Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706652
Neeraj Koul, N. Bui, Vasant G Honavar
The emergence of data rich domains has led to an exponential growth in the size and number of data repositories, offering exciting opportunities to learn from the data using machine learning algorithms. In particular, sequence data is being made available at a rapid rate. In many applications, the learning algorithm may not have direct access to the entire dataset because of a variety of reasons such as massive data size or bandwidth limitation. In such settings, there is a need for techniques that can learn predictive models (e.g., classifiers) from large datasets without direct access to the data. We describe an approach to learn from massive sequence datasets using statistical queries. Specifically we show how Markov Models and Probabilistic Suffix Trees (PSTs) can be constructed from sequence databases that answer only a class of count queries. We analyze the query complexity (a measure of the number of queries needed) for constructing classifiers in such settings and outline some techniques to minimize the query complexity. We also show how some of the models can be updated in response to addition or deletion of subsets of sequences from the underlying sequence database.
{"title":"Scalable, updatable predictive models for sequence data","authors":"Neeraj Koul, N. Bui, Vasant G Honavar","doi":"10.1109/BIBM.2010.5706652","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706652","url":null,"abstract":"The emergence of data rich domains has led to an exponential growth in the size and number of data repositories, offering exciting opportunities to learn from the data using machine learning algorithms. In particular, sequence data is being made available at a rapid rate. In many applications, the learning algorithm may not have direct access to the entire dataset because of a variety of reasons such as massive data size or bandwidth limitation. In such settings, there is a need for techniques that can learn predictive models (e.g., classifiers) from large datasets without direct access to the data. We describe an approach to learn from massive sequence datasets using statistical queries. Specifically we show how Markov Models and Probabilistic Suffix Trees (PSTs) can be constructed from sequence databases that answer only a class of count queries. We analyze the query complexity (a measure of the number of queries needed) for constructing classifiers in such settings and outline some techniques to minimize the query complexity. We also show how some of the models can be updated in response to addition or deletion of subsets of sequences from the underlying sequence database.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"108 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":"133264480","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.2010.5706541
C. Su, S. D. Handoko, C. Kwoh, C. Schönbach, X. Li
The H1N1 influenza A 2009 pandemic caused a global concern as it has killed more than 18,000 people worldwide so far. Studies that have found cross-neutralizing antibodies between the 1918 and 2009 pandemic flu elicit a basis of pre-existing immunity against the 2009 H1N1 virus in old population. The cross-reactivity occurs due to conserved antigenic epitopes shared between the two pandemic viruses. However, evolutionary mutation can enable the virus to elude human immunity system, making these antibodies probably no longer effective. In our study, we found that a possible mutation in B-cell epitope (the sequence PNHDSNKG) could be the chance for the virus to escape the 1918 antibody recognition. Hence, this finding can be helpful for further vaccine designs against the H1N1 2009 influenza A virus.
{"title":"A possible mutation that enables H1N1 influenza a virus to escape antibody recognition","authors":"C. Su, S. D. Handoko, C. Kwoh, C. Schönbach, X. Li","doi":"10.1109/BIBM.2010.5706541","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706541","url":null,"abstract":"The H1N1 influenza A 2009 pandemic caused a global concern as it has killed more than 18,000 people worldwide so far. Studies that have found cross-neutralizing antibodies between the 1918 and 2009 pandemic flu elicit a basis of pre-existing immunity against the 2009 H1N1 virus in old population. The cross-reactivity occurs due to conserved antigenic epitopes shared between the two pandemic viruses. However, evolutionary mutation can enable the virus to elude human immunity system, making these antibodies probably no longer effective. In our study, we found that a possible mutation in B-cell epitope (the sequence PNHDSNKG) could be the chance for the virus to escape the 1918 antibody recognition. Hence, this finding can be helpful for further vaccine designs against the H1N1 2009 influenza A virus.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"37 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":"134372834","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.2010.5706629
Qiwei Li, Tong Liang, Xiaodan Fan, Chunhui Xu, Weichang Yu, S. Li
Fluorescence in situ hybridization (FISH) is a powerful technique that localizes specific DNA sequences on chromosomes for use in physical and genetic maps assembling, genetic counselling, species identification, etc. Highly repetitive sequences are considered to be suitable FISH probes that can avoid many potential problems of using unique sequences as FISH probes. The distinct chromosomal distributions of these highly repetitive sequences are also ideal for labelling purposes such as karyotyping. In this paper, we present an automatic computational procedure for searching highly repetitive sequences from a whole genome as FISH probes, as well as an experimental protocol to use them in FISH analysis. We successfully applied the method on the newly released genome of Brachypodium distachyon (Brachypodium) and produced satisfactory results of FISH experiment.
{"title":"An automatic procedure to search highly repetitive sequences in genome as fluorescence in situ hybridization probes and its application on Brachypodium distachyon","authors":"Qiwei Li, Tong Liang, Xiaodan Fan, Chunhui Xu, Weichang Yu, S. Li","doi":"10.1109/BIBM.2010.5706629","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706629","url":null,"abstract":"Fluorescence in situ hybridization (FISH) is a powerful technique that localizes specific DNA sequences on chromosomes for use in physical and genetic maps assembling, genetic counselling, species identification, etc. Highly repetitive sequences are considered to be suitable FISH probes that can avoid many potential problems of using unique sequences as FISH probes. The distinct chromosomal distributions of these highly repetitive sequences are also ideal for labelling purposes such as karyotyping. In this paper, we present an automatic computational procedure for searching highly repetitive sequences from a whole genome as FISH probes, as well as an experimental protocol to use them in FISH analysis. We successfully applied the method on the newly released genome of Brachypodium distachyon (Brachypodium) and produced satisfactory results of FISH experiment.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"61 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":"133779083","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.2010.5706647
Chih-Hsuan Wei, Hung-Yu kao
In extraction of information from the biomedical literature, name disambiguation of domain-specific entities, such as proteins, is one of the most important issues. The entity ambiguity with the highest dimension is the species to which an entity is associated with. Furthermore, one of the bottlenecks in inter-species gene name normalization is species disambiguation. To enhance the performance of species disambiguation, the detection of focus species detection remains a substantial challenge. This study presents a method addressing this issue. The results present evaluations of all articles from the BioCreaTive I&II GN task. Our method is robust for all types of articles, particularly those without explicit species entity information. Since our method requires a training corpus to be the indicator vector, we developed an iterative corpus distillation method to extend the corpus. In the conducted experiments, the proposed method achieved a high accuracy of 85.64% and 84.32% without species entity information.
{"title":"Represented indicator measurement and corpus distillation on focus species detection","authors":"Chih-Hsuan Wei, Hung-Yu kao","doi":"10.1109/BIBM.2010.5706647","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706647","url":null,"abstract":"In extraction of information from the biomedical literature, name disambiguation of domain-specific entities, such as proteins, is one of the most important issues. The entity ambiguity with the highest dimension is the species to which an entity is associated with. Furthermore, one of the bottlenecks in inter-species gene name normalization is species disambiguation. To enhance the performance of species disambiguation, the detection of focus species detection remains a substantial challenge. This study presents a method addressing this issue. The results present evaluations of all articles from the BioCreaTive I&II GN task. Our method is robust for all types of articles, particularly those without explicit species entity information. Since our method requires a training corpus to be the indicator vector, we developed an iterative corpus distillation method to extend the corpus. In the conducted experiments, the proposed method achieved a high accuracy of 85.64% and 84.32% without species entity information.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"4 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":"130279320","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.2010.5706588
M. Pradhan, L. Ledford, Yogesh Pandit, M. Palakal
In this paper we present a global analysis of colon rectal cancer genes and their associated miRNAs. Significant genes in colon cancer were obtained by mining the literature and cancer related miRNAs were obtained from miRbase. Five different features were used to analyze to obtain a global gene-miRNA profile. By combining the topological features along with miRNA-gene associations and gene propensity measures, we identified a set of genes and modules that are significant in CRC. The proposed methodology identified 123 significant modules of miRNA-genes that can be further studied for understanding the disease and marker discovery.
{"title":"Global analysis of miRNA target genes in colon rectal cancer","authors":"M. Pradhan, L. Ledford, Yogesh Pandit, M. Palakal","doi":"10.1109/BIBM.2010.5706588","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706588","url":null,"abstract":"In this paper we present a global analysis of colon rectal cancer genes and their associated miRNAs. Significant genes in colon cancer were obtained by mining the literature and cancer related miRNAs were obtained from miRbase. Five different features were used to analyze to obtain a global gene-miRNA profile. By combining the topological features along with miRNA-gene associations and gene propensity measures, we identified a set of genes and modules that are significant in CRC. The proposed methodology identified 123 significant modules of miRNA-genes that can be further studied for understanding the disease and marker discovery.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"12 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":"133609576","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.2010.5706615
Tao Zeng, Xuan Guo, Juan Liu
Along with the emergence and development of translational biomedicine, more and more genetic information has been applied in clinical practice. In recent decade, the discovery of genetic biomarkers for cancer prognosis obtains increasing attentions and many methods have been developed. The ”element” methods use one or two independent genes to judge the Boolean status of disease. The ”set” methods use general genetic biomarkers to classify patients into different risks as a whole. And the advanced ”sets” methods use a group of different gene sets as biomarkers. However, the existing methods always concern positive correlations among genes ignoring negative correlations. Whereas the negative regulation, negative feedback, and functional repression are actually the important clues in cancer expression profiles. Therefore, in this paper, we propose to mine negative correlated gene sets (NCGSs) from multiple datasets, and use them along with the pure positive correlated gene sets for prognosis classification. The exploring experimental results have shown the encouraging promotion of cancer prognosis accuracy with NCGSs.
{"title":"Discovering negative correlated gene sets from integrative gene expression data for cancer prognosis","authors":"Tao Zeng, Xuan Guo, Juan Liu","doi":"10.1109/BIBM.2010.5706615","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706615","url":null,"abstract":"Along with the emergence and development of translational biomedicine, more and more genetic information has been applied in clinical practice. In recent decade, the discovery of genetic biomarkers for cancer prognosis obtains increasing attentions and many methods have been developed. The ”element” methods use one or two independent genes to judge the Boolean status of disease. The ”set” methods use general genetic biomarkers to classify patients into different risks as a whole. And the advanced ”sets” methods use a group of different gene sets as biomarkers. However, the existing methods always concern positive correlations among genes ignoring negative correlations. Whereas the negative regulation, negative feedback, and functional repression are actually the important clues in cancer expression profiles. Therefore, in this paper, we propose to mine negative correlated gene sets (NCGSs) from multiple datasets, and use them along with the pure positive correlated gene sets for prognosis classification. The exploring experimental results have shown the encouraging promotion of cancer prognosis accuracy with NCGSs.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"26 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":"115429718","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.2010.5706654
Xiaoshi Yin, Zhoujun Li, Xiangji Huang, Xiaohua Hu
Traditional retrieval models assume that the relevance of a document is independent of the relevance of other documents. However, this assumption may result in high redundancy and low diversity in a ranked list. In order to provide comprehensive and diverse answers to fulfill biologists' information need, we propose a relevance-novelty combined model, named RelNov model, based on the framework of an undirected graphical model. Experiments conducted on the TREC 2006 and 2007 Genomics collections show that the proposed approach is effective in promoting both diversity and relevance of retrieval ranked lists.
{"title":"A relevance-novelty combined model for genomics search result diversification","authors":"Xiaoshi Yin, Zhoujun Li, Xiangji Huang, Xiaohua Hu","doi":"10.1109/BIBM.2010.5706654","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706654","url":null,"abstract":"Traditional retrieval models assume that the relevance of a document is independent of the relevance of other documents. However, this assumption may result in high redundancy and low diversity in a ranked list. In order to provide comprehensive and diverse answers to fulfill biologists' information need, we propose a relevance-novelty combined model, named RelNov model, based on the framework of an undirected graphical model. Experiments conducted on the TREC 2006 and 2007 Genomics collections show that the proposed approach is effective in promoting both diversity and relevance of retrieval ranked lists.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"29 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":"117101910","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.2010.5706530
V. Pejaver, Sun Kim
Proximity-based methods and co-evolution-based phylogenetic profiles methods have been successfully used for the identification of functionally related genes. Proximity-based methods are effective for physically clustered genes while the phylogenetic profiles method is effective for co-occurring gene sets. However, both methods predict many false positives and false negatives. In this paper, we propose the Gene Cluster Profile Vector (GCPV) method, which combines these two methods by using phylogenetic profiles of whole gene clusters. Moreover, the GCPV method is, currently, the only method that allows for the characterization of relationships between gene clusters themselves. The GCPV method groups together reasonably related operons in E. coli about 60% of the time. The method is minimally dependent on the reference genome set used and it outperforms the conventional phylogenetic profiles method. Finally, we show that the method works well for predicted gene clusters from C. crescentus and can serve as an important tool not only for understanding gene function, but also for elucidating mechanisms of general biological processes.
{"title":"Gene cluster profile vectors: A novel method to infer functional coupling using both gene proximity and co-occurrence profiles","authors":"V. Pejaver, Sun Kim","doi":"10.1109/BIBM.2010.5706530","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706530","url":null,"abstract":"Proximity-based methods and co-evolution-based phylogenetic profiles methods have been successfully used for the identification of functionally related genes. Proximity-based methods are effective for physically clustered genes while the phylogenetic profiles method is effective for co-occurring gene sets. However, both methods predict many false positives and false negatives. In this paper, we propose the Gene Cluster Profile Vector (GCPV) method, which combines these two methods by using phylogenetic profiles of whole gene clusters. Moreover, the GCPV method is, currently, the only method that allows for the characterization of relationships between gene clusters themselves. The GCPV method groups together reasonably related operons in E. coli about 60% of the time. The method is minimally dependent on the reference genome set used and it outperforms the conventional phylogenetic profiles method. Finally, we show that the method works well for predicted gene clusters from C. crescentus and can serve as an important tool not only for understanding gene function, but also for elucidating mechanisms of general biological processes.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"168 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":"122196440","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.2010.5706616
Hyungmin Lee, Miyoung Shin, Munpyo Hong
For the identification of significant genes involved in specific diseases, microarray gene expression profiles have been widely used to prioritize candidate genes. In this paper, we propose a new gene ranking method that employs genegene relations extracted from literature along with gene expression scores obtained from microarrays. Here the genegene relations are extracted by taking a hybrid approach which is a combination of syntactic analysis and co-occurrence based approaches. Specifically, we perform the syntactic parsing on the text and then, within each clause of the parsed sentence, the co-occurred gene names are considered to be mutually related. Both the gene network derived from the gene-gene relations obtained in the above way and the gene expression scores are given as the inputs to the GeneRank algorithm. For the evaluation of our approach, we conducted experiments with the publicly available prostate cancer data. The results show that our method is superior in the precision and the recall to the original GeneRank which employs the gene-gene relations built from gene ontology annotations. Furthermore, our hybrid approach to the gene-gene relation extraction produces better prioritization of truly disease-related genes in top ranks than the existing popular co-occurrence approach.
{"title":"A gene ranking method using text-mining for the identification of disease related genes","authors":"Hyungmin Lee, Miyoung Shin, Munpyo Hong","doi":"10.1109/BIBM.2010.5706616","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706616","url":null,"abstract":"For the identification of significant genes involved in specific diseases, microarray gene expression profiles have been widely used to prioritize candidate genes. In this paper, we propose a new gene ranking method that employs genegene relations extracted from literature along with gene expression scores obtained from microarrays. Here the genegene relations are extracted by taking a hybrid approach which is a combination of syntactic analysis and co-occurrence based approaches. Specifically, we perform the syntactic parsing on the text and then, within each clause of the parsed sentence, the co-occurred gene names are considered to be mutually related. Both the gene network derived from the gene-gene relations obtained in the above way and the gene expression scores are given as the inputs to the GeneRank algorithm. For the evaluation of our approach, we conducted experiments with the publicly available prostate cancer data. The results show that our method is superior in the precision and the recall to the original GeneRank which employs the gene-gene relations built from gene ontology annotations. Furthermore, our hybrid approach to the gene-gene relation extraction produces better prioritization of truly disease-related genes in top ranks than the existing popular co-occurrence approach.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"62 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":"114065648","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.2010.5706650
Sérgio Dias, A. Gomes
The problem addressed in this paper consists in triangulating the van der Waals surface without computing the geometric intersections of its atoms. Recall that the van der Waals surface is useful in computational molecular biology and biochemistry to, for example, determine the volume occupied by a molecule, as well as other important geometric properties. Assuming that every atom is represented by a ball, this amounts to compute the surface of the union of a number of balls. The novelty of our method lies in avoiding the computation of surface-surface intersections (SSI) of two or more balls.
{"title":"GPU-based triangulation of the van der Waals surface","authors":"Sérgio Dias, A. Gomes","doi":"10.1109/BIBM.2010.5706650","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706650","url":null,"abstract":"The problem addressed in this paper consists in triangulating the van der Waals surface without computing the geometric intersections of its atoms. Recall that the van der Waals surface is useful in computational molecular biology and biochemistry to, for example, determine the volume occupied by a molecule, as well as other important geometric properties. Assuming that every atom is represented by a ball, this amounts to compute the surface of the union of a number of balls. The novelty of our method lies in avoiding the computation of surface-surface intersections (SSI) of two or more balls.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"306 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":"132056205","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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