Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706543
Junho Kim, Doheon Lee
Most loci discovered through genome-wide association analyses are predicted to affect gene expression, the integrative approach of genome-wide analysis with gene expression data is becoming essential procedure for discovering genetic effect of disease development. Many studies have been performed to discover significant SNP-gene associations, but most of them are limited to consider only cis-associations and neglect trans-territory. In this study, we explored the effect of trans-eSNP associations that may underlie the alternation of gene expressions. Through the integrative genome-wide association analysis considering the entire SNP-gene associations, we identified numerous trans associations which are significantly associated with gene expression, even more than cis associations in quantity and significance. Our findings revealed the necessity of reconsidering trans-association effect from integrative genome-wide association analysis and provided novel insights to find undiscovered genetic causalities.
{"title":"A new perspective of integrative genome-wide association analysis considering trans eSNP effect","authors":"Junho Kim, Doheon Lee","doi":"10.1109/BIBM.2010.5706543","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706543","url":null,"abstract":"Most loci discovered through genome-wide association analyses are predicted to affect gene expression, the integrative approach of genome-wide analysis with gene expression data is becoming essential procedure for discovering genetic effect of disease development. Many studies have been performed to discover significant SNP-gene associations, but most of them are limited to consider only cis-associations and neglect trans-territory. In this study, we explored the effect of trans-eSNP associations that may underlie the alternation of gene expressions. Through the integrative genome-wide association analysis considering the entire SNP-gene associations, we identified numerous trans associations which are significantly associated with gene expression, even more than cis associations in quantity and significance. Our findings revealed the necessity of reconsidering trans-association effect from integrative genome-wide association analysis and provided novel insights to find undiscovered genetic causalities.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1955 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129563474","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706546
R. Leung, S. Tsui
Nucleotides and amino acids are basic building units of RNA, DNA and protein. Although intensive studies on understanding how the change of these building blocks affect the phenotypes of these biopolymers are ever increasing, many popular alignment formats are generated by pairwise comparision tools such as the Basic Local Alignment Search Tool (BLAST). These alignments are user friendly to researchers but are not convenient for searching, filtering and storage, in particular when there are thousands of alignments generated from highly conserved sequences. Here, we introduce a new alignment format, alns, to facilitate rapid and convenient association of genetic changes and similarity to other sources of information such as phenotypes, disease state, time, geography and taxonomy via simple spreadsheet functions. The format shall assist biologists from wide disciplines in knowledge discovery.
{"title":"alns — A searchable and filterable sequence alignment format","authors":"R. Leung, S. Tsui","doi":"10.1109/BIBM.2010.5706546","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706546","url":null,"abstract":"Nucleotides and amino acids are basic building units of RNA, DNA and protein. Although intensive studies on understanding how the change of these building blocks affect the phenotypes of these biopolymers are ever increasing, many popular alignment formats are generated by pairwise comparision tools such as the Basic Local Alignment Search Tool (BLAST). These alignments are user friendly to researchers but are not convenient for searching, filtering and storage, in particular when there are thousands of alignments generated from highly conserved sequences. Here, we introduce a new alignment format, alns, to facilitate rapid and convenient association of genetic changes and similarity to other sources of information such as phenotypes, disease state, time, geography and taxonomy via simple spreadsheet functions. The format shall assist biologists from wide disciplines in knowledge discovery.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114146297","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706604
E. Martin
Within the field of medical informatics, the analysis of human body acceleration signals to examine gait patterns can provide valuable information for multiple health-related applications. In this paper, we study the suitability of the wavelet transform for the analysis of body acceleration signals, and propose useful guidelines to solve existing issues in this field (such as the need for training), thus enabling a smooth application of this signal processing tool in medical environments. Making use of these guidelines, we have successfully tested our approach to analyze body acceleration signals, delivering a rich characterization of different gait patterns, without the need for training.
{"title":"Solving training issues in the application of the wavelet transform to precisely analyze human body acceleration signals","authors":"E. Martin","doi":"10.1109/BIBM.2010.5706604","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706604","url":null,"abstract":"Within the field of medical informatics, the analysis of human body acceleration signals to examine gait patterns can provide valuable information for multiple health-related applications. In this paper, we study the suitability of the wavelet transform for the analysis of body acceleration signals, and propose useful guidelines to solve existing issues in this field (such as the need for training), thus enabling a smooth application of this signal processing tool in medical environments. Making use of these guidelines, we have successfully tested our approach to analyze body acceleration signals, delivering a rich characterization of different gait patterns, without the need for training.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128254022","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}
Bacterial meningitis is still a life-threatening disease, and early diagnosis of pathogen can be crucial to improving survival rate. Using the surface-enhanced Raman scattering (SERS) platform developed by our group, the pathogens can be differentiated on the basis of their SERS spectra which are believed to related to their surface chemical components. We collected the SERS spectra of ten pathogens: Streptococcus pneumoniae(Spn), Streptococcus agalactiae (group B streptococcus, GBS), Staphylococcus aureus (Sa), Pseudomonas aeruginosae (Psa), Acinetobacter baumannii (Ab), Klebsiella pneumoniae (Kp), Neisseria meningitidis (Nm), Listeria monocy-togenes (Lm), Haemophilus influenzae (Hi), and Escherichia coli (E. coli). These samples were obtained from patients in National Taiwan University Hospital, and were believed to represent the real diversity of clinical pathogens. Using the support vector machine (SVM) method, the classification accuracy can achieve around 88%. However, we noted that SVM cannot distinguish between [E. coli, Kp] and [Sa, Hi] due to the fact that the global features of these two groups of pathogens are very similar. We therefore incorporated a classification tree method that can focus on local differences in classification rules. This improved the accuracy to 90%. To get a better understanding of the SERS signals, we also compared several other classification methods. In addition, rule extraction method which attempts to explain why classifier fail or succeed is also discussed. Our preliminary results are interesting, encouraging, and await more thorough investigation.
{"title":"Hybrid SVM/CART classification of pathogenic species of bacterial meningitis with surface-enhanced Raman scattering","authors":"Chung-Yueh Huang, Tsung-Heng Tsai, Bing-Cheng Wen, Chia-Wen Chung, Yung-Jui Li, Ya-Ching Chuang, Wen-Jie Lin, Li-Li Li, Juen-Kai Wang, Yuh‐Lin Wang, Chi-Hung Lin, Da-Wei Wang","doi":"10.1109/BIBM.2010.5706600","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706600","url":null,"abstract":"Bacterial meningitis is still a life-threatening disease, and early diagnosis of pathogen can be crucial to improving survival rate. Using the surface-enhanced Raman scattering (SERS) platform developed by our group, the pathogens can be differentiated on the basis of their SERS spectra which are believed to related to their surface chemical components. We collected the SERS spectra of ten pathogens: Streptococcus pneumoniae(Spn), Streptococcus agalactiae (group B streptococcus, GBS), Staphylococcus aureus (Sa), Pseudomonas aeruginosae (Psa), Acinetobacter baumannii (Ab), Klebsiella pneumoniae (Kp), Neisseria meningitidis (Nm), Listeria monocy-togenes (Lm), Haemophilus influenzae (Hi), and Escherichia coli (E. coli). These samples were obtained from patients in National Taiwan University Hospital, and were believed to represent the real diversity of clinical pathogens. Using the support vector machine (SVM) method, the classification accuracy can achieve around 88%. However, we noted that SVM cannot distinguish between [E. coli, Kp] and [Sa, Hi] due to the fact that the global features of these two groups of pathogens are very similar. We therefore incorporated a classification tree method that can focus on local differences in classification rules. This improved the accuracy to 90%. To get a better understanding of the SERS signals, we also compared several other classification methods. In addition, rule extraction method which attempts to explain why classifier fail or succeed is also discussed. Our preliminary results are interesting, encouraging, and await more thorough investigation.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132452545","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706568
N. Baskaran, C. Kwoh, K. Hui
Gene association analysis of cancer microarray data provides a wealth of information on gene expression patterns and cancer pathways to enhance the identification of potential biomarkers for cancer diagnosis, prognosis, and prediction of therapeutic responsiveness. However, achieving these biological/clinical objectives relies heavily on the functional capabilities and accuracy of the various analytical tools to mine these cancer microarray gene expression profiles. Many preprocessing algorithms exist for analyzing Affymetrix microarray gene expression data. Previous studies have evaluated these algorithms on their capabilities in accurately determining gene expression using a variety of spike-in as well as experimental data sets. However, variations in detecting differentially expressed genes between these different pre-processing algorithms on a single cancer dataset have not been done in a systems-level evaluation. In this study, we assessed the comparability and the level of variation between PLIER, GCRMA, RMA and MAS5 for their capability to detect differentially expressed genes.
{"title":"Outcomes of gene association analysis of cancer microarray data are impacted by pre-processing algorithms","authors":"N. Baskaran, C. Kwoh, K. Hui","doi":"10.1109/BIBM.2010.5706568","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706568","url":null,"abstract":"Gene association analysis of cancer microarray data provides a wealth of information on gene expression patterns and cancer pathways to enhance the identification of potential biomarkers for cancer diagnosis, prognosis, and prediction of therapeutic responsiveness. However, achieving these biological/clinical objectives relies heavily on the functional capabilities and accuracy of the various analytical tools to mine these cancer microarray gene expression profiles. Many preprocessing algorithms exist for analyzing Affymetrix microarray gene expression data. Previous studies have evaluated these algorithms on their capabilities in accurately determining gene expression using a variety of spike-in as well as experimental data sets. However, variations in detecting differentially expressed genes between these different pre-processing algorithms on a single cancer dataset have not been done in a systems-level evaluation. In this study, we assessed the comparability and the level of variation between PLIER, GCRMA, RMA and MAS5 for their capability to detect differentially expressed genes.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125888844","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706553
M. Ananda, Jianjun Hu
Recent studies showed that protein-protein interaction network based features can significantly improve the prediction of protein subcellular localization. However, it is unclear whether network prediction models or other types of protein-protein correlation networks would also improve localization prediction. We present NetLoc, a novel diffusion kernel-based logistic regression (KLR) algorithm for predicting protein subcellular localization using four types of protein networks including physical protein-protein interaction (PPPI) networks, genetic PPI networks (GPPI), mixed PPI networks (MPPI), and co-expression networks (COEXP). We applied NetLoc to yeast protein localization prediction. The results showed that protein networks can provide rich information for protein localization prediction, achieving prediction performance up to AUC score of 0.93. We also showed that networks with high connectivity and high percentage of interacting protein pairs targeting the same location lead to better prediction performance. We found that physical PPPI is better than GPPI which is better than COEXP in terms of localization prediction. The prediction performance (AUC) using the yeast PPPI network ranges between 0.71 and 0.93 for 7 locations. Compared to the previous network feature based prediction algorithm which achieved AUC scores of (0.49 and 0.52) on the yeast PPI network of the DIP database, NetLoc achieved significantly better overall performance with the AUC of 0.74.
{"title":"NetLoc: Network based protein localization prediction using protein-protein interaction and co-expression networks","authors":"M. Ananda, Jianjun Hu","doi":"10.1109/BIBM.2010.5706553","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706553","url":null,"abstract":"Recent studies showed that protein-protein interaction network based features can significantly improve the prediction of protein subcellular localization. However, it is unclear whether network prediction models or other types of protein-protein correlation networks would also improve localization prediction. We present NetLoc, a novel diffusion kernel-based logistic regression (KLR) algorithm for predicting protein subcellular localization using four types of protein networks including physical protein-protein interaction (PPPI) networks, genetic PPI networks (GPPI), mixed PPI networks (MPPI), and co-expression networks (COEXP). We applied NetLoc to yeast protein localization prediction. The results showed that protein networks can provide rich information for protein localization prediction, achieving prediction performance up to AUC score of 0.93. We also showed that networks with high connectivity and high percentage of interacting protein pairs targeting the same location lead to better prediction performance. We found that physical PPPI is better than GPPI which is better than COEXP in terms of localization prediction. The prediction performance (AUC) using the yeast PPPI network ranges between 0.71 and 0.93 for 7 locations. Compared to the previous network feature based prediction algorithm which achieved AUC scores of (0.49 and 0.52) on the yeast PPI network of the DIP database, NetLoc achieved significantly better overall performance with the AUC of 0.74.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"7 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132463435","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706559
Jian Ma
We introduce a probabilistic framework for inferring contiguous ancestral regions. Our previous work, the inferCARs algorithm, is a method based on adjacencies between synteny blocks. However, the local parsimony procedure has the limitation that it ignores many adjacencies that are potentially possible to exist in the ancestors. In this paper, we introduce a probabilistic method for reconstructing ancestral orders. The essential part of this method is to predict the posterior probability of an adjacency occurring in the ancestor based on an extended Jukes-Cantor model for breakpoints. We implemented a program called inferCARsPro to reconstruct contiguous ancestral regions. Both simulation and real data application results are discussed.
{"title":"A probabilistic framework for inferring ancestral genomic orders","authors":"Jian Ma","doi":"10.1109/BIBM.2010.5706559","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706559","url":null,"abstract":"We introduce a probabilistic framework for inferring contiguous ancestral regions. Our previous work, the inferCARs algorithm, is a method based on adjacencies between synteny blocks. However, the local parsimony procedure has the limitation that it ignores many adjacencies that are potentially possible to exist in the ancestors. In this paper, we introduce a probabilistic method for reconstructing ancestral orders. The essential part of this method is to predict the posterior probability of an adjacency occurring in the ancestor based on an extended Jukes-Cantor model for breakpoints. We implemented a program called inferCARsPro to reconstruct contiguous ancestral regions. Both simulation and real data application results are discussed.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131761087","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706655
Lei Shi, Shikui Tu, L. Xu
We propose a clustering algorithm based on a structural prior based Local Factor Analysis (spLFA) model under the Bayesian Ying-Yang harmony learning, which automatically determines the hidden dimensionalities during parameter learning, reduces the number of free parameters by projecting the mean vectors onto a low dimensional manifold, imposes the sparseness by a Normal-Jeffreys prior. Experiments on the diagnostic research dataset show that BYY-spLFA outperforms the k-means clustering and single-link hierarchical clustering. The experiments on a lymphoma cancer datset further indicate the BYY-spLFA is able to uncover the number of phenotypes correctly and cluster the phenotypes more accurately. In addition, we modify BYY-spLFA to implement supervised learning and preliminarily demonstrate its effectiveness on a Leukemia data for classification.
{"title":"Gene clustering by structural prior based local factor analysis model under Bayesian Ying-Yang harmony learning","authors":"Lei Shi, Shikui Tu, L. Xu","doi":"10.1109/BIBM.2010.5706655","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706655","url":null,"abstract":"We propose a clustering algorithm based on a structural prior based Local Factor Analysis (spLFA) model under the Bayesian Ying-Yang harmony learning, which automatically determines the hidden dimensionalities during parameter learning, reduces the number of free parameters by projecting the mean vectors onto a low dimensional manifold, imposes the sparseness by a Normal-Jeffreys prior. Experiments on the diagnostic research dataset show that BYY-spLFA outperforms the k-means clustering and single-link hierarchical clustering. The experiments on a lymphoma cancer datset further indicate the BYY-spLFA is able to uncover the number of phenotypes correctly and cluster the phenotypes more accurately. In addition, we modify BYY-spLFA to implement supervised learning and preliminarily demonstrate its effectiveness on a Leukemia data for classification.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128119809","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706627
S. Bleik, Wei Xiong, Yiran Wang, Min Song
Assigning keywords to articles can be extremely costly. In this paper we propose a new approach to biomedical concept extraction using semantic features of concept graphs to help in automatic labeling of scientific publications. The proposed system extracts key concepts similar to author-provided keywords. We represent full-text documents by graphs and map biomedical terms to predefined ontology concepts. In addition to occurrence frequency weights, we use concept relation weights to rank potential key concepts. We compare our technique to that of KEA's, a state-of-the-art keyphrase extraction software. The results show that using the relations weight significantly improves the performance of concept extraction. The results also highlight the subjectivity of the concept extraction procedure as well as of its evaluation.
{"title":"Biomedical concept extraction using concept graphs and ontology-based mapping","authors":"S. Bleik, Wei Xiong, Yiran Wang, Min Song","doi":"10.1109/BIBM.2010.5706627","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706627","url":null,"abstract":"Assigning keywords to articles can be extremely costly. In this paper we propose a new approach to biomedical concept extraction using semantic features of concept graphs to help in automatic labeling of scientific publications. The proposed system extracts key concepts similar to author-provided keywords. We represent full-text documents by graphs and map biomedical terms to predefined ontology concepts. In addition to occurrence frequency weights, we use concept relation weights to rank potential key concepts. We compare our technique to that of KEA's, a state-of-the-art keyphrase extraction software. The results show that using the relations weight significantly improves the performance of concept extraction. The results also highlight the subjectivity of the concept extraction procedure as well as of its evaluation.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128121291","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706596
Iman Rezaeian, L. Rueda
Gridding cDNA microarray images is a critical step in gene expression analysis, since any errors in this stage are propagated in future steps in the analysis. We propose a fully automatic approach to detect the locations of the spots. The approach first detects and corrects rotations in the sub-grids by an affine transformation, followed by a polynomial-time optimal multi-level thresholding algorithm that finds the positions of the spots. Additionally, a new validity index is proposed in order to find the correct number of spots in each sub-grid, followed by a refinement procedure used to improve the performance of the method. Extensive experiments on real-life microarray images show that the proposed method performs these tasks automatically and with very high accuracy.
{"title":"A parameterless automatic spot detection method for cDNA microarray images","authors":"Iman Rezaeian, L. Rueda","doi":"10.1109/BIBM.2010.5706596","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706596","url":null,"abstract":"Gridding cDNA microarray images is a critical step in gene expression analysis, since any errors in this stage are propagated in future steps in the analysis. We propose a fully automatic approach to detect the locations of the spots. The approach first detects and corrects rotations in the sub-grids by an affine transformation, followed by a polynomial-time optimal multi-level thresholding algorithm that finds the positions of the spots. Additionally, a new validity index is proposed in order to find the correct number of spots in each sub-grid, followed by a refinement procedure used to improve the performance of the method. Extensive experiments on real-life microarray images show that the proposed method performs these tasks automatically and with very high accuracy.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126505896","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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