Yanqiao Zhu, Fuhai Li, D. Cridebring, Jinwen Ma, Stephen T. C. Wong, T. Vadakkan, Mei Zhang, John D. Landua, Wei Wei, M. Dickinson, J. Rosen, M. Lewis
Interactions between cancer cells and factors within the tumor microenvironment (mE) are essential for understanding tumor development. The spatial relationships between blood vessel cells and cancer cells, e.g. tumor initiating cells (TICs), are an important parameter. Accurate segmentation of blood vessel is necessary for the quantization of their spatial relationships. However, this remains an open problem due to uneven intensity and low signal to noise ratio (SNR). To overcome these challenges, we propose a novel approach that integrates an oriented hidden Markov random field model (Ori-HMRF) with local clustering. The local clustering delineates boundaries of blood vessel segments with low SNR. Then blood vessel segments are viewed as random variables in the Ori-HMRF and their spatial dependence is defined based on directional information. The Ori-HMRF model suppresses noise and generates accurate blood vessel segmentation results. Experimental validations were conducted on both normal mammary and breast cancer tissues.
{"title":"Coupling Oriented Hidden Markov Random Field Model with Local Clustering for Segmenting Blood Vessels and Measuring Spatial Structures in Images of Tumor Microenvironment","authors":"Yanqiao Zhu, Fuhai Li, D. Cridebring, Jinwen Ma, Stephen T. C. Wong, T. Vadakkan, Mei Zhang, John D. Landua, Wei Wei, M. Dickinson, J. Rosen, M. Lewis","doi":"10.1109/BIBM.2011.104","DOIUrl":"https://doi.org/10.1109/BIBM.2011.104","url":null,"abstract":"Interactions between cancer cells and factors within the tumor microenvironment (mE) are essential for understanding tumor development. The spatial relationships between blood vessel cells and cancer cells, e.g. tumor initiating cells (TICs), are an important parameter. Accurate segmentation of blood vessel is necessary for the quantization of their spatial relationships. However, this remains an open problem due to uneven intensity and low signal to noise ratio (SNR). To overcome these challenges, we propose a novel approach that integrates an oriented hidden Markov random field model (Ori-HMRF) with local clustering. The local clustering delineates boundaries of blood vessel segments with low SNR. Then blood vessel segments are viewed as random variables in the Ori-HMRF and their spatial dependence is defined based on directional information. The Ori-HMRF model suppresses noise and generates accurate blood vessel segmentation results. Experimental validations were conducted on both normal mammary and breast cancer tissues.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"1 1","pages":"352-357"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80148122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112358
Zoya Dimitrova, David S. Campo, S. Ramachandran, Gilberto Vaughan, L. Ganova-Raeva, Yulin Lin, J. Forbi, G. Xia, P. Skums, B. Pearlman, Y. Khudyakov
Recent advances in sequencing methods allow the analysis of an unprecedented number of viral variants from infected patients and present a novel opportunity for understanding viral evolution, drug resistance and immune escape. In the present paper, we compared three technologies for amplicon analysis: (i) Next Generation Sequencing; (ii) Clonal sequencing using End-point Limiting-dilution for isolation of individual sequence variants followed by Real-Time PCR and sequencing; and (iii) Mass spectrometry of base-specific cleavage reactions of a target sequence. Hypervariable region 1 of hepatitis C virus was analyzed using these three technologies to assess diversity and genetic relatedness of intra-host viral populations in specimens obtained from 38 patients. Estimates of population heterogeneity varied among technologies. However, all three technologies were equally accurate in identification of genetic relatedness among viral strains, supporting their application in molecular epidemiology for tracking viral variants and detecting transmission events.
{"title":"Assessments of intra- and inter-host diversity of hepatitis C virus using Next Generation Sequencing and Mass spectrometry","authors":"Zoya Dimitrova, David S. Campo, S. Ramachandran, Gilberto Vaughan, L. Ganova-Raeva, Yulin Lin, J. Forbi, G. Xia, P. Skums, B. Pearlman, Y. Khudyakov","doi":"10.1109/BIBMW.2011.6112358","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112358","url":null,"abstract":"Recent advances in sequencing methods allow the analysis of an unprecedented number of viral variants from infected patients and present a novel opportunity for understanding viral evolution, drug resistance and immune escape. In the present paper, we compared three technologies for amplicon analysis: (i) Next Generation Sequencing; (ii) Clonal sequencing using End-point Limiting-dilution for isolation of individual sequence variants followed by Real-Time PCR and sequencing; and (iii) Mass spectrometry of base-specific cleavage reactions of a target sequence. Hypervariable region 1 of hepatitis C virus was analyzed using these three technologies to assess diversity and genetic relatedness of intra-host viral populations in specimens obtained from 38 patients. Estimates of population heterogeneity varied among technologies. However, all three technologies were equally accurate in identification of genetic relatedness among viral strains, supporting their application in molecular epidemiology for tracking viral variants and detecting transmission events.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"30 1","pages":"79-86"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85015613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112382
Hui Li, Chunmei Liu, M. Rwebangira, L. Burge, W. Southerland
Identifying post-translational modifications (PTMs) is a big challenge for proteomics. Mass spectrometry is a popular tool for the identification of peptide sequence. Here we present a method for rapid identification of PTMs by mass spectrometry based on graph theory. The approach takes advantage of several possibility pair's values of corresponding Tandem Mass Spectrometry (MS/MS), and uses score function to screen candidate peptide sequences. We proposed the Pair Peak of Set (PPS) and used the most possibility mass of PPS as the root of graph tree, the rest ones are viewed as the reference node of graph. Our experiment on 2620 experimental MS/MS with two PTMs shows that our approach achieves better accuracy than PepNovo approaches with higher efficiency and it could deal with low quality PTMs data.
{"title":"Rapid identification of multi-PTMs peptide sequence tags with a graph search approach","authors":"Hui Li, Chunmei Liu, M. Rwebangira, L. Burge, W. Southerland","doi":"10.1109/BIBMW.2011.6112382","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112382","url":null,"abstract":"Identifying post-translational modifications (PTMs) is a big challenge for proteomics. Mass spectrometry is a popular tool for the identification of peptide sequence. Here we present a method for rapid identification of PTMs by mass spectrometry based on graph theory. The approach takes advantage of several possibility pair's values of corresponding Tandem Mass Spectrometry (MS/MS), and uses score function to screen candidate peptide sequences. We proposed the Pair Peak of Set (PPS) and used the most possibility mass of PPS as the root of graph tree, the rest ones are viewed as the reference node of graph. Our experiment on 2620 experimental MS/MS with two PTMs shows that our approach achieves better accuracy than PepNovo approaches with higher efficiency and it could deal with low quality PTMs data.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"94 1","pages":"247-250"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85037493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112403
A. Mucherino, C. Lavor, Leo Liberti
Branch & Prune (BP) is a deterministic algorithm for the solution of the Discretizable Molecular Distance Geometry Problem (DMDGP). This problem has important applications in the field of structural biology, in particular for the determination of the three-dimensional conformation of a molecule by using information obtained by NMR techniques. In recent works, we proved that the search domain of the DMDGP, which is represented by a binary tree, contains various symmetries which are related to the number of solutions to the problem. In the present work, we propose a variant of the BP algorithm which is able to exploit the information regarding the symmetries to speed up the search. Computational experiments show that the symmetry-driven BP (symBP) outperforms the original BP algorithm in particular when instances having several solutions are considered.
BP (Branch & Prune)是求解可离散分子距离几何问题(DMDGP)的确定性算法。这个问题在结构生物学领域有重要的应用，特别是在利用核磁共振技术获得的信息来确定分子的三维构象方面。在最近的工作中，我们证明了用二叉树表示的DMDGP的搜索域包含各种对称性，这些对称性与问题的解的数量有关。在目前的工作中，我们提出了一种BP算法的变体，该算法能够利用有关对称性的信息来加快搜索速度。计算实验表明，对称驱动BP (symBP)算法在考虑多个解的情况下优于原BP算法。
{"title":"A symmetry-driven BP algorithm for the Discretizable Molecular Distance Geometry Problem","authors":"A. Mucherino, C. Lavor, Leo Liberti","doi":"10.1109/BIBMW.2011.6112403","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112403","url":null,"abstract":"Branch & Prune (BP) is a deterministic algorithm for the solution of the Discretizable Molecular Distance Geometry Problem (DMDGP). This problem has important applications in the field of structural biology, in particular for the determination of the three-dimensional conformation of a molecule by using information obtained by NMR techniques. In recent works, we proved that the search domain of the DMDGP, which is represented by a binary tree, contains various symmetries which are related to the number of solutions to the problem. In the present work, we propose a variant of the BP algorithm which is able to exploit the information regarding the symmetries to speed up the search. Computational experiments show that the symmetry-driven BP (symBP) outperforms the original BP algorithm in particular when instances having several solutions are considered.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"74 1","pages":"390-395"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85793052","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}
Jiandong Ding, Shusi Yu, U. Ohler, J. Guan, Shuigeng Zhou
MiRNA are about 22nt long small noncoding RNAs that post transcription ally regulate gene expression in animals, plants and protozoa. Confident identification of MiRNA-Target Interactions (MTI) is vital to understand their function. Currently, several integrated programs and databases are available for animal miRNAs, the mechanisms of which are significantly different from plant miRNAs. Here we present imiRTP, an integrated MTI prediction and analysis toolkit for Arabidopsis thaliana. It features two important functions: (i) combination of several effective plant miRNA target prediction methods provides a sufficiently large MTI candidate set, and (ii) different filters allow for an efficient selection of potential targets. The modularity of imiRTP enables the prediction of high quality targets on genome-wide scale.
{"title":"imiRTP: An Integrated Method to Identifying miRNA-target Interactions in Arabidopsis thaliana","authors":"Jiandong Ding, Shusi Yu, U. Ohler, J. Guan, Shuigeng Zhou","doi":"10.1109/BIBM.2011.13","DOIUrl":"https://doi.org/10.1109/BIBM.2011.13","url":null,"abstract":"MiRNA are about 22nt long small noncoding RNAs that post transcription ally regulate gene expression in animals, plants and protozoa. Confident identification of MiRNA-Target Interactions (MTI) is vital to understand their function. Currently, several integrated programs and databases are available for animal miRNAs, the mechanisms of which are significantly different from plant miRNAs. Here we present imiRTP, an integrated MTI prediction and analysis toolkit for Arabidopsis thaliana. It features two important functions: (i) combination of several effective plant miRNA target prediction methods provides a sufficiently large MTI candidate set, and (ii) different filters allow for an efficient selection of potential targets. The modularity of imiRTP enables the prediction of high quality targets on genome-wide scale.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"12 1","pages":"100-104"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84046866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112531
Nick V. L. Serão, J. Beever, Dan B. Faulkner, S. Rodriguez-Zas
The lack of consideration of polygenic effects in genome-wide association studies (GWAS) may bias the results in complex traits controlled by multiple genes. The goal of this study is to develop a composite-GWAS model that identifies individual SNPs while adjusting for polygenic effects. The complex trait residual feed intake (RFI), an indicator of the feed efficiency based on maintenance and growth, was modeled. RFI and genotypic data (5,910 SNPs from chromosomes 3, 11 and 24) from 1,387 steers from different breeds and receiving different diets were analyzed, with and without the additive polygenic effect. The model included the fixed effects of days of feed, diet, breed and interaction between diet and breed, and the random effects of contemporary group and additive polygenic effect. A total of 69 and 141 SNPs were detected (P-value < 0.01) with the model including and excluding polygenic effects, respectively. The higher number of SNPs identified by the second model confirms that ignoring polygenic effects in GWAS of multi-gene traits can lead to false positives due to linkage disequilibrium. Seven SNPs (P-value < 0.001), four in chromosomes 3, two in chromosome 11 and one in chromosome 24, were detected using the polygenic model. Two SNPs, one from chromosome 3 and one from 11 are located within coding gene regions. Our results demonstrate the need to use composite-GWAS that include polygenic effects in complex multi-gene traits. These results indicated that the genetic improvement of feed efficiency in beef cattle may be accelerated by the incorporation of these markers in genomic selection strategies.
{"title":"Integration of polygenic and individual SNP effects in genome-wide association analyses","authors":"Nick V. L. Serão, J. Beever, Dan B. Faulkner, S. Rodriguez-Zas","doi":"10.1109/BIBMW.2011.6112531","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112531","url":null,"abstract":"The lack of consideration of polygenic effects in genome-wide association studies (GWAS) may bias the results in complex traits controlled by multiple genes. The goal of this study is to develop a composite-GWAS model that identifies individual SNPs while adjusting for polygenic effects. The complex trait residual feed intake (RFI), an indicator of the feed efficiency based on maintenance and growth, was modeled. RFI and genotypic data (5,910 SNPs from chromosomes 3, 11 and 24) from 1,387 steers from different breeds and receiving different diets were analyzed, with and without the additive polygenic effect. The model included the fixed effects of days of feed, diet, breed and interaction between diet and breed, and the random effects of contemporary group and additive polygenic effect. A total of 69 and 141 SNPs were detected (P-value < 0.01) with the model including and excluding polygenic effects, respectively. The higher number of SNPs identified by the second model confirms that ignoring polygenic effects in GWAS of multi-gene traits can lead to false positives due to linkage disequilibrium. Seven SNPs (P-value < 0.001), four in chromosomes 3, two in chromosome 11 and one in chromosome 24, were detected using the polygenic model. Two SNPs, one from chromosome 3 and one from 11 are located within coding gene regions. Our results demonstrate the need to use composite-GWAS that include polygenic effects in complex multi-gene traits. These results indicated that the genetic improvement of feed efficiency in beef cattle may be accelerated by the incorporation of these markers in genomic selection strategies.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"88 1","pages":"985-987"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84179001","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}
Operation with adjuvant chemotherapy is still the principal means to treat Ovarian cancer. Identifying Ovarian Cancer Chemotherapy Response (OCCR) relevant genes and describe their interactions is thus an important issue. However the problems of high dimensional micro array data and the scarcity of biological priors make building a complete OCCR biological network intractable. To this end, we combine liquid association (LA) algorithm with biological knowledgebase searching to identify OCCR relevant gene clique and describe their interactions. Rather than trying to build a gene network, our approach focus on identifying OCCR relevant gene cliques and then patching them up. Statistical analysis and biological validation show that the identified gene cliques play important roles in tumor genesis, immunity, cells proliferation and migration etc and significantly OCCR relevant. More importantly, the connection of independent gene cliques is established and the associations of genes are described. Methodologically, the proposed method avoids the problem of complex computation, relies only on available biological priors and provides a novel way to build gene network.
{"title":"Identifying Ovarian Cancer Chemotherapy Response Relevant Gene Cliques","authors":"Yan-E. Li, Juan Zhang, Bin Han, Lihua Li","doi":"10.1109/BIBM.2011.65","DOIUrl":"https://doi.org/10.1109/BIBM.2011.65","url":null,"abstract":"Operation with adjuvant chemotherapy is still the principal means to treat Ovarian cancer. Identifying Ovarian Cancer Chemotherapy Response (OCCR) relevant genes and describe their interactions is thus an important issue. However the problems of high dimensional micro array data and the scarcity of biological priors make building a complete OCCR biological network intractable. To this end, we combine liquid association (LA) algorithm with biological knowledgebase searching to identify OCCR relevant gene clique and describe their interactions. Rather than trying to build a gene network, our approach focus on identifying OCCR relevant gene cliques and then patching them up. Statistical analysis and biological validation show that the identified gene cliques play important roles in tumor genesis, immunity, cells proliferation and migration etc and significantly OCCR relevant. More importantly, the connection of independent gene cliques is established and the associations of genes are described. Methodologically, the proposed method avoids the problem of complex computation, relies only on available biological priors and provides a novel way to build gene network.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"51 1","pages":"294-298"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77817478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112446
Yang Huang, D. Zisook, Yunan Chen, M. Selter, P. Minardi, J. Mattison
While most research in the NLP domain focuses on information accuracy, the adoption of NLP applications in healthcare extends beyond technical innovations. This study investigates the adoption issues of an NLP application in three different field sites. Using both quantitative log analysis and qualitative user interviews, we identified four main factors that affect NLP adoption: organizational culture and support, system usability, information quality and system reliability. These factors must be considered to ensure successful adoption of NLP applications that provide real-time decision support in a clinical care setting.
{"title":"Lessons learned in improving the adoption of a real-time NLP decision support system","authors":"Yang Huang, D. Zisook, Yunan Chen, M. Selter, P. Minardi, J. Mattison","doi":"10.1109/BIBMW.2011.6112446","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112446","url":null,"abstract":"While most research in the NLP domain focuses on information accuracy, the adoption of NLP applications in healthcare extends beyond technical innovations. This study investigates the adoption issues of an NLP application in three different field sites. Using both quantitative log analysis and qualitative user interviews, we identified four main factors that affect NLP adoption: organizational culture and support, system usability, information quality and system reliability. These factors must be considered to ensure successful adoption of NLP applications that provide real-time decision support in a clinical care setting.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"25 1","pages":"643-648"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82577012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112544
Yiqian Zhou, Jacqueline Gerhart, A. Sacan
Gene regulatory networks provide a powerful abstraction of the complex interactions among genes involved in functional pathways. Experimental determination of these interactions using a classical experimental method, although of extreme value, is laborious and prohibitive at large scales. Over the last decade, a number of computational approaches have been developed to infer gene regulatory networks from high-throughput experimental data. In this study, we introduce a new algorithm for regulatory network inference, based on stepwise multiple regression of time-series microarray data. Compared to other existing methods, our regression-based method provides a clear interpretation of the inferred interactions. The statistical significance associated with each prediction can be utilized to rank the interactions, which is important in prioritization of predictions for further experimental verification. We demonstrate the performance of our approach on a well-known yeast cell cycle pathway and show that it makes more accurate predictions than existing methods.
{"title":"Reconstruction of gene regulatory networks by stepwise multiple linear regression from time-series microarray data","authors":"Yiqian Zhou, Jacqueline Gerhart, A. Sacan","doi":"10.1109/BIBMW.2011.6112544","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112544","url":null,"abstract":"Gene regulatory networks provide a powerful abstraction of the complex interactions among genes involved in functional pathways. Experimental determination of these interactions using a classical experimental method, although of extreme value, is laborious and prohibitive at large scales. Over the last decade, a number of computational approaches have been developed to infer gene regulatory networks from high-throughput experimental data. In this study, we introduce a new algorithm for regulatory network inference, based on stepwise multiple regression of time-series microarray data. Compared to other existing methods, our regression-based method provides a clear interpretation of the inferred interactions. The statistical significance associated with each prediction can be utilized to rank the interactions, which is important in prioritization of predictions for further experimental verification. We demonstrate the performance of our approach on a well-known yeast cell cycle pathway and show that it makes more accurate predictions than existing methods.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"35 1","pages":"1017-1019"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81348306","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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