Pub Date : 2007-11-01DOI: 10.1109/BIBMW.2007.4425412
A. Sidhu, T. Dillon, E. Chang
Most biological resources available today on the web provide a good number of cross-links to other resources with relevant information. However, in our opinion, what is still lacking is an integrated view that provides complete coverage of information through a single entry point. The main problem lies in interpreting biological nomenclature because the underlying data sources are inconsistent. In this paper we discuss Protein Ontology (PO) Algebra that we use for composition and interoperability of protein data sources. We outline the existing research in interoperability of biological data sources, before discussing our semantic interoperability approach in detail. The actual implementation of Protein Ontology is also discussed briefly in this paper, which depends on the strength of the Protein Ontology Algebra.
{"title":"Ontology algebra for composition of protein data sources","authors":"A. Sidhu, T. Dillon, E. Chang","doi":"10.1109/BIBMW.2007.4425412","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425412","url":null,"abstract":"Most biological resources available today on the web provide a good number of cross-links to other resources with relevant information. However, in our opinion, what is still lacking is an integrated view that provides complete coverage of information through a single entry point. The main problem lies in interpreting biological nomenclature because the underlying data sources are inconsistent. In this paper we discuss Protein Ontology (PO) Algebra that we use for composition and interoperability of protein data sources. We outline the existing research in interoperability of biological data sources, before discussing our semantic interoperability approach in detail. The actual implementation of Protein Ontology is also discussed briefly in this paper, which depends on the strength of the Protein Ontology Algebra.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"65 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126407238","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 : 2007-11-01DOI: 10.1109/BIBMW.2007.4425411
Xiaoyan A. Qu, R. C. Gudivada, A. Jegga, Eric K. Neumann, Bruce J. Aronow
To pursue a systematic approach to the discovery of novel and inferable relationships between drugs and diseases based on mechanistic knowledge, we have sought to apply semantic Web-based technologies to integrate heterogeneous data from pharmacological and biological domains. We have devised a knowledge framework, Disease-Drug Correlation Ontology (DDCO), constructed for semantic representation of the key entities and relationships. A collection of prior knowledge sets including pharmacological substance, drug target, pathway, disease and clinical features, and all interlinking properties were integrated using an RDF (resource description framework) model derived from the semantic elements defined in the DDCO framework. Using the resulting RDF graph network, ontology-based mining and queries could identify embedded associations in this genome-phenome-pharmacome network. Several use-cases demonstrated that potentially powerful rewards could be obtained through semantic integration based on principles of drug action modeling.
{"title":"ailSemantic Web-based data representation and reasoning applied to disease mechanism and pharmacology","authors":"Xiaoyan A. Qu, R. C. Gudivada, A. Jegga, Eric K. Neumann, Bruce J. Aronow","doi":"10.1109/BIBMW.2007.4425411","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425411","url":null,"abstract":"To pursue a systematic approach to the discovery of novel and inferable relationships between drugs and diseases based on mechanistic knowledge, we have sought to apply semantic Web-based technologies to integrate heterogeneous data from pharmacological and biological domains. We have devised a knowledge framework, Disease-Drug Correlation Ontology (DDCO), constructed for semantic representation of the key entities and relationships. A collection of prior knowledge sets including pharmacological substance, drug target, pathway, disease and clinical features, and all interlinking properties were integrated using an RDF (resource description framework) model derived from the semantic elements defined in the DDCO framework. Using the resulting RDF graph network, ontology-based mining and queries could identify embedded associations in this genome-phenome-pharmacome network. Several use-cases demonstrated that potentially powerful rewards could be obtained through semantic integration based on principles of drug action modeling.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133375794","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 : 2007-11-01DOI: 10.1109/BIBMW.2007.4425417
Jong Youl Choi, Youngik Yang, Sun Kim, Dennis Gannon
Recent development of genome and gene analysis technology enabled rapid accumulation of biological data. To utilize such huge data, a biologist needs to have resource-rich computing environment and user-friendly analysis tool invocation. To response such requirements, we designed and implemented a virtual lab, named Virtual Collaborative Lab (V-Lab-Protein), using an efficient and flexible computing resource management and workflow engine with a user-friendly graphical workflow composer. Utility of our system is demonstrated by analyzing sample protein sequence sets. This is the first system of its kind that combines flexible workflow systems and on-demand compute and data resources (Amazon EC2/S3 in this case). We believe that this system design principle will be a new and effective paradigm for small biology research labs to handle the ever-increasing biological data.
{"title":"V-Lab-Protein: Virtual Collaborative Lab for protein sequence analysis","authors":"Jong Youl Choi, Youngik Yang, Sun Kim, Dennis Gannon","doi":"10.1109/BIBMW.2007.4425417","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425417","url":null,"abstract":"Recent development of genome and gene analysis technology enabled rapid accumulation of biological data. To utilize such huge data, a biologist needs to have resource-rich computing environment and user-friendly analysis tool invocation. To response such requirements, we designed and implemented a virtual lab, named Virtual Collaborative Lab (V-Lab-Protein), using an efficient and flexible computing resource management and workflow engine with a user-friendly graphical workflow composer. Utility of our system is demonstrated by analyzing sample protein sequence sets. This is the first system of its kind that combines flexible workflow systems and on-demand compute and data resources (Amazon EC2/S3 in this case). We believe that this system design principle will be a new and effective paradigm for small biology research labs to handle the ever-increasing biological data.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114393438","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}
In this paper, we present a method which only employs protein primary structure to predict protein-protein interactions. The statistical method is used to generate sequence features, which are normalized for satisfying experiments. Six parameters of physicochemical properties are calculated for each protein, including assessable residues, buried residues, hydrophobility, molecular weight, polarity and average area buried. The sequence features are extracted both from interaction proteins and non-interaction proteins. And BP neural network is used to classify two kinds of protein. The statistical evaluation of the BP neural network classifier shows that it performs well above 87% accuracy rate through 10-fold cross-validation. 2000 sequences which come from Scerevisiae yeast dataset are classified in our experimentation. The results demonstrate that 1780 sequences are classified correctly, which show that our proposed method is effective and feasible.
{"title":"Predicting protein-protein interactions based on BP neural network","authors":"Zhiqiang Ma, Chunguang Zhou, Linying Lu, Yanan Ma, Pingping Sun, Ying Cui","doi":"10.1109/BIBMW.2007.4425393","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425393","url":null,"abstract":"In this paper, we present a method which only employs protein primary structure to predict protein-protein interactions. The statistical method is used to generate sequence features, which are normalized for satisfying experiments. Six parameters of physicochemical properties are calculated for each protein, including assessable residues, buried residues, hydrophobility, molecular weight, polarity and average area buried. The sequence features are extracted both from interaction proteins and non-interaction proteins. And BP neural network is used to classify two kinds of protein. The statistical evaluation of the BP neural network classifier shows that it performs well above 87% accuracy rate through 10-fold cross-validation. 2000 sequences which come from Scerevisiae yeast dataset are classified in our experimentation. The results demonstrate that 1780 sequences are classified correctly, which show that our proposed method is effective and feasible.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129882593","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 : 2007-11-01DOI: 10.1109/BIBMW.2007.4425402
Y. Lu, C. Strauss, Jing He
We have developed a new method for adding constraints derived from low resolution density maps to Rosetta ab initio prediction method. This method incorporates the geometrical constraints of the helix skeleton that can be detected from a low resolution density map. We propose a 2-stage approach to predict the backbone of a protein from a low resolution map. In stage one, a small set of possible topologies will be predicted for the helix skeleton [1]. This paper describes the second stage that is to predict the backbone of the protein from a low resolution density map. A constraint scoring function was developed and incorporated in the Rosetta simulation process. The entire density map is only used for the final selection among the possible backbones that satisfy the constraints. Our method was tested with 16 mainly alpha-helical proteins ranging from 50 to 150 residues. 12 of the 16 proteins show improved accuracy for both the top 1 prediction and the best of the top 5 predictions. The average improvement of the RMSD to native is 4.76 A for the top 1 model and 3.05 A for the best of the top 5 ranked models when the density map is applied.
{"title":"Incorporating constraints from low resolution density map in ab initio structure prediction using Rosetta","authors":"Y. Lu, C. Strauss, Jing He","doi":"10.1109/BIBMW.2007.4425402","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425402","url":null,"abstract":"We have developed a new method for adding constraints derived from low resolution density maps to Rosetta ab initio prediction method. This method incorporates the geometrical constraints of the helix skeleton that can be detected from a low resolution density map. We propose a 2-stage approach to predict the backbone of a protein from a low resolution map. In stage one, a small set of possible topologies will be predicted for the helix skeleton [1]. This paper describes the second stage that is to predict the backbone of the protein from a low resolution density map. A constraint scoring function was developed and incorporated in the Rosetta simulation process. The entire density map is only used for the final selection among the possible backbones that satisfy the constraints. Our method was tested with 16 mainly alpha-helical proteins ranging from 50 to 150 residues. 12 of the 16 proteins show improved accuracy for both the top 1 prediction and the best of the top 5 predictions. The average improvement of the RMSD to native is 4.76 A for the top 1 model and 3.05 A for the best of the top 5 ranked models when the density map is applied.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133153289","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 : 2007-11-01DOI: 10.1109/BIBMW.2007.4425418
Meeyoung Park, Jubin Sanghvi, D. Dinakarpandian
The identification of putative motifs in biomolecular sequences or whole genomes/proteomes is frequently based on window-based scanning with position frequency matrices (PFMs). The exponential increase in the amount of sequence data and the growing number of patterns to be screened has resulted in the need for rapid screening methods. In recognition of this, we have developed the Indexed DAG of regular expressions extractor (INDARE), a tool that dynamically extracts regular expressions (REs) for each PFM in the database, and creates a directed acyclic graph of REs. The INDARE generated DAG is very effective in pruning the search space and easily outperforms the naive exhaustive sequential search approach. The method is general enough to be applicable for the identification of motifs in any domain.
{"title":"INDARE - An indexed DAG of regular expressions for selecting position frequency matrices","authors":"Meeyoung Park, Jubin Sanghvi, D. Dinakarpandian","doi":"10.1109/BIBMW.2007.4425418","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425418","url":null,"abstract":"The identification of putative motifs in biomolecular sequences or whole genomes/proteomes is frequently based on window-based scanning with position frequency matrices (PFMs). The exponential increase in the amount of sequence data and the growing number of patterns to be screened has resulted in the need for rapid screening methods. In recognition of this, we have developed the Indexed DAG of regular expressions extractor (INDARE), a tool that dynamically extracts regular expressions (REs) for each PFM in the database, and creates a directed acyclic graph of REs. The INDARE generated DAG is very effective in pruning the search space and easily outperforms the naive exhaustive sequential search approach. The method is general enough to be applicable for the identification of motifs in any domain.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"55 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132425773","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 : 2007-11-01DOI: 10.1109/BIBMW.2007.4425401
F. Towfic, Cornelia Caragea, D. Dobbs, D. Gemperline, Feihong Wu, Vasant G Honavar
We explore whether protein-RNA interfaces differ from non-interfaces in terms of their structural features and whether structural features vary according to the type of the bound RNA (e.g., mRNA, siRNA...etc), using a non-redundant dataset of 147 protein chains extracted from protein-RNA complexes in the protein data bank. Our analysis of surface roughness, solid angle and CX value of amino acid residues for each of the protein chains in the dataset shows that: The protein-RNA interface residues tend to be protruding compared to non-interface residues and tend to have higher surface roughness and exhibit moderately convex or concave solid angles. Furthermore, the protein chains in protein-RNA interfaces that contain Viral RNA and rRNA significantly differ from those that contain dsRNA, mRNA siRNA, snRNA, SRP RNA and tRNA with respect to their CX values. The results of this analysis sug gests the possibility of using such structural features to reliably identify protein-RNA interface residues when the structure of the protein is available but the structures of complexes formed by the protein with RNA are not.
{"title":"Structural characterization of RNA-binding sites of proteins: Preliminary results","authors":"F. Towfic, Cornelia Caragea, D. Dobbs, D. Gemperline, Feihong Wu, Vasant G Honavar","doi":"10.1109/BIBMW.2007.4425401","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425401","url":null,"abstract":"We explore whether protein-RNA interfaces differ from non-interfaces in terms of their structural features and whether structural features vary according to the type of the bound RNA (e.g., mRNA, siRNA...etc), using a non-redundant dataset of 147 protein chains extracted from protein-RNA complexes in the protein data bank. Our analysis of surface roughness, solid angle and CX value of amino acid residues for each of the protein chains in the dataset shows that: The protein-RNA interface residues tend to be protruding compared to non-interface residues and tend to have higher surface roughness and exhibit moderately convex or concave solid angles. Furthermore, the protein chains in protein-RNA interfaces that contain Viral RNA and rRNA significantly differ from those that contain dsRNA, mRNA siRNA, snRNA, SRP RNA and tRNA with respect to their CX values. The results of this analysis sug gests the possibility of using such structural features to reliably identify protein-RNA interface residues when the structure of the protein is available but the structures of complexes formed by the protein with RNA are not.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116980605","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 : 2007-11-01DOI: 10.1109/BIBMW.2007.4425398
A. D. Palù, Enrico Pontelli, A. Dovier
This paper investigates alternative global constraints that can be introduced in a constraint solver over discrete crystal lattices. The objective is to enhance the efficiency of lattice solvers in dealing with the construction of approximate solutions of the protein structure determination problem. The paper discusses various alternatives and provides preliminary results concerning the computational properties of the different global constraints.
{"title":"Enhancing the computation of approximate solutions of the protein structure determination problem through global constraints for discrete crystal lattices","authors":"A. D. Palù, Enrico Pontelli, A. Dovier","doi":"10.1109/BIBMW.2007.4425398","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425398","url":null,"abstract":"This paper investigates alternative global constraints that can be introduced in a constraint solver over discrete crystal lattices. The objective is to enhance the efficiency of lattice solvers in dealing with the construction of approximate solutions of the protein structure determination problem. The paper discusses various alternatives and provides preliminary results concerning the computational properties of the different global constraints.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130714603","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 : 2007-11-01DOI: 10.1109/BIBMW.2007.4425407
A. Tegge, S. Rodriguez-Zas, J. Sweedler, B. Southey
Bioinformatic predictions of neuropeptides resulting from enzymatic cleavages of precursors enable a range of follow-up studies that are aided by accurate predictions. A comparative study of the performance of complementary cleavage prediction models has been undertaken. Binary logistic and artificial neural network (ANN) models were created using various strategies and trained and tested on bovine and rat precursors with experimental cleavage information. Multiple criteria were used to compare 4 logistic regression models with varying properties and 8 ANN with varying structures. All models had high specificity (>90%) and sensitivity ranged from 68% to 100%. ANN based on well-represented amino acid locations performed similarly or slightly worse than networks based on all amino acid locations. Logistic parameter estimates aided in the identification of amino acids associated with cleavage. No model was superior across data sets and thus, prediction of neuropeptides should rely on multiple model specifications and comprehensive training data sets.
{"title":"Comparative analysis of binary logistic regression to artificial neural networks in predicting precursor sequence cleavage","authors":"A. Tegge, S. Rodriguez-Zas, J. Sweedler, B. Southey","doi":"10.1109/BIBMW.2007.4425407","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425407","url":null,"abstract":"Bioinformatic predictions of neuropeptides resulting from enzymatic cleavages of precursors enable a range of follow-up studies that are aided by accurate predictions. A comparative study of the performance of complementary cleavage prediction models has been undertaken. Binary logistic and artificial neural network (ANN) models were created using various strategies and trained and tested on bovine and rat precursors with experimental cleavage information. Multiple criteria were used to compare 4 logistic regression models with varying properties and 8 ANN with varying structures. All models had high specificity (>90%) and sensitivity ranged from 68% to 100%. ANN based on well-represented amino acid locations performed similarly or slightly worse than networks based on all amino acid locations. Logistic parameter estimates aided in the identification of amino acids associated with cleavage. No model was superior across data sets and thus, prediction of neuropeptides should rely on multiple model specifications and comprehensive training data sets.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130639740","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 : 2007-11-01DOI: 10.1109/BIBMW.2007.4425414
Chi-Chang Chang
In medical decision making, the event of primary interest is recurrent, so that for a given unit the event could be observed more than once during the study. In general, the successive times between failures of human physiological systems are not necessarily identically distributed. However, if any critical deterioration is detected, then the decision of when to take the intervention, given the costs of diagnosis and therapeutics, is of fundamental importance. In this paper, Bayesian inference of a nonhomogeneous Poisson process with power law failure intensity function is used to describe the behavior of aging physiological systems with aging chronic disease. In addition, we illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease. Finally, this paper develops a systematic way to integrate the expert's opinions which will furnish decision makers with valuable support for quality medical decision making.
{"title":"A Bayesian approach for the analysis of recurrent events with chronic granulomatous disease","authors":"Chi-Chang Chang","doi":"10.1109/BIBMW.2007.4425414","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425414","url":null,"abstract":"In medical decision making, the event of primary interest is recurrent, so that for a given unit the event could be observed more than once during the study. In general, the successive times between failures of human physiological systems are not necessarily identically distributed. However, if any critical deterioration is detected, then the decision of when to take the intervention, given the costs of diagnosis and therapeutics, is of fundamental importance. In this paper, Bayesian inference of a nonhomogeneous Poisson process with power law failure intensity function is used to describe the behavior of aging physiological systems with aging chronic disease. In addition, we illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease. Finally, this paper develops a systematic way to integrate the expert's opinions which will furnish decision makers with valuable support for quality medical decision making.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130670693","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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