Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112422
Shuihua Wang, Yingli Tian
Object detection plays a very important role in many applications such as image retrieval, surveillance, robot navigation, wayfinding, etc. In this paper, we propose a novel approach to detect indoor signage to help blind people find their destinations in unfamiliar environments. Our method first extracts the attended areas by using a saliency map. Then the signage is detected in the attended areas by using bipartite graph matching. The proposed method can handle multiple signage detection. Experimental results on our collected indoor signage dataset demonstrate the effectiveness and efficiency of our proposed method. Furthermore, saliency maps could eliminate the interference information and improve the accuracy of the detection results.
{"title":"Indoor signage detection based on saliency map and bipartite graph matching","authors":"Shuihua Wang, Yingli Tian","doi":"10.1109/BIBMW.2011.6112422","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112422","url":null,"abstract":"Object detection plays a very important role in many applications such as image retrieval, surveillance, robot navigation, wayfinding, etc. In this paper, we propose a novel approach to detect indoor signage to help blind people find their destinations in unfamiliar environments. Our method first extracts the attended areas by using a saliency map. Then the signage is detected in the attended areas by using bipartite graph matching. The proposed method can handle multiple signage detection. Experimental results on our collected indoor signage dataset demonstrate the effectiveness and efficiency of our proposed method. Furthermore, saliency maps could eliminate the interference information and improve the accuracy of the detection results.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"22 1","pages":"518-525"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78059896","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.2011.6112501
S. Jadhav, S. Nalbalwar, A. Ghatol
Cardiotocography (CTG) is a simultaneous recording of foetal heart rate (FHR) and uterine contractions (UC) and it is one of the most common diagnostic techniques to evaluate maternal and foetal well-being during pregnancy and before delivery. Assessment of the foetal state can be verified only after delivery using the foetal (newborn) outcome data. One of the most important features defining the abnormal foetal outcome is low birth weight. This paper proposes a multi-class classification algorithm using Modular neural network (MNN) models. It tries to boost two conflicting main objectives of multi-class classifiers: a high correct classification rate level and a high classification rate for each class. Using a Cardiotocography database of normal, suspect and pathological cases, we trained MNN classifiers with 23 real valued diagnostic features collected from total 2126 foetal CTG signal recordings data from UCI Machine Learning Repository. We used the classification in a detection process. The proposed methodology is presented, which then is tested on UCI Cardiotocography unseen testing data sets. Experimental results are promising paving the way for further research in that direction.
{"title":"Modular neural network model based foetal state classification","authors":"S. Jadhav, S. Nalbalwar, A. Ghatol","doi":"10.1109/BIBMW.2011.6112501","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112501","url":null,"abstract":"Cardiotocography (CTG) is a simultaneous recording of foetal heart rate (FHR) and uterine contractions (UC) and it is one of the most common diagnostic techniques to evaluate maternal and foetal well-being during pregnancy and before delivery. Assessment of the foetal state can be verified only after delivery using the foetal (newborn) outcome data. One of the most important features defining the abnormal foetal outcome is low birth weight. This paper proposes a multi-class classification algorithm using Modular neural network (MNN) models. It tries to boost two conflicting main objectives of multi-class classifiers: a high correct classification rate level and a high classification rate for each class. Using a Cardiotocography database of normal, suspect and pathological cases, we trained MNN classifiers with 23 real valued diagnostic features collected from total 2126 foetal CTG signal recordings data from UCI Machine Learning Repository. We used the classification in a detection process. The proposed methodology is presented, which then is tested on UCI Cardiotocography unseen testing data sets. Experimental results are promising paving the way for further research in that direction.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"5 1","pages":"915-917"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78167534","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.2011.6112502
Z. Passand, M. Azarnoosh
This article proposes a technique to improve compression ratio for model-based ECG compression techniques. The proposed technique takes advantage of the quasi-periodic nature of ECG signals and uses differential coding to increase the compression ratio. It is shown that the proposed technique increase the compression ratio by a factor of about two compared to conventional compression ratio for model-based ECG compressions.
{"title":"Improved compression ratio for model-based ECG compression using differential coding","authors":"Z. Passand, M. Azarnoosh","doi":"10.1109/BIBMW.2011.6112502","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112502","url":null,"abstract":"This article proposes a technique to improve compression ratio for model-based ECG compression techniques. The proposed technique takes advantage of the quasi-periodic nature of ECG signals and uses differential coding to increase the compression ratio. It is shown that the proposed technique increase the compression ratio by a factor of about two compared to conventional compression ratio for model-based ECG compressions.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"1 1","pages":"918-918"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78554878","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.2011.6112454
Sungyoung Lee, Min-Seok Kwon, Iksoo Huh, T. Park
In genome-wide association studies (GWAS), logistic regression (LR) has been most commonly used for finding an association between a disease phenotype and genetic variants such as single nucleotide polymorphism (SNP). Since logistic regression model requires iterative algorithms to get the parameter estimates, its application to GWAS has been limited to the identification of the individual SNPs. Thus, there have been limited applications of LR to multiple SNP analysis including gene-gene interaction analysis in large scale GWAS data. To overcome this computational burden, we developed a logistic regression analysis tool named CUDA-LR, based on the new programming architecture using Graphics Processing Unit (GPU). CUDA-LR supports not only the simple model with single SNP but also more complex model with two SNPs including the interaction. In addition, CUDA-LR provides various parameters to gain more acceleration and perform specified analysis. In the comparison between our analysis and the other methods, CUDA-LR showed almost 700-folds of acceleration and highly reliable results by our GPU specified optimization techniques. We believe that the CUDA-LR now is a useful logistic regression analysis tool for interaction analysis of large scale GWAS datasets.
{"title":"CUDA-LR: CUDA-accelerated logistic regression analysis tool for gene-gene interaction for genome-wide association study","authors":"Sungyoung Lee, Min-Seok Kwon, Iksoo Huh, T. Park","doi":"10.1109/BIBMW.2011.6112454","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112454","url":null,"abstract":"In genome-wide association studies (GWAS), logistic regression (LR) has been most commonly used for finding an association between a disease phenotype and genetic variants such as single nucleotide polymorphism (SNP). Since logistic regression model requires iterative algorithms to get the parameter estimates, its application to GWAS has been limited to the identification of the individual SNPs. Thus, there have been limited applications of LR to multiple SNP analysis including gene-gene interaction analysis in large scale GWAS data. To overcome this computational burden, we developed a logistic regression analysis tool named CUDA-LR, based on the new programming architecture using Graphics Processing Unit (GPU). CUDA-LR supports not only the simple model with single SNP but also more complex model with two SNPs including the interaction. In addition, CUDA-LR provides various parameters to gain more acceleration and perform specified analysis. In the comparison between our analysis and the other methods, CUDA-LR showed almost 700-folds of acceleration and highly reliable results by our GPU specified optimization techniques. We believe that the CUDA-LR now is a useful logistic regression analysis tool for interaction analysis of large scale GWAS datasets.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"47 1","pages":"691-695"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77888398","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.2011.6112550
Xiaodong Zhou, E. George
In a typical gene expression study with high throughput technique, such as microarray, a biologist usually focuses on the top genes ranked by the P-values to establish gene functional relationship / network, biological pathway, and microbiologically ramifications of the gene's selection. With more datasets publically available, researchers pool data from independent experiments, typically by pooling P-values with equal weight assigned to each dataset, aiming to fetch more biological information from the pooled data. However, the qualities of datasets may vary substantially. Assigning equal weights may not guarantee the optimal result. Applying the equal weights approach to six independent datasets, we observe the top rank genes of data pooled with this approach have less functional coherence than the single dataset that has highest functional coherence. We propose a procedure based on enhanced simulated annealing (ESA) and literature semantic indexing cohesive (LSI-c) analysis to assign optimal weights to datasets so as to maximize the functional coherence of the top rank genes ordered by their pooled P-values. We observe significantly more functional coherence in optimally pooled data than any single dataset or data pooled with equal weights. Identification of top rank genes through our optimal procedure should improve the downstream analysis.
{"title":"Weighted pooling high-throughput gene expression data sets to maximize the functional coherence of the top rank genes","authors":"Xiaodong Zhou, E. George","doi":"10.1109/BIBMW.2011.6112550","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112550","url":null,"abstract":"In a typical gene expression study with high throughput technique, such as microarray, a biologist usually focuses on the top genes ranked by the P-values to establish gene functional relationship / network, biological pathway, and microbiologically ramifications of the gene's selection. With more datasets publically available, researchers pool data from independent experiments, typically by pooling P-values with equal weight assigned to each dataset, aiming to fetch more biological information from the pooled data. However, the qualities of datasets may vary substantially. Assigning equal weights may not guarantee the optimal result. Applying the equal weights approach to six independent datasets, we observe the top rank genes of data pooled with this approach have less functional coherence than the single dataset that has highest functional coherence. We propose a procedure based on enhanced simulated annealing (ESA) and literature semantic indexing cohesive (LSI-c) analysis to assign optimal weights to datasets so as to maximize the functional coherence of the top rank genes ordered by their pooled P-values. We observe significantly more functional coherence in optimally pooled data than any single dataset or data pooled with equal weights. Identification of top rank genes through our optimal procedure should improve the downstream analysis.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"91 1","pages":"1033-1033"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73054824","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.2011.6112360
Nicholas Mancuso, Bassam Tork, P. Skums, I. Măndoiu, A. Zelikovsky
We consider the quasispecies spectrum reconstruction problem in amplicon reads. The main contribution of this paper is several methods to reconstruct HCV quasispecies from simulated error-free amplicon reads. Our comparison with existing methods for quasispecies spectrum reconstruction both based on shotgun and amplicon reads show significant advantages of the proposed technique. In most of the cases, even low coverage allows to reconstruct majority of quasispecies and very accurately estimate their frequencies in the simulated samples. The source code for all implemented algorithms is available at https://bitbucket.org/nmancuso/bioa/
{"title":"Viral quasispecies reconstruction from amplicon 454 pyrosequencing reads","authors":"Nicholas Mancuso, Bassam Tork, P. Skums, I. Măndoiu, A. Zelikovsky","doi":"10.1109/BIBMW.2011.6112360","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112360","url":null,"abstract":"We consider the quasispecies spectrum reconstruction problem in amplicon reads. The main contribution of this paper is several methods to reconstruct HCV quasispecies from simulated error-free amplicon reads. Our comparison with existing methods for quasispecies spectrum reconstruction both based on shotgun and amplicon reads show significant advantages of the proposed technique. In most of the cases, even low coverage allows to reconstruct majority of quasispecies and very accurately estimate their frequencies in the simulated samples. The source code for all implemented algorithms is available at https://bitbucket.org/nmancuso/bioa/","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"127 1","pages":"94-101"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75928566","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.2011.6112478
Xiaoyu Chen, Lizhuang Ma, Na Chu, Yiyang Hu
Accurate discriminants of relationship between syndromes and syndrome information (symptoms, and lab indicators) are much desired in medical diagnosis applications. Although discriminants have been applied widely, the researches and applications of discriminant diagnosis model (DDT) are still blanks in diagnosis of chronic hepatitis B in traditional Chinese medicine (TCM). In this paper, a new discriminant diagnosis model constructed by attribute selection, decision tree C5.0 algorithm and discrimination analysis is proposed, which consists of two phases. One is attribute selection. The critical attributes are filtered out from the original attributes. The other is modeling phase to acquire discriminants between syndromes of chronic hepatitis B and syndrome information in TCM. From our experiments, combinations of TCM clinical symptoms and lab indicators are selected to provide formulas for syndrome differentiation of chronic hepatitis B in TCM from original 247 symptoms initially, and the model shows a better prospect for application in TCM diagnosis.
{"title":"Diagnosis based on decision tree and discrimination analysis for chronic hepatitis b in TCM","authors":"Xiaoyu Chen, Lizhuang Ma, Na Chu, Yiyang Hu","doi":"10.1109/BIBMW.2011.6112478","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112478","url":null,"abstract":"Accurate discriminants of relationship between syndromes and syndrome information (symptoms, and lab indicators) are much desired in medical diagnosis applications. Although discriminants have been applied widely, the researches and applications of discriminant diagnosis model (DDT) are still blanks in diagnosis of chronic hepatitis B in traditional Chinese medicine (TCM). In this paper, a new discriminant diagnosis model constructed by attribute selection, decision tree C5.0 algorithm and discrimination analysis is proposed, which consists of two phases. One is attribute selection. The critical attributes are filtered out from the original attributes. The other is modeling phase to acquire discriminants between syndromes of chronic hepatitis B and syndrome information in TCM. From our experiments, combinations of TCM clinical symptoms and lab indicators are selected to provide formulas for syndrome differentiation of chronic hepatitis B in TCM from original 247 symptoms initially, and the model shows a better prospect for application in TCM diagnosis.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"26 1","pages":"817-822"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75946419","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.2011.6112391
S. Rashid, G. M. Maruf
A significant challenge in DNA microarray and mass spectrometric data analysis can be attributed to the problem of having a large number of features with a small number of samples or patients in the data set. Particular care is required to deal with such a problem as the low classification accuracy of a model brought about by the small number of features may depict a low predictive capability. To overcome the associated challenges, proper approaches for data preprocessing, feature reduction and identifying the optimal set of features are critical. In this paper, a novel technique has been proposed for feature reduction and cancer classification; which is applicable for two different types of biological data. The proposed method has been implemented on Surface enhanced laser desorption/ionization time-of-flight mass spectrometric (SELDI-TOF-MS) and DNA microarray data sets. This technique is self adaptive and independent of the type data sets. We have developed a two step strategy for feature reduction such as (1) data preprocessing which includes merging and t-testing and (2) wavelet decomposition. For classification purpose, support vector machine (SVM) has been proposed. By evaluating the performance of the proposed algorithm on the two types of datasets it has been shown that the classification accuracy, sensitivity and specificity obtained by the features selected by the proposed method consistently give excellent performance.
{"title":"An adaptive feature reduction algorithm for cancer classification using wavelet decomposition of serum proteomic and DNA microarray data","authors":"S. Rashid, G. M. Maruf","doi":"10.1109/BIBMW.2011.6112391","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112391","url":null,"abstract":"A significant challenge in DNA microarray and mass spectrometric data analysis can be attributed to the problem of having a large number of features with a small number of samples or patients in the data set. Particular care is required to deal with such a problem as the low classification accuracy of a model brought about by the small number of features may depict a low predictive capability. To overcome the associated challenges, proper approaches for data preprocessing, feature reduction and identifying the optimal set of features are critical. In this paper, a novel technique has been proposed for feature reduction and cancer classification; which is applicable for two different types of biological data. The proposed method has been implemented on Surface enhanced laser desorption/ionization time-of-flight mass spectrometric (SELDI-TOF-MS) and DNA microarray data sets. This technique is self adaptive and independent of the type data sets. We have developed a two step strategy for feature reduction such as (1) data preprocessing which includes merging and t-testing and (2) wavelet decomposition. For classification purpose, support vector machine (SVM) has been proposed. By evaluating the performance of the proposed algorithm on the two types of datasets it has been shown that the classification accuracy, sensitivity and specificity obtained by the features selected by the proposed method consistently give excellent performance.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"40 1","pages":"305-312"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72936030","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}
The regulatory mechanism of recombination is a fundamental problem in genomics, with wide applications in genome wide association studies, birth-defect diseases, molecular evolution, cancer research, etc. In mammalian genomes, recombination events cluster into short genomic regions called ¡§recombination hotspots¡¨. Recently, a 13-mer motif enriched in hotspots is identified as a candidate cis-regulatory element of human recombination hotspots, moreover, a zinc finger protein, PRDM9, binds to this motif and is associated with variation of recombination phenotype in human and mouse genomes, thus is a trans-acting regulator of recombination hotspots. However, this pair of cis and trans-regulators covers only a fraction of hotspots, thus other regulators of recombination hotspots remain to be discovered. In this paper, we propose an approach to predicting additional trans-regulators from DNA-binding proteins by comparing their enrichment of binding sites in hotspots. Applying this approach on newly mapped mouse hotspots genome-wide, we confirmed that PRDM9 is a major trans-regulator of hotspots. In addition, a list of top candidate trans-regulators of mouse hotspots is reported. Using GO analysis we observed that the top genes are enriched with function of his tone modification, highlighting the epigenetic regulatory mechanisms of recombination hotspots.
{"title":"Prediction of Trans-regulators of Recombination Hotspots in Mouse Genome","authors":"Min Wu, C. Kwoh, T. Przytycka, Jing Li, Jie Zheng","doi":"10.1109/BIBM.2011.77","DOIUrl":"https://doi.org/10.1109/BIBM.2011.77","url":null,"abstract":"The regulatory mechanism of recombination is a fundamental problem in genomics, with wide applications in genome wide association studies, birth-defect diseases, molecular evolution, cancer research, etc. In mammalian genomes, recombination events cluster into short genomic regions called ¡§recombination hotspots¡¨. Recently, a 13-mer motif enriched in hotspots is identified as a candidate cis-regulatory element of human recombination hotspots, moreover, a zinc finger protein, PRDM9, binds to this motif and is associated with variation of recombination phenotype in human and mouse genomes, thus is a trans-acting regulator of recombination hotspots. However, this pair of cis and trans-regulators covers only a fraction of hotspots, thus other regulators of recombination hotspots remain to be discovered. In this paper, we propose an approach to predicting additional trans-regulators from DNA-binding proteins by comparing their enrichment of binding sites in hotspots. Applying this approach on newly mapped mouse hotspots genome-wide, we confirmed that PRDM9 is a major trans-regulator of hotspots. In addition, a list of top candidate trans-regulators of mouse hotspots is reported. Using GO analysis we observed that the top genes are enriched with function of his tone modification, highlighting the epigenetic regulatory mechanisms of recombination hotspots.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"104 1","pages":"57-62"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74267351","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.2011.6112458
Sungkyoung Choi, Sungho Won
We propose a new method for genome-wide association analysis with a family-based design. The proposed method is robust against population substructure while it is more efficient than the traditional method such as transmission disequilibrium test for related samples. The proposed method estimates the correlation matrix between individuals and then the principal component analysis is applied. To maximize the statistical power, we consider the additive polygenic model and a best linear unbiased predictor is used as offset. We confirmed that the proposed method is always efficient by simulation studies. The method will be applied to Framingham Heart study.
{"title":"Robust analysis of related samples under the presence of population substructure","authors":"Sungkyoung Choi, Sungho Won","doi":"10.1109/BIBMW.2011.6112458","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112458","url":null,"abstract":"We propose a new method for genome-wide association analysis with a family-based design. The proposed method is robust against population substructure while it is more efficient than the traditional method such as transmission disequilibrium test for related samples. The proposed method estimates the correlation matrix between individuals and then the principal component analysis is applied. To maximize the statistical power, we consider the additive polygenic model and a best linear unbiased predictor is used as offset. We confirmed that the proposed method is always efficient by simulation studies. The method will be applied to Framingham Heart study.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"11 1","pages":"714-720"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84950649","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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