Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470300
Marco Mina, P. Guzzi
Evolutionary analysis and comparison of biological networks may result in the identification of conserved mechanism between species as well as conserved modules, such as protein complexes and pathways. Following an holistic philosophy several algorithms, known as network alignment algorithms, have been proposed recently as counterpart of sequence and structure alignment algorithms, to unravel relations between different species at the interactome level. In this work we present AlignMCL, a local alignment algorithm for the identification of conserved subnetworks in different species. As many other existing tools, AlignMCL is based on the idea of merging many protein interaction networks in a single alignment graph and subsequently mining it to identify potentially conserved subnetworks. In order to asses AlignMCL we compared it to the state of the art local alignment algorithms over a rather extensive and updated dataset. Finally, to improve the usability of our tool we developed a Cytoscape plugin, AlignMCL, that offers a graphical user interface to an MCL engine.
{"title":"AlignMCL: Comparative analysis of protein interaction networks through Markov clustering","authors":"Marco Mina, P. Guzzi","doi":"10.1109/BIBMW.2012.6470300","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470300","url":null,"abstract":"Evolutionary analysis and comparison of biological networks may result in the identification of conserved mechanism between species as well as conserved modules, such as protein complexes and pathways. Following an holistic philosophy several algorithms, known as network alignment algorithms, have been proposed recently as counterpart of sequence and structure alignment algorithms, to unravel relations between different species at the interactome level. In this work we present AlignMCL, a local alignment algorithm for the identification of conserved subnetworks in different species. As many other existing tools, AlignMCL is based on the idea of merging many protein interaction networks in a single alignment graph and subsequently mining it to identify potentially conserved subnetworks. In order to asses AlignMCL we compared it to the state of the art local alignment algorithms over a rather extensive and updated dataset. Finally, to improve the usability of our tool we developed a Cytoscape plugin, AlignMCL, that offers a graphical user interface to an MCL engine.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75514376","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 : 2012-10-04DOI: 10.1109/BIBM.2012.6392672
Jingyao Li, D. Lin, Hongbao Cao, Yu-ping Wang
We developed a structure based sparse representation model for classifying chromosomes in M-FISH images. The sparse representation based classification model used in our previous work only considered one pixel without incorporating any structural information. The new proposed model extends the previous one to multiple pixels case, where each target pixel together with its neighboring pixels will be used simultaneously for classification. We also extend Orthogonal Matching Pursuit (OMP) algorithm to the multiple pixels case, named simultaneous OMP algorithm (SOMP), to solve the structure based sparse representation model. The classification results show that our new model outperforms the previous sparse representation model with the p-value less than le-6. We also discussed the effects of several parameters (neighborhood size, sparsity level, and training sample size) on the accuracy of the classification. Our proposed method can be affected by the sparsity level and the neighborhood size but is insensitive to the training sample size. Therefore, the comparison indicates that the structure based sparse representation model can significantly improve the accuracy of the chromosome classification, leading to improved diagnosis of genetic diseases and cancers.
{"title":"Classification of multicolor fluorescence in-situ hybridization (M-FISH) image using structure based sparse representation model","authors":"Jingyao Li, D. Lin, Hongbao Cao, Yu-ping Wang","doi":"10.1109/BIBM.2012.6392672","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392672","url":null,"abstract":"We developed a structure based sparse representation model for classifying chromosomes in M-FISH images. The sparse representation based classification model used in our previous work only considered one pixel without incorporating any structural information. The new proposed model extends the previous one to multiple pixels case, where each target pixel together with its neighboring pixels will be used simultaneously for classification. We also extend Orthogonal Matching Pursuit (OMP) algorithm to the multiple pixels case, named simultaneous OMP algorithm (SOMP), to solve the structure based sparse representation model. The classification results show that our new model outperforms the previous sparse representation model with the p-value less than le-6. We also discussed the effects of several parameters (neighborhood size, sparsity level, and training sample size) on the accuracy of the classification. Our proposed method can be affected by the sparsity level and the neighborhood size but is insensitive to the training sample size. Therefore, the comparison indicates that the structure based sparse representation model can significantly improve the accuracy of the chromosome classification, leading to improved diagnosis of genetic diseases and cancers.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74221870","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 : 2012-10-04DOI: 10.1109/BIBMW.2012.6470206
Jian Wang, Qian Xu, Hongfei Lin, Zhihao Yang, Yanpeng Li
In biomédical event extraction domain, there is a small amount of labeled data along with a large pool of unlabeled data. Many supervised learning algorithms for bio-event extraction have been affected by the data sparseness. In this paper, we present a new solution to perform biomédical event extraction from scientific documents, applying a semi-supervised approach to extract features from unlabeled data using labeled data features as a reference. This strategy is evaluated via experiments in which the data from the BioNLP2011 and PubMed are applied. To the best of our knowledge, it is the first time that the combination of labeled and unlabeled data are used for biomédical event extraction and our experimental results demonstrate the state-of-the-art performance in this task.
{"title":"Combining labeled and unlabeled data for biomédical event extraction","authors":"Jian Wang, Qian Xu, Hongfei Lin, Zhihao Yang, Yanpeng Li","doi":"10.1109/BIBMW.2012.6470206","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470206","url":null,"abstract":"In biomédical event extraction domain, there is a small amount of labeled data along with a large pool of unlabeled data. Many supervised learning algorithms for bio-event extraction have been affected by the data sparseness. In this paper, we present a new solution to perform biomédical event extraction from scientific documents, applying a semi-supervised approach to extract features from unlabeled data using labeled data features as a reference. This strategy is evaluated via experiments in which the data from the BioNLP2011 and PubMed are applied. To the best of our knowledge, it is the first time that the combination of labeled and unlabeled data are used for biomédical event extraction and our experimental results demonstrate the state-of-the-art performance in this task.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78733738","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 : 2012-10-04DOI: 10.1109/BIBM.2012.6392631
Qiang Lou, Z. Obradovic
In order to more accurately predict an individual's health status, in clinical applications it is often important to perform analysis of high-dimensional gene expression data that varies with time. A major challenge in predicting from such temporal microarray data is that the number of biomarkers used as features is typically much larger than the number of labeled subjects. One way to address this challenge is to perform feature selection as a preprocessing step and then apply a classification method on selected features. However, traditional feature selection methods cannot handle multivariate temporal data without applying techniques that flatten temporal data into a single matrix in advance. In this study, a feature selection filter that can directly select informative features from temporal gene expression data is proposed. In our approach we measure the distance between multivariate temporal data from two subjects. Based on this distance, we define the objective function of temporal margin based feature selection to maximize each subject's temporal margin in its own relevant subspace. The experimental results on two real flu data sets provide evidence that our method outperforms the alternatives, which flatten the temporal data in advance.
{"title":"Predicting viral infection by selecting informative biomarkers from temporal high-dimensional gene expression data","authors":"Qiang Lou, Z. Obradovic","doi":"10.1109/BIBM.2012.6392631","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392631","url":null,"abstract":"In order to more accurately predict an individual's health status, in clinical applications it is often important to perform analysis of high-dimensional gene expression data that varies with time. A major challenge in predicting from such temporal microarray data is that the number of biomarkers used as features is typically much larger than the number of labeled subjects. One way to address this challenge is to perform feature selection as a preprocessing step and then apply a classification method on selected features. However, traditional feature selection methods cannot handle multivariate temporal data without applying techniques that flatten temporal data into a single matrix in advance. In this study, a feature selection filter that can directly select informative features from temporal gene expression data is proposed. In our approach we measure the distance between multivariate temporal data from two subjects. Based on this distance, we define the objective function of temporal margin based feature selection to maximize each subject's temporal margin in its own relevant subspace. The experimental results on two real flu data sets provide evidence that our method outperforms the alternatives, which flatten the temporal data in advance.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78249469","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 : 2012-10-04DOI: 10.1109/BIBMW.2012.6470313
Saurabh Shirgaonkar, D. Jeong, T. Huynh, Soo-Yeon Ji
Approximately 7 million people each year in the world suffer from brain injuries caused by motor vehicle accidents, falls, or assaults. Thus, correctly identifying bleeding in the brain is critical to make fast and reliable treatments and diagnostic decisions for proving better cares to brain injury patients. Although it is very challenging to detect bleeding areas in low resolution Computed Tomography (CT) images having complex bleeding patterns, developing an automated detection method can significantly help physicians understand bleeding patterns and determine the severity of brain injuries. In this paper, we propose a fast and robust hybrid method to detect bleeding areas on clinical brain CT images. Specifically, our proposed method follows several steps to segment bleeding areas in brain CT images as eliminating noise, detecting and separating skull regions, applying a combined approach of histogram and a modified global thresholding. By applying our approach to 30 brain CT image, we found the accuracy of 90% in identifying bleeding areas correctly.
{"title":"Designing a robust bleeding detection method for brain CT image analysis","authors":"Saurabh Shirgaonkar, D. Jeong, T. Huynh, Soo-Yeon Ji","doi":"10.1109/BIBMW.2012.6470313","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470313","url":null,"abstract":"Approximately 7 million people each year in the world suffer from brain injuries caused by motor vehicle accidents, falls, or assaults. Thus, correctly identifying bleeding in the brain is critical to make fast and reliable treatments and diagnostic decisions for proving better cares to brain injury patients. Although it is very challenging to detect bleeding areas in low resolution Computed Tomography (CT) images having complex bleeding patterns, developing an automated detection method can significantly help physicians understand bleeding patterns and determine the severity of brain injuries. In this paper, we propose a fast and robust hybrid method to detect bleeding areas on clinical brain CT images. Specifically, our proposed method follows several steps to segment bleeding areas in brain CT images as eliminating noise, detecting and separating skull regions, applying a combined approach of histogram and a modified global thresholding. By applying our approach to 30 brain CT image, we found the accuracy of 90% in identifying bleeding areas correctly.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78384871","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 : 2012-10-04DOI: 10.1109/BIBMW.2012.6470205
M. Elbashir, Jianxin Wang, Fang Wu
A β-turn is a secondary protein structure type that plays a significant role in protein folding, stability, and molecular recognition. It is the most common type of non-repetitive structures. On average 25% of amino acids in protein structures are located in β-turns. In this paper, we propose a hybrid approach of support vector machines (SVMs) with logistic regression (LR) for β-turn prediction. In this hybrid approach, the non β-turn class in a training set is under-sampled several times and combined with the β-turn class to create a number of balanced sets. Each balanced set is used for training one SVM at a time. The results of the SVMs are aggregated by using a logistic regression model. By adopting this hybrid approach, we cannot only avoid the difficulty of imbalanced data, but also have outputs with probability, and less ambiguous than combining SVM with other methods such as voting. Our simulation studies on BT426, and other datasets show that this hybrid approach achieves favorable performance in predicting β-turns as measured by the Matthew correlation coefficient (MCC) when compared with other competing methods.
{"title":"A hybrid approach of support vector machines with logistic regression for β-turn prediction","authors":"M. Elbashir, Jianxin Wang, Fang Wu","doi":"10.1109/BIBMW.2012.6470205","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470205","url":null,"abstract":"A β-turn is a secondary protein structure type that plays a significant role in protein folding, stability, and molecular recognition. It is the most common type of non-repetitive structures. On average 25% of amino acids in protein structures are located in β-turns. In this paper, we propose a hybrid approach of support vector machines (SVMs) with logistic regression (LR) for β-turn prediction. In this hybrid approach, the non β-turn class in a training set is under-sampled several times and combined with the β-turn class to create a number of balanced sets. Each balanced set is used for training one SVM at a time. The results of the SVMs are aggregated by using a logistic regression model. By adopting this hybrid approach, we cannot only avoid the difficulty of imbalanced data, but also have outputs with probability, and less ambiguous than combining SVM with other methods such as voting. Our simulation studies on BT426, and other datasets show that this hybrid approach achieves favorable performance in predicting β-turns as measured by the Matthew correlation coefficient (MCC) when compared with other competing methods.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78180897","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}
Radiculopathy is the chief pattern of cervical spondylosis (CS) characterized by neck pain, numbness, stiffness and radicular pain to the arms and fingers. Nowadays acupuncture is a complementary therapy for radiculopathy which is regarded as one of most effective, widely used and well-accepted method. However, most of the classic acupuncture is only taken by the proximal and local acupoints while distal acupoints along meridians is another empirical option, which considered as the point-selection treatment based on syndrome differentiation. In this paper, we present a research protocol designed for a single-parallel, randomized, controlled trial to evaluate the effect of the distal point acupuncture treatment for radiculopathy. In our study, the objective is to evaluate the clinical effect of the distal point acupuncture treatment along meridians compared with the classic acupuncture treatment by proximal and local acupoints.
{"title":"Distal point acupuncture for cervical spondylosis with radiculopathy based on flow of meridians: A research protocol for clinical trial","authors":"Ziping Li, Fushan Qiu, Lingfeng Zeng, Zhaohui Liang, Yanyan Huang","doi":"10.1109/BIBMW.2012.6470325","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470325","url":null,"abstract":"Radiculopathy is the chief pattern of cervical spondylosis (CS) characterized by neck pain, numbness, stiffness and radicular pain to the arms and fingers. Nowadays acupuncture is a complementary therapy for radiculopathy which is regarded as one of most effective, widely used and well-accepted method. However, most of the classic acupuncture is only taken by the proximal and local acupoints while distal acupoints along meridians is another empirical option, which considered as the point-selection treatment based on syndrome differentiation. In this paper, we present a research protocol designed for a single-parallel, randomized, controlled trial to evaluate the effect of the distal point acupuncture treatment for radiculopathy. In our study, the objective is to evaluate the clinical effect of the distal point acupuncture treatment along meridians compared with the classic acupuncture treatment by proximal and local acupoints.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80142257","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 : 2012-10-04DOI: 10.1109/BIBM.2012.6392664
Hang T. T. Phan, M. Sternberg, E. Gelenbe
We have developed RNNI, a global alignment method for protein-protein interaction networks between species, using a random neural network model (RNN) tailored for the alignment problem. The benchmark of the method in comparison with other available alignment approaches was performed using a range of measurements. The alignment results of the human and yeast pair showed that RNNI is capable of generating alignments with large conserved networks with functionally-related protein pairs while maintaining the closeness to the naive- sequence homology approach (BLAST).
{"title":"Aligning protein-protein interaction networks using random neural networks","authors":"Hang T. T. Phan, M. Sternberg, E. Gelenbe","doi":"10.1109/BIBM.2012.6392664","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392664","url":null,"abstract":"We have developed RNNI, a global alignment method for protein-protein interaction networks between species, using a random neural network model (RNN) tailored for the alignment problem. The benchmark of the method in comparison with other available alignment approaches was performed using a range of measurements. The alignment results of the human and yeast pair showed that RNNI is capable of generating alignments with large conserved networks with functionally-related protein pairs while maintaining the closeness to the naive- sequence homology approach (BLAST).","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80411866","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 : 2012-10-04DOI: 10.1109/BIBM.2012.6392652
Yuanzhe Bei, Pengyu Hong
False discovery rate (FDR) control is widely practiced to correct for multiple comparisons in selecting statistically significant features from genome-wide datasets. In this paper, we present an advanced significance analysis method called miFDR that minimizes FDR when the number of the required significant features is fixed. We compared our approach with other well-known significance analysis approaches such as Significance Analysis of Microarrays [1-3], the Benjamini-Hochberg approach [4] and the Storey approach [5]. The results of using both simulated data sets and public microarray data sets demonstrated that miFDR is more powerful.
{"title":"Significance analysis by minimizing false discovery rate","authors":"Yuanzhe Bei, Pengyu Hong","doi":"10.1109/BIBM.2012.6392652","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392652","url":null,"abstract":"False discovery rate (FDR) control is widely practiced to correct for multiple comparisons in selecting statistically significant features from genome-wide datasets. In this paper, we present an advanced significance analysis method called miFDR that minimizes FDR when the number of the required significant features is fixed. We compared our approach with other well-known significance analysis approaches such as Significance Analysis of Microarrays [1-3], the Benjamini-Hochberg approach [4] and the Storey approach [5]. The results of using both simulated data sets and public microarray data sets demonstrated that miFDR is more powerful.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79122479","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 : 2012-10-04DOI: 10.1109/BIBMW.2012.6470296
O. Ghasemi, Nguyen T. Nguyen, Trevi A. Ramirez, Jianhua Zhang, M. Lindsey, Yufang Jin
Extracellular matrix (ECM) remodeling is an important process to determine the functional and geometric changes of the left ventricle (LV) post-myocardial infarction (MI). Currently, little research has been performed to determine key factors associated with extracellular matrix remodeling post-ML We have collected the expression levels of 84 genes in LV extracellular matrix from wild type C57BL/6J mice at day 0 (control group), day 28 (MI saline group), and day 28 MI groups treated with Aliskiren, Valsartan, and a combination of these two drugs, given from 3 h post-MI (number=6 each group). Further, we have categorized these genes using sparse singular value decomposition (SSVD) based biclustering algorithm with measurement noises considered. Our results identified the 10 most significant genes in the infarct region, and these genes were cadherin-1, collagen I and IL connective tissue growth factor, matrix metalloproteinase-3, neural cell adhesion molecule-2, osteopontin, thrombospondin-1, Tissue inhibitor of metallopreteinases-1, and tenascin C. We also identified the 15 most significant genes in the non-infarct region, which shared 6 significant genes with the infarct region (collagen IL connective tissue growth factor, matrix metalloproteinase-3, osteopontin, thrombospondin-1, and tenascin C). We then analyzed pathways enriched by the identified significant genes. Interestingly, cell death and adhesion pathways were the most significant functions identified in the infarct region while cell adhesion, cell migration, and inflammatory pathways were enriched in non-infarct region, suggesting their effect on the LV remodeling process. Our results provide a rationale for future research that target these pathways.
{"title":"A biclustering approach to analyze drug effects on extracellular matrix remodeling post-myocardial infarction","authors":"O. Ghasemi, Nguyen T. Nguyen, Trevi A. Ramirez, Jianhua Zhang, M. Lindsey, Yufang Jin","doi":"10.1109/BIBMW.2012.6470296","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470296","url":null,"abstract":"Extracellular matrix (ECM) remodeling is an important process to determine the functional and geometric changes of the left ventricle (LV) post-myocardial infarction (MI). Currently, little research has been performed to determine key factors associated with extracellular matrix remodeling post-ML We have collected the expression levels of 84 genes in LV extracellular matrix from wild type C57BL/6J mice at day 0 (control group), day 28 (MI saline group), and day 28 MI groups treated with Aliskiren, Valsartan, and a combination of these two drugs, given from 3 h post-MI (number=6 each group). Further, we have categorized these genes using sparse singular value decomposition (SSVD) based biclustering algorithm with measurement noises considered. Our results identified the 10 most significant genes in the infarct region, and these genes were cadherin-1, collagen I and IL connective tissue growth factor, matrix metalloproteinase-3, neural cell adhesion molecule-2, osteopontin, thrombospondin-1, Tissue inhibitor of metallopreteinases-1, and tenascin C. We also identified the 15 most significant genes in the non-infarct region, which shared 6 significant genes with the infarct region (collagen IL connective tissue growth factor, matrix metalloproteinase-3, osteopontin, thrombospondin-1, and tenascin C). We then analyzed pathways enriched by the identified significant genes. Interestingly, cell death and adhesion pathways were the most significant functions identified in the infarct region while cell adhesion, cell migration, and inflammatory pathways were enriched in non-infarct region, suggesting their effect on the LV remodeling process. Our results provide a rationale for future research that target these pathways.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81737783","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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