)2D3-altered mechanisms in zebrafish embryo development. GO networks were constructed over 4 consecutive time points. Results suggest that biological processes such as cartilage development and one-carbon compound metabolic process are temporally regulated by 1α,25(OH)2D3. Such discoveries could not have been identified with canonical gene set enrichment analyses. These results demonstrate that the proposed approach can provide insight on the molecular mechanisms taking place in vertebrate embryo development upon treatment with 1α,25(OH)2D3. Our approach enables the monitoring of biological processes that can serve as a basis for generating new testable hypotheses. Such network-based integration approach can be easily extended to any temporal- or condition-dependent genomic data analyses.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"76 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":"131553736","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}
Dyslipidemia is a leading causative factor in cardiovascular diseases, and the traditional modulating lipid drugs mainly focus on reducing Low Density Lipoprotein Cholesterol (LDL-C). However, the increase of High Density Lipoprotein Cholesterol (HDL-C) also has gradually become an important focus on modulating lipid drugs. It is universally acknowledged that the drugs for significantly increasing HDL-C act on either the niacin receptor or cholesteryl ester transfer protein (CETP). Therefore, by comprehensively considering advantages and shortness of these two drug targets, compounds which act on the dual targets were studied in this paper. To be specific, a HipHop pharmacophore model for CETP inhibitors was built firstly, and then the pharmacophore model was validated internally and externally. The best pharmacophore model for CETP inhibitors included one hydrogen bond acceptor, four hydrophobic groups and two ring aromatics. In addition, the common basic structure of niacin receptor agonists was analyzed, and the novel basic structure was designed by bioisosterism principle. Afterward, the database of niacin receptor agonists, including 214 compounds, was established by fragment searching from traditional Chinese medicine database (TCMD, version 2009) and Lipinski' rules. Finally, five natural products with dual targets activity were gained by using CETP inhibitors pharmacophore model to screen the molecular database of niacin receptor agonists, which provided the study of dual-targets drug design with a reliable utility.
{"title":"Discovery of natural products for dual pharmacology CETP inhibitors and niacin receptor agonists","authors":"Lian-sheng Qiao, Yilian Cai, Yusu He, Yongqiang Yang, Ludi Jiang, Yanling Zhang","doi":"10.1109/ISB.2014.6990428","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990428","url":null,"abstract":"Dyslipidemia is a leading causative factor in cardiovascular diseases, and the traditional modulating lipid drugs mainly focus on reducing Low Density Lipoprotein Cholesterol (LDL-C). However, the increase of High Density Lipoprotein Cholesterol (HDL-C) also has gradually become an important focus on modulating lipid drugs. It is universally acknowledged that the drugs for significantly increasing HDL-C act on either the niacin receptor or cholesteryl ester transfer protein (CETP). Therefore, by comprehensively considering advantages and shortness of these two drug targets, compounds which act on the dual targets were studied in this paper. To be specific, a HipHop pharmacophore model for CETP inhibitors was built firstly, and then the pharmacophore model was validated internally and externally. The best pharmacophore model for CETP inhibitors included one hydrogen bond acceptor, four hydrophobic groups and two ring aromatics. In addition, the common basic structure of niacin receptor agonists was analyzed, and the novel basic structure was designed by bioisosterism principle. Afterward, the database of niacin receptor agonists, including 214 compounds, was established by fragment searching from traditional Chinese medicine database (TCMD, version 2009) and Lipinski' rules. Finally, five natural products with dual targets activity were gained by using CETP inhibitors pharmacophore model to screen the molecular database of niacin receptor agonists, which provided the study of dual-targets drug design with a reliable utility.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"4 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":"130770097","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.2014.6990737
Xiaoqing Cheng, Yushan Qiu, Wenpin Hou, W. Ching
Modeling genetic regulatory networks is an important issue in systems biology. Various models and mathematical formalisms have been proposed in the literature to solve the capture problem. The main purpose in this paper is to show that the transition matrix generated under semi-tensor product approach (Here we call it the probability structure matrix for simplicity) and the traditional approach (Transition probability matrix) are similar to each other. And we shall discuss three important problems in Probabilistic Boolean Networks (PBNs): the dynamic of a PBN, the steady-state probability distribution and the inverse problem. Numerical examples are given to show the validity of our theory. We shall give a brief introduction to semi-tensor and its application. After that we shall focus on the main results: to show the similarity of these two matrices. Since the semi-tensor approach gives a new way for interpreting a BN and therefore a PBN, we expect that advanced algorithms can be developed if one can describe the PBN through semi-tensor product approach.
{"title":"A semi-tensor product approach for Probabilistic Boolean Networks","authors":"Xiaoqing Cheng, Yushan Qiu, Wenpin Hou, W. Ching","doi":"10.1109/ISB.2014.6990737","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990737","url":null,"abstract":"Modeling genetic regulatory networks is an important issue in systems biology. Various models and mathematical formalisms have been proposed in the literature to solve the capture problem. The main purpose in this paper is to show that the transition matrix generated under semi-tensor product approach (Here we call it the probability structure matrix for simplicity) and the traditional approach (Transition probability matrix) are similar to each other. And we shall discuss three important problems in Probabilistic Boolean Networks (PBNs): the dynamic of a PBN, the steady-state probability distribution and the inverse problem. Numerical examples are given to show the validity of our theory. We shall give a brief introduction to semi-tensor and its application. After that we shall focus on the main results: to show the similarity of these two matrices. Since the semi-tensor approach gives a new way for interpreting a BN and therefore a PBN, we expect that advanced algorithms can be developed if one can describe the PBN through semi-tensor product approach.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"102 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":"115546187","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.2014.6990751
Elizabeth Y. Chong, Yijian Huang, Hao Wu, Tianwei Yu, N. Ghasemzadeh, K. Uppal, A. Quyyumi, Dean P. Jones
Feature selection is a critical step in translational omics research. False discovery rate (FDR) is anintegral tool of statistical inference in feature selection from high-throughput data. It is commonly used to screen features (SNPs, genes, proteins, or metabolites) for their relevance to the specific clinical outcome under study. Traditionally, all features are treated equally in the calculation of false discovery rate. In many applications, different features are measured with different levels of reliability. In such situations, treating all features equally will cause substantial loss of statistical power to detect significant features. Feature reliability can often be quantified in the measurements. Here we present a new method to estimate the local false discovery rate that incorporates feature reliability. We also propose a composite reliability index for metabolomics data. Combined with the new local false discovery rate method, it helps to detect more differentially expressed metabolites that are biologically meaningful in a real metabolomics dataset.
{"title":"Incorporating feature reliability in false discovery rateestimation improves statistical power to detect differentially expressed features","authors":"Elizabeth Y. Chong, Yijian Huang, Hao Wu, Tianwei Yu, N. Ghasemzadeh, K. Uppal, A. Quyyumi, Dean P. Jones","doi":"10.1109/ISB.2014.6990751","DOIUrl":"https://doi.org/10.1109/ISB.2014.6990751","url":null,"abstract":"Feature selection is a critical step in translational omics research. False discovery rate (FDR) is anintegral tool of statistical inference in feature selection from high-throughput data. It is commonly used to screen features (SNPs, genes, proteins, or metabolites) for their relevance to the specific clinical outcome under study. Traditionally, all features are treated equally in the calculation of false discovery rate. In many applications, different features are measured with different levels of reliability. In such situations, treating all features equally will cause substantial loss of statistical power to detect significant features. Feature reliability can often be quantified in the measurements. Here we present a new method to estimate the local false discovery rate that incorporates feature reliability. We also propose a composite reliability index for metabolomics data. Combined with the new local false discovery rate method, it helps to detect more differentially expressed metabolites that are biologically meaningful in a real metabolomics dataset.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"1 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":"123286161","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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