Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314152
Fuyan Hu, Xingming Zhao, N. Tang, Yan Zhang, Luonan Chen
Prostate cancer is one of the most important public health problems in developed countries. To date, a systematic understanding of the pathogenesis of prostate cancer is still lacking. In this work, we identified differentially expressed protein-coding genes and long non-coding RNAs (lncRNAs) between normal and cancer tissues based on a recent RNA-seq study from Caucasian population. We then investigated the relationship between differentially expressed genes and lncRNAs. Furthermore, based on a recently published prostate cancer study on Chinese population, we identified differentially expressed genes between Caucasian and Chinese populations to investigate racial difference. Moreover, for the first time, we compared the correlation of lncRNA-gene across populations. In the end, a lot of differentially expressed genes and lncRNAs were identified. Our results revealed that most of the lncRNA-gene pairs were positively correlated especially for the lncRNA-host gene pairs, indicating the probable mechanism of lncRNA. And 320 genes were differentially expressed in prostate cancer across populations, which may help us to investigate the ethnic differences of prostate cancer. In addition, our results suggested that lncRNAs regulate genes in different manners across populations. Our findings may help understand molecular events underlying prostate cancer development.
{"title":"Comparative analysis of protein-coding genes and long non-coding RNAs of prostate cancer between Caucasian and Chinese populations","authors":"Fuyan Hu, Xingming Zhao, N. Tang, Yan Zhang, Luonan Chen","doi":"10.1109/ISB.2012.6314152","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314152","url":null,"abstract":"Prostate cancer is one of the most important public health problems in developed countries. To date, a systematic understanding of the pathogenesis of prostate cancer is still lacking. In this work, we identified differentially expressed protein-coding genes and long non-coding RNAs (lncRNAs) between normal and cancer tissues based on a recent RNA-seq study from Caucasian population. We then investigated the relationship between differentially expressed genes and lncRNAs. Furthermore, based on a recently published prostate cancer study on Chinese population, we identified differentially expressed genes between Caucasian and Chinese populations to investigate racial difference. Moreover, for the first time, we compared the correlation of lncRNA-gene across populations. In the end, a lot of differentially expressed genes and lncRNAs were identified. Our results revealed that most of the lncRNA-gene pairs were positively correlated especially for the lncRNA-host gene pairs, indicating the probable mechanism of lncRNA. And 320 genes were differentially expressed in prostate cancer across populations, which may help us to investigate the ethnic differences of prostate cancer. In addition, our results suggested that lncRNAs regulate genes in different manners across populations. Our findings may help understand molecular events underlying prostate cancer development.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122725937","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314136
T. Zeng, Ruo-Chiau Wang, Luonan Chen
Response to stress is an important biological mechanism to react to environment variations. Different from distinguishing stresses like heat shock, ER stress, and oxidative stress, the study of response to an artificial signal like drug in therapy would be an alternative and also attractive way to understand the cellular response mechanism, which also benefits clinical application. Although differentially expressed genes are usually thought to be therapy responsive genes in many previous researches, more and more attention is diverted from single genes to functions or pathways, in particular for cancer therapy analysis. Thus, comparing with purely molecule (e.g., gene) rewiring, understanding functional reorganization or module rewiring would be more important for systematically studying therapy response or other dynamic biological processes. Therefore, in this paper we propose a model of module network rewiring to characterize functional reorganization, in contrast to gene network rewiring. Specifically, we develop a new framework named as module network rewiring analysis (MNRA) to investigate relevant network modules and their re-connections during an antiviral therapy. In MNRA, we aim to study module dynamics from the network viewpoint, by defining a module network with a module as a node and a path connecting two modules as an edge, which is a network for the molecular interaction system on a higher level. By MNRA experiments on expression data of patients with Hepatitis C virus infection (HCV) receiving Interferon therapy, we found that (1) the consistent module (a set of genes) separates two new subtypes of patients which were not discovered by differentially expressed genes; (2) the patient-group specific module network rewiring reveals necessary functional connections bridged by biological paths; (3) the hierarchical structures of temporal module network rewiring show that they can be taken as spatial-temporal markers to diagnose whether a patient has therapy response or not. Thus, MNRA indeed can provide biologically systematic clues for potential pharmacogenomic applications and has ability to characterize complex dynamic processes for many biological systems.
{"title":"Module network rewiring in response to therapy","authors":"T. Zeng, Ruo-Chiau Wang, Luonan Chen","doi":"10.1109/ISB.2012.6314136","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314136","url":null,"abstract":"Response to stress is an important biological mechanism to react to environment variations. Different from distinguishing stresses like heat shock, ER stress, and oxidative stress, the study of response to an artificial signal like drug in therapy would be an alternative and also attractive way to understand the cellular response mechanism, which also benefits clinical application. Although differentially expressed genes are usually thought to be therapy responsive genes in many previous researches, more and more attention is diverted from single genes to functions or pathways, in particular for cancer therapy analysis. Thus, comparing with purely molecule (e.g., gene) rewiring, understanding functional reorganization or module rewiring would be more important for systematically studying therapy response or other dynamic biological processes. Therefore, in this paper we propose a model of module network rewiring to characterize functional reorganization, in contrast to gene network rewiring. Specifically, we develop a new framework named as module network rewiring analysis (MNRA) to investigate relevant network modules and their re-connections during an antiviral therapy. In MNRA, we aim to study module dynamics from the network viewpoint, by defining a module network with a module as a node and a path connecting two modules as an edge, which is a network for the molecular interaction system on a higher level. By MNRA experiments on expression data of patients with Hepatitis C virus infection (HCV) receiving Interferon therapy, we found that (1) the consistent module (a set of genes) separates two new subtypes of patients which were not discovered by differentially expressed genes; (2) the patient-group specific module network rewiring reveals necessary functional connections bridged by biological paths; (3) the hierarchical structures of temporal module network rewiring show that they can be taken as spatial-temporal markers to diagnose whether a patient has therapy response or not. Thus, MNRA indeed can provide biologically systematic clues for potential pharmacogenomic applications and has ability to characterize complex dynamic processes for many biological systems.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130305613","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314117
Xinrong Zhou, K. Horimoto, Shigeru Saito, Luonan Chen, Huarong Zhou
We investigated the macroscopic changes in the regulatory coordination of diabetes progression during three periods in three tissues, adipose, liver and muscle, of Goto-Kakizaki (GK) rats. For this purpose, we performed network clustering by the Newman algorithm for the regulatory networks inferred by a modified path consistency algorithm, and investigated the biological functions of each cluster by an enrichment analysis of the constituent genes. We then compared the network clusters characterized by biological functions with the diabetes progression of GK rats in each of the three tissues. The network structure, the number of clusters, and the number of clusters characterized by biological functions during the three periods showed similar patterns in the three tissues. In contrast, further scrutiny of the biological functions at coordinated clusters revealed characteristic differences between the three tissues along the diabetes progression. In particular, the hypothetical roles of each tissue emerged: adipose and liver function at the cellular and molecular levels at the early stage, respectively, and all three tissues are responsible for diabetes progression, under the control of various transcriptional regulators.
{"title":"Network clustering along diabetes progression in three tissues of Goto-Kakizaki rats","authors":"Xinrong Zhou, K. Horimoto, Shigeru Saito, Luonan Chen, Huarong Zhou","doi":"10.1109/ISB.2012.6314117","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314117","url":null,"abstract":"We investigated the macroscopic changes in the regulatory coordination of diabetes progression during three periods in three tissues, adipose, liver and muscle, of Goto-Kakizaki (GK) rats. For this purpose, we performed network clustering by the Newman algorithm for the regulatory networks inferred by a modified path consistency algorithm, and investigated the biological functions of each cluster by an enrichment analysis of the constituent genes. We then compared the network clusters characterized by biological functions with the diabetes progression of GK rats in each of the three tissues. The network structure, the number of clusters, and the number of clusters characterized by biological functions during the three periods showed similar patterns in the three tissues. In contrast, further scrutiny of the biological functions at coordinated clusters revealed characteristic differences between the three tissues along the diabetes progression. In particular, the hypothetical roles of each tissue emerged: adipose and liver function at the cellular and molecular levels at the early stage, respectively, and all three tissues are responsible for diabetes progression, under the control of various transcriptional regulators.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"80 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120997260","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314118
Gongxian Xu
This paper considers multi-objective optimization problems of biological systems. The biological system is represented by the S-system formalism. The advantage of this representation is that the steady-state equations are linear when the variables of the models are expressed in logarithmic coordinates. Profiting from this special property of S-system models, we transform the original nonlinear problem into a multi-objective linear programming. The obtained problem is then reformulated as a new multi-objective programming that has no equality or inequality constraints. The example of tryptophan biosynthesis is performed to the proposed framework and shown to the effectiveness of the approach. The simulation is also studied to give a performance comparison between the proposed and nonlinear approaches.
{"title":"Multi-objective optimization of biological systems represented by S-system models","authors":"Gongxian Xu","doi":"10.1109/ISB.2012.6314118","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314118","url":null,"abstract":"This paper considers multi-objective optimization problems of biological systems. The biological system is represented by the S-system formalism. The advantage of this representation is that the steady-state equations are linear when the variables of the models are expressed in logarithmic coordinates. Profiting from this special property of S-system models, we transform the original nonlinear problem into a multi-objective linear programming. The obtained problem is then reformulated as a new multi-objective programming that has no equality or inequality constraints. The example of tryptophan biosynthesis is performed to the proposed framework and shown to the effectiveness of the approach. The simulation is also studied to give a performance comparison between the proposed and nonlinear approaches.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131307501","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314131
Fei Shi, Peipei Zhou, Ruiqi Wang
Coupled positive feedback loops are frequently-occurring motifs in gene transcription regulatory networks and signaling pathways. So it's very important to investigate the function of coupled positive feedback loops. In this paper we establish mathematical models of coupled positive feedback loops. Through the bifurcation analysis, we prove that two coupled positive feedback loops can generate reversible and irreversible switch. And coupled positive feedback loops can strengthen bistable, enlarge signal and extend the signal reaction time. Coupled positive feedback loops play an important role in regulate biological behaviors.
{"title":"Coupled positive feedback loops regulate the biological behavior","authors":"Fei Shi, Peipei Zhou, Ruiqi Wang","doi":"10.1109/ISB.2012.6314131","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314131","url":null,"abstract":"Coupled positive feedback loops are frequently-occurring motifs in gene transcription regulatory networks and signaling pathways. So it's very important to investigate the function of coupled positive feedback loops. In this paper we establish mathematical models of coupled positive feedback loops. Through the bifurcation analysis, we prove that two coupled positive feedback loops can generate reversible and irreversible switch. And coupled positive feedback loops can strengthen bistable, enlarge signal and extend the signal reaction time. Coupled positive feedback loops play an important role in regulate biological behaviors.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131528160","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314149
Bin Kang, Yuan-yuan Li, Yixue Li
Circadian regulatory system is an evolutionarily ancient biological system. Its prevalence in life kingdoms suggests it has fundamental role in life processes. Although genomic scale of circadian gene expression has been found in various species from cyanobacteria to mammalians, transcriptional patterns and mechanisms of global circadian gene regulation have not yet been revealed. Using high resolution temporal profiling of mouse circadian gene expression, we show that contrary with previously demonstrated clustering tendency of functionally related genes in mammalian genomes, circadian regulated genes display anti-clustering propensity in mouse liver. This unique property does not conform to the notion of domain-wide coordinated gene regulation dictated by acetyl modifications, which is recently identified as a hallmark of circadian regulation. These results suggest that global circadian regulation in mouse liver might involve other structural chromosome interactions irrelevant with clustering regulation.
{"title":"Anti-clustering of circadian gene expression in mouse liver genome","authors":"Bin Kang, Yuan-yuan Li, Yixue Li","doi":"10.1109/ISB.2012.6314149","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314149","url":null,"abstract":"Circadian regulatory system is an evolutionarily ancient biological system. Its prevalence in life kingdoms suggests it has fundamental role in life processes. Although genomic scale of circadian gene expression has been found in various species from cyanobacteria to mammalians, transcriptional patterns and mechanisms of global circadian gene regulation have not yet been revealed. Using high resolution temporal profiling of mouse circadian gene expression, we show that contrary with previously demonstrated clustering tendency of functionally related genes in mammalian genomes, circadian regulated genes display anti-clustering propensity in mouse liver. This unique property does not conform to the notion of domain-wide coordinated gene regulation dictated by acetyl modifications, which is recently identified as a hallmark of circadian regulation. These results suggest that global circadian regulation in mouse liver might involve other structural chromosome interactions irrelevant with clustering regulation.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132428706","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314120
Bing Yang, Junyan Tan, N. Deng, Ling Jing
The importance of network-based approach to identifying biological markers has been increasingly recognized. Lots of papers indicated that genes in a network tend to function together in biological processes, so taking full advantage of the biological observation can improve the performance of microarray classification. However, lots of SVM methods don't consider this situation during their classifier building. The main idea of this paper intends to embed the information of gene networks into a new SVM learning framework. Based on a new regularization, we propose a novel method, Network Kernel SVM (NK-SVM), for binary classification problem and gene sets selection. By constructing some special kernel matrixes from the prior information of gene network, the new NK-SVM method makes the genes in the same set to be selected (or eliminated) together. The numerical experiments on a real microarray application show that the proposed method tends to provide a better performance than other methods on gene sets selection.
{"title":"Network Kernel SVM for microarray classification and gene sets selection","authors":"Bing Yang, Junyan Tan, N. Deng, Ling Jing","doi":"10.1109/ISB.2012.6314120","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314120","url":null,"abstract":"The importance of network-based approach to identifying biological markers has been increasingly recognized. Lots of papers indicated that genes in a network tend to function together in biological processes, so taking full advantage of the biological observation can improve the performance of microarray classification. However, lots of SVM methods don't consider this situation during their classifier building. The main idea of this paper intends to embed the information of gene networks into a new SVM learning framework. Based on a new regularization, we propose a novel method, Network Kernel SVM (NK-SVM), for binary classification problem and gene sets selection. By constructing some special kernel matrixes from the prior information of gene network, the new NK-SVM method makes the genes in the same set to be selected (or eliminated) together. The numerical experiments on a real microarray application show that the proposed method tends to provide a better performance than other methods on gene sets selection.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121962005","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314150
Zikai Wu, Yong Wang, Luonan Chen
With the merits of faster development time and reduced risk, identifying new indications for marketed drugs draws more and more attention. In particular, repositioning drugs with known indications has become an hot topic in the area of computational systems biology. However, one of the common shortcomings for most of the previous methods is the ignorance of side effect, i.e., drug through primary targets and off targets might induce both desired and unintended effects respectively, which could not appropriately evaluated in most of existing methods. In this paper with a new measure considering both efficacy and side effect, we developed a new method for identifying the repositioned drugs against prostate cancer by evaluating the mutual relations of the gene expression levels between prostate cancer samples and those induced by bioactive compounds. In this measure, the overlap between gene sets that were oppositely regulated in disease state and drug treatment state was quantified by jaccard index as drug's efficacy while the overlap between essential genes and positively correlated genes (or regulated just after drug treatment) was quantified by jaccard index as drug's side effect, which were balanced with a parameter λ. The preliminary results on repositioning drugs for prostate cancer verify the effectiveness and efficiency of the new method.
{"title":"A new method to identify repositioned drugs for prostate cancer","authors":"Zikai Wu, Yong Wang, Luonan Chen","doi":"10.1109/ISB.2012.6314150","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314150","url":null,"abstract":"With the merits of faster development time and reduced risk, identifying new indications for marketed drugs draws more and more attention. In particular, repositioning drugs with known indications has become an hot topic in the area of computational systems biology. However, one of the common shortcomings for most of the previous methods is the ignorance of side effect, i.e., drug through primary targets and off targets might induce both desired and unintended effects respectively, which could not appropriately evaluated in most of existing methods. In this paper with a new measure considering both efficacy and side effect, we developed a new method for identifying the repositioned drugs against prostate cancer by evaluating the mutual relations of the gene expression levels between prostate cancer samples and those induced by bioactive compounds. In this measure, the overlap between gene sets that were oppositely regulated in disease state and drug treatment state was quantified by jaccard index as drug's efficacy while the overlap between essential genes and positively correlated genes (or regulated just after drug treatment) was quantified by jaccard index as drug's side effect, which were balanced with a parameter λ. The preliminary results on repositioning drugs for prostate cancer verify the effectiveness and efficiency of the new method.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128150942","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314113
Zimo Yin, Junyan Tan
Protein phosphorylation is involved in most cellular functions. Because of the importance of protein phosphorylation, many methods are conducted to identify the phosphorylation sites. Experimental methods for identifying phosphorylation sites are not only costly but also time consuming. Hence, computational methods are highly desired. In this paper, three new encoding methods, BinCTF(Binary-conjoint triad feature), CTF2(new conjoint triad feature) and BinCTF2(Binary-new conjoint triad feature), which are the modification of Binary and CTF encoding, are developed. Then an ensemble support vector machine is applied to predict the phosphorylation sites related to serine (S), threonine (T) and tyrosine (Y) residues. The numerical results indicate that some of the performance of these new methods are better than previous methods.
{"title":"New encoding schemes for prediction of protein phosphorylation sites","authors":"Zimo Yin, Junyan Tan","doi":"10.1109/ISB.2012.6314113","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314113","url":null,"abstract":"Protein phosphorylation is involved in most cellular functions. Because of the importance of protein phosphorylation, many methods are conducted to identify the phosphorylation sites. Experimental methods for identifying phosphorylation sites are not only costly but also time consuming. Hence, computational methods are highly desired. In this paper, three new encoding methods, BinCTF(Binary-conjoint triad feature), CTF2(new conjoint triad feature) and BinCTF2(Binary-new conjoint triad feature), which are the modification of Binary and CTF encoding, are developed. Then an ensemble support vector machine is applied to predict the phosphorylation sites related to serine (S), threonine (T) and tyrosine (Y) residues. The numerical results indicate that some of the performance of these new methods are better than previous methods.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134555019","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314104
Li-Zhi Liu, Fang-Xiang Wu, Wen-Jun Zhang
The S-system, which is a set of nonlinear ordinary differential equations and derived from the generalized mass action law, is a consistent model to describe various biological systems. Parameters in S-systems contain important biological information and yet can not be obtained directly from experiments. Therefore, the parameter estimation methods are a choice to estimate parameters in S-systems. However, the parameter estimation for this model turns out to be a complex nonlinear optimization problem. A novel method, alternating weighted least squares (AWLS), is proposed in this paper to estimate the parameters in S-systems. The fast deterministic AWLS method takes advantage of the special structure of the S-system model and reduces solving the nonlinear optimization problem into alternately solving weighed least squares problems which have analytical solutions. The effectiveness of AWLS is demonstrated by the simulation studies and the results show that the AWLS outperforms the existing alternating regression method.
{"title":"Alternating weighted least squares parameter estimation for biological S-systems","authors":"Li-Zhi Liu, Fang-Xiang Wu, Wen-Jun Zhang","doi":"10.1109/ISB.2012.6314104","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314104","url":null,"abstract":"The S-system, which is a set of nonlinear ordinary differential equations and derived from the generalized mass action law, is a consistent model to describe various biological systems. Parameters in S-systems contain important biological information and yet can not be obtained directly from experiments. Therefore, the parameter estimation methods are a choice to estimate parameters in S-systems. However, the parameter estimation for this model turns out to be a complex nonlinear optimization problem. A novel method, alternating weighted least squares (AWLS), is proposed in this paper to estimate the parameters in S-systems. The fast deterministic AWLS method takes advantage of the special structure of the S-system model and reduces solving the nonlinear optimization problem into alternately solving weighed least squares problems which have analytical solutions. The effectiveness of AWLS is demonstrated by the simulation studies and the results show that the AWLS outperforms the existing alternating regression method.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133143123","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：