Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314119
Yongmei Su, Yerong Wen, L. Min
Mathematical models have been used to understand the factors that govern infectious disease progression in viral infections. Many hepatitis B virus (HBV) models were set up based on the basic virus infection model (BVIM) introduced by Zeuzem et al. and Nowak et al. But some references have pointed out that the basic infection reproductive number of the BVIM is biologically questionable and given the modified models. And so far, no immune model with alanine aminotransferase (ALT) was given based on the modified models. In this paper one immune models with ALT based on the modified model is discussed. The stability analysis and simulation of the model is also given based on clinical data of ALT and HBV DNA.
{"title":"Analysis of a HBV infection model with ALT","authors":"Yongmei Su, Yerong Wen, L. Min","doi":"10.1109/ISB.2012.6314119","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314119","url":null,"abstract":"Mathematical models have been used to understand the factors that govern infectious disease progression in viral infections. Many hepatitis B virus (HBV) models were set up based on the basic virus infection model (BVIM) introduced by Zeuzem et al. and Nowak et al. But some references have pointed out that the basic infection reproductive number of the BVIM is biologically questionable and given the modified models. And so far, no immune model with alanine aminotransferase (ALT) was given based on the modified models. In this paper one immune models with ALT based on the modified model is discussed. The stability analysis and simulation of the model is also given based on clinical data of ALT and HBV DNA.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"118 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":"115639864","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.2012.6314143
Chien-Ming Chen, Tsan-Huang Shih, Tun-Wen Pai, Zhen-Long Liu, M. Chang
RNA-seq data analysis not only detects novel transcripts, promoters, and single nucleotide polymorphisms in a transcriptome scale, but also shows quantitative measurement of gene expression. In order to perform differential expression analysis for unraveling biological functions, we proposed a workflow which integrated annotations from KEGG biological pathways and Gene Ontology associations for manipulating multiple RNA-seq datasets. The developed system started from mapping short reads onto reference genes, and then performed normalization procedures on read coverage to evaluate and compare expression levels within various gene clusters. Different levels of gene expression were indicated by diverse color shades and graphically shown in designed temporal pathways. Besides, representative GO terms associated with differentially expressed gene cluster were also visually displayed by a GO tag cloud representation. Three different public RNA-seq datasets were applied to demonstrate that the proposed workflow could provide effective and efficient analysis on differential gene expression for either cross-strain comparison or an identical sample sequenced at different time points.
{"title":"RNA-seq coverage effects on biological pathways and GO tag clouds","authors":"Chien-Ming Chen, Tsan-Huang Shih, Tun-Wen Pai, Zhen-Long Liu, M. Chang","doi":"10.1109/ISB.2012.6314143","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314143","url":null,"abstract":"RNA-seq data analysis not only detects novel transcripts, promoters, and single nucleotide polymorphisms in a transcriptome scale, but also shows quantitative measurement of gene expression. In order to perform differential expression analysis for unraveling biological functions, we proposed a workflow which integrated annotations from KEGG biological pathways and Gene Ontology associations for manipulating multiple RNA-seq datasets. The developed system started from mapping short reads onto reference genes, and then performed normalization procedures on read coverage to evaluate and compare expression levels within various gene clusters. Different levels of gene expression were indicated by diverse color shades and graphically shown in designed temporal pathways. Besides, representative GO terms associated with differentially expressed gene cluster were also visually displayed by a GO tag cloud representation. Three different public RNA-seq datasets were applied to demonstrate that the proposed workflow could provide effective and efficient analysis on differential gene expression for either cross-strain comparison or an identical sample sequenced at different time points.","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":"114773862","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.2012.6314148
Song Xu, Shuyun Jiao, Pengyao Jiang, Bo Yuan, P. Ao
We study the transition time between different meta-stable states in the continuous Wright-Fisher (diffusion) model. We construct an adaptive landscape for describing the system both qualitatively and quantitatively. When strong genetic drift and weak mutation generate infinite adaptive peaks, we calculate the expected time to escape from such peak states. We find a new way to analytically approximate the escape time, which extends the application of Kramer's classical formulae to the cases of non-Gaussian equilibrium distribution and bridges previous results in two limits. Our adaptive landscape, compared to the classical fitness landscape or other scalar functions, is directly related to system's middle-and-long-term dynamics and is self-consistent in the whole parameter space. Our work provides a complete description for the bi-stabilities in the present model.
{"title":"Escape from infinite adaptive peak","authors":"Song Xu, Shuyun Jiao, Pengyao Jiang, Bo Yuan, P. Ao","doi":"10.1109/ISB.2012.6314148","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314148","url":null,"abstract":"We study the transition time between different meta-stable states in the continuous Wright-Fisher (diffusion) model. We construct an adaptive landscape for describing the system both qualitatively and quantitatively. When strong genetic drift and weak mutation generate infinite adaptive peaks, we calculate the expected time to escape from such peak states. We find a new way to analytically approximate the escape time, which extends the application of Kramer's classical formulae to the cases of non-Gaussian equilibrium distribution and bridges previous results in two limits. Our adaptive landscape, compared to the classical fitness landscape or other scalar functions, is directly related to system's middle-and-long-term dynamics and is self-consistent in the whole parameter space. Our work provides a complete description for the bi-stabilities in the present model.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"08 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":"129485310","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.2012.6314103
Limin Li, Hao Jiang, W. Ching, V. Vassiliadis
Metabolites can serve as biomarkers and their identification has significant importance in the study of biochemical reaction and signalling networks. Incorporating metabolic and gene expression data to reveal biochemical networks is a considerable challenge, which attracts a lot of attention in recent research. In this paper, we propose a promising approach to identify metabolic biomarkers through integrating available biomedical data and disease-specific gene expression data. A Linear Programming (LP) based method is then utilized to determine flux variability intervals, therefore enabling the analysis of significant metabolic reactions. A statistical approach is also presented to uncover these metabolites. The identified metabolites are then verified by comparing with the results in the existing literature. The proposed approach here can also be applied to the discovery of potential novel biomarkers.
{"title":"Metabolite biomarker discovery for metabolic diseases by flux analysis","authors":"Limin Li, Hao Jiang, W. Ching, V. Vassiliadis","doi":"10.1109/ISB.2012.6314103","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314103","url":null,"abstract":"Metabolites can serve as biomarkers and their identification has significant importance in the study of biochemical reaction and signalling networks. Incorporating metabolic and gene expression data to reveal biochemical networks is a considerable challenge, which attracts a lot of attention in recent research. In this paper, we propose a promising approach to identify metabolic biomarkers through integrating available biomedical data and disease-specific gene expression data. A Linear Programming (LP) based method is then utilized to determine flux variability intervals, therefore enabling the analysis of significant metabolic reactions. A statistical approach is also presented to uncover these metabolites. The identified metabolites are then verified by comparing with the results in the existing literature. The proposed approach here can also be applied to the discovery of potential novel biomarkers.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"26 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":"128480020","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.2012.6314109
Shaoqiang Zhang, Lifen Jiang, Chuanbin Du, Z. Su
Identifying binding sites recognized by transcription factors (TFs) is one of major challenges to decipher complex genetic regulatory networks encoded in a genome. A set of binding sites recognized by the same TF, called a motif, can be accurately represented by a position frequency matrix (PFM) or a position-specific scoring matrix (PSSM). Very often, we need to compare motifs when searching for similar motifs in a motif database for a query motif, or clustering motifs possibly recognized by the same TF. In this paper, we have designed a novel metric, called SPIC (Similarity between Positions with Information Contents), for quantifying the similarity between two motifs using their PFMs, PSSMs, and column information contents, and demonstrated that this metric outperforms the other state-of-the-art methods for clustering motifs of the same TF and differentiating motifs of different TFs.
{"title":"A novel information contents based similarity metric for comparing TFBS motifs","authors":"Shaoqiang Zhang, Lifen Jiang, Chuanbin Du, Z. Su","doi":"10.1109/ISB.2012.6314109","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314109","url":null,"abstract":"Identifying binding sites recognized by transcription factors (TFs) is one of major challenges to decipher complex genetic regulatory networks encoded in a genome. A set of binding sites recognized by the same TF, called a motif, can be accurately represented by a position frequency matrix (PFM) or a position-specific scoring matrix (PSSM). Very often, we need to compare motifs when searching for similar motifs in a motif database for a query motif, or clustering motifs possibly recognized by the same TF. In this paper, we have designed a novel metric, called SPIC (Similarity between Positions with Information Contents), for quantifying the similarity between two motifs using their PFMs, PSSMs, and column information contents, and demonstrated that this metric outperforms the other state-of-the-art methods for clustering motifs of the same TF and differentiating motifs of different TFs.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"67 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":"134623113","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.2012.6314142
G. Tam, Chunqi Chang, Y. Hung
Granger causality (GC) has been applied to gene regulatory network discovery using DNA microarray time-series data. Since the number of genes is much larger than the data length, a full model cannot be applied in a straightforward manner, hence GC is often applied to genes pairwisely. In this paper, firstly we investigate with synthetic data and point out how spurious causalities (false discoveries) may emerge in pairwise GC detection. In addition, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. Therefore, besides using a suitable model order, we recommend a full model over pairwise GC. This is possible if pairwise GC is first used to identify a network of interactions among only a few genes, and then all these interactions are validated with a full model again. If a full model is not possible, we recommend using model validation techniques to remove spurious discoveries. Secondly, we apply pairwise GC with model validation to a real dataset (HeLa). To estimate the model order, the Akaike information criterion is found to be more suitable than the Bayesian information criterion. Degree distribution and network hubs are obtained and compared with previous publications. The hubs tend to act as sources of interactions rather than receivers of interactions.
{"title":"Application of Granger causality to gene regulatory network discovery","authors":"G. Tam, Chunqi Chang, Y. Hung","doi":"10.1109/ISB.2012.6314142","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314142","url":null,"abstract":"Granger causality (GC) has been applied to gene regulatory network discovery using DNA microarray time-series data. Since the number of genes is much larger than the data length, a full model cannot be applied in a straightforward manner, hence GC is often applied to genes pairwisely. In this paper, firstly we investigate with synthetic data and point out how spurious causalities (false discoveries) may emerge in pairwise GC detection. In addition, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. Therefore, besides using a suitable model order, we recommend a full model over pairwise GC. This is possible if pairwise GC is first used to identify a network of interactions among only a few genes, and then all these interactions are validated with a full model again. If a full model is not possible, we recommend using model validation techniques to remove spurious discoveries. Secondly, we apply pairwise GC with model validation to a real dataset (HeLa). To estimate the model order, the Akaike information criterion is found to be more suitable than the Bayesian information criterion. Degree distribution and network hubs are obtained and compared with previous publications. The hubs tend to act as sources of interactions rather than receivers of interactions.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"33 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":"121682217","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.2012.6314105
Liwei Qian, Hao-ran Zheng
Drug therapy to patients is often with partial success, and has been associated with a number of adverse reactions. Prediction of patients' response to therapy at the early stage of the treatment is crucial to avoiding those unnecessary adverse reactions. In this paper, a new approach that integrates time series gene expression and Protein Protein Interaction (PPI) network is presented to improve the prediction of patients' response to drug therapy. Experimental results showed that our method outperformed previous approaches. The method proposed here offers a huge potential for applications in pharmacogenomics and medicine.
对患者的药物治疗通常是部分成功的，并且与一些不良反应有关。在治疗早期预测患者对治疗的反应对于避免不必要的不良反应至关重要。本文提出了一种将时间序列基因表达与蛋白蛋白相互作用(Protein Protein Interaction, PPI)网络相结合的新方法，以提高对患者药物治疗反应的预测。实验结果表明，我们的方法优于以往的方法。本文提出的方法在药物基因组学和医学领域具有巨大的应用潜力。
{"title":"Improving prediction of drug therapy outcome via integration of time series gene expression and Protein Protein Interaction network","authors":"Liwei Qian, Hao-ran Zheng","doi":"10.1109/ISB.2012.6314105","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314105","url":null,"abstract":"Drug therapy to patients is often with partial success, and has been associated with a number of adverse reactions. Prediction of patients' response to therapy at the early stage of the treatment is crucial to avoiding those unnecessary adverse reactions. In this paper, a new approach that integrates time series gene expression and Protein Protein Interaction (PPI) network is presented to improve the prediction of patients' response to drug therapy. Experimental results showed that our method outperformed previous approaches. The method proposed here offers a huge potential for applications in pharmacogenomics and medicine.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"54 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":"128662323","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.2012.6314156
Xinxin Wang, Zhana Duren, Chao Zhang, Lin Chen, Yong Wang
Gastric cancer is the fourth most common cancer and second leading cause of cancer-related death worldwide. Nowadays the accumulated large scale clinical data allows the clinicopathlogical review to identify the clinical factors, reveal their possible correlations, and mine the possible clinical patterns for gastric cancer. Here we analyze the clinical data of over 1500 gastric cancer patients histopathologically diagnosed and treated during 2006 to 2010. Specifically, we collect and preprocess the data by extracting 14 available clinical factors from three categories, i.e., the clinical background, immunohistochemistry data, and the caner's stage information. Then these factors are quantized and the significant factors and their correlations are calculated. Importantly, we define a distance between two patients by their clinical factors profile similarity and cluster all the patients into subgroups. We find that most of the patients fall into three major classes and we define them as three subtypes of gastric cancer. Each subtype is analyzed and characterized by its own significant factors and correlations. Our analysis may provide important insights for gastric cancer classification and diagnose.
{"title":"Clinical data analysis reveals three subytpes of gastric cancer","authors":"Xinxin Wang, Zhana Duren, Chao Zhang, Lin Chen, Yong Wang","doi":"10.1109/ISB.2012.6314156","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314156","url":null,"abstract":"Gastric cancer is the fourth most common cancer and second leading cause of cancer-related death worldwide. Nowadays the accumulated large scale clinical data allows the clinicopathlogical review to identify the clinical factors, reveal their possible correlations, and mine the possible clinical patterns for gastric cancer. Here we analyze the clinical data of over 1500 gastric cancer patients histopathologically diagnosed and treated during 2006 to 2010. Specifically, we collect and preprocess the data by extracting 14 available clinical factors from three categories, i.e., the clinical background, immunohistochemistry data, and the caner's stage information. Then these factors are quantized and the significant factors and their correlations are calculated. Importantly, we define a distance between two patients by their clinical factors profile similarity and cluster all the patients into subgroups. We find that most of the patients fall into three major classes and we define them as three subtypes of gastric cancer. Each subtype is analyzed and characterized by its own significant factors and correlations. Our analysis may provide important insights for gastric cancer classification and diagnose.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"11 3 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":"128529798","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.2012.6314134
Li-Ping Tian, Zhong-ke Shi, Fang-Xiang Wu
The study of the global stability is essential for designing and controlling genetic regulatory networks. Most existing results on this issue are based on linear matrix inequality (LMI) approach, which results in checking the existence of feasible solutions to high dimensional LMIs. In our previous study, we present several stability conditions for genetic regulatory networks with time-varying delays, based on M-matrix theory and the non-smooth Lyapunov function. In this paper, we design a smooth Lyapunov function and employ M-matrix theory to derive new stability conditions for genetic regulatory networks with time-varying delays. Theoretically, these conditions are less conservative than existing ones in some cases. For genetic regulatory networks with n genes and n proteins, these conditions become to check if an n×n matrix is an M-matrix, which is much easier than existing results. To illustrate the effectiveness of our theoretical results, two genetic regulatory networks are analyzed.
{"title":"New global stability conditions for genetic regulatory networks with time-varying delays","authors":"Li-Ping Tian, Zhong-ke Shi, Fang-Xiang Wu","doi":"10.1109/ISB.2012.6314134","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314134","url":null,"abstract":"The study of the global stability is essential for designing and controlling genetic regulatory networks. Most existing results on this issue are based on linear matrix inequality (LMI) approach, which results in checking the existence of feasible solutions to high dimensional LMIs. In our previous study, we present several stability conditions for genetic regulatory networks with time-varying delays, based on M-matrix theory and the non-smooth Lyapunov function. In this paper, we design a smooth Lyapunov function and employ M-matrix theory to derive new stability conditions for genetic regulatory networks with time-varying delays. Theoretically, these conditions are less conservative than existing ones in some cases. For genetic regulatory networks with n genes and n proteins, these conditions become to check if an n×n matrix is an M-matrix, which is much easier than existing results. To illustrate the effectiveness of our theoretical results, two genetic regulatory networks are analyzed.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"37 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":"128622802","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.2012.6314140
Qiang Huang, Ling-Yun Wu, Xiang-Sun Zhang
Due to the rapid progress of high-throughput techniques in past decade, a lot of biomolecular networks are constructed and collected in various databases. However, the biological functional annotations to networks do not keep up with the pace. Network alignment is a fundamental and important bioinformatics approach for predicting functional annotations and discovering conserved functional modules. Although many methods were developed to address the network alignment problem, it is not solved satisfactorily. In this paper, we propose a novel network alignment method called CNetA, which is based on the conditional random field model. The new method is compared with other four methods on three real protein-protein interaction (PPI) network pairs by using four structural and five biological criteria. Compared with structure-dominated methods, larger biological conserved subnetworks are found, while compared with the node-dominated methods, larger connected subnetworks are found. In a word, CNetA preferably balances the biological and topological similarities.
{"title":"CNetA: Network alignment by combining biological and topological features","authors":"Qiang Huang, Ling-Yun Wu, Xiang-Sun Zhang","doi":"10.1109/ISB.2012.6314140","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314140","url":null,"abstract":"Due to the rapid progress of high-throughput techniques in past decade, a lot of biomolecular networks are constructed and collected in various databases. However, the biological functional annotations to networks do not keep up with the pace. Network alignment is a fundamental and important bioinformatics approach for predicting functional annotations and discovering conserved functional modules. Although many methods were developed to address the network alignment problem, it is not solved satisfactorily. In this paper, we propose a novel network alignment method called CNetA, which is based on the conditional random field model. The new method is compared with other four methods on three real protein-protein interaction (PPI) network pairs by using four structural and five biological criteria. Compared with structure-dominated methods, larger biological conserved subnetworks are found, while compared with the node-dominated methods, larger connected subnetworks are found. In a word, CNetA preferably balances the biological and topological similarities.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"5 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":"131979848","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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