Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188962
Wei Li, Jeffrey D. Johnson
We present a computational model of approach teaming in a T-maze environment. We show that our model learns the correct sequence of six decisions that lead to the location of positive reinforcement and in a manner consistent with experimental observations. Our model exhibits many properties that are characteristic of animal learning in maze environments including delay conditioning, secondary conditioning, and backward chaining. Our model incorporates a comprehensive definition of drive that consists of a primary drive (food) and deficit-related signal (hunger), and an acquired drive (the learned expectation for future reward or punishment). In the T-maze environment, the deficit-related drive of hunger motivates the teaming system to search for food. After several trials in the T-maze, the acquired drive (learned expectation) will shape the teaming system's behavior and allow it to consistently find the food. We propose that changes in drive level, not merely the level of the drive, lead to teaming. Positive changes in drive level results in the enhanced behavior and negative changes result in the depressed behavior. Our comprehensive definition of drive allows us to explain teaming in a biologically plausible manner and is supported by results from hypertension, obesity, and Parkinson's disease research.
{"title":"Computational model of the role of deficit-related drives in sequential movement learning in a T-maze environment","authors":"Wei Li, Jeffrey D. Johnson","doi":"10.1109/BIBE.2003.1188962","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188962","url":null,"abstract":"We present a computational model of approach teaming in a T-maze environment. We show that our model learns the correct sequence of six decisions that lead to the location of positive reinforcement and in a manner consistent with experimental observations. Our model exhibits many properties that are characteristic of animal learning in maze environments including delay conditioning, secondary conditioning, and backward chaining. Our model incorporates a comprehensive definition of drive that consists of a primary drive (food) and deficit-related signal (hunger), and an acquired drive (the learned expectation for future reward or punishment). In the T-maze environment, the deficit-related drive of hunger motivates the teaming system to search for food. After several trials in the T-maze, the acquired drive (learned expectation) will shape the teaming system's behavior and allow it to consistently find the food. We propose that changes in drive level, not merely the level of the drive, lead to teaming. Positive changes in drive level results in the enhanced behavior and negative changes result in the depressed behavior. Our comprehensive definition of drive allows us to explain teaming in a biologically plausible manner and is supported by results from hypertension, obesity, and Parkinson's disease research.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130963004","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 : 2003-03-10DOI: 10.1109/BIBE.2003.1188930
Jeffrey D. Johnson, D. Plenz, John M. Beggs, Wei Li, M. Mieier, N. Miltner, K. Owe
Multi-microelectrode array devices can be used to simultaneously record activity from multiple neurons distributed in a tissue slice. One of the brain functions being investigated with microelectrode arrays is the periodic behavior of spontaneously active neurons in the cortex and basal ganglia.. However, these recording methods generate several hundred megabytes of data per hour and, currently, there is no efficient and accurate approach for the identification of the repeated pattern. We present an approach that uses the discrete wavelet transform to accelerate identification of repeating patterns of neural activity. We perform match filtering on the coefficient data, not the time-domain data. Our wavelet approach operates on 1/4 the data but provides similar classification abilities as the time domain correlation.
{"title":"Analysis of spontaneous activity in cultured brain tissue using the discrete wavelet transform","authors":"Jeffrey D. Johnson, D. Plenz, John M. Beggs, Wei Li, M. Mieier, N. Miltner, K. Owe","doi":"10.1109/BIBE.2003.1188930","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188930","url":null,"abstract":"Multi-microelectrode array devices can be used to simultaneously record activity from multiple neurons distributed in a tissue slice. One of the brain functions being investigated with microelectrode arrays is the periodic behavior of spontaneously active neurons in the cortex and basal ganglia.. However, these recording methods generate several hundred megabytes of data per hour and, currently, there is no efficient and accurate approach for the identification of the repeated pattern. We present an approach that uses the discrete wavelet transform to accelerate identification of repeating patterns of neural activity. We perform match filtering on the coefficient data, not the time-domain data. Our wavelet approach operates on 1/4 the data but provides similar classification abilities as the time domain correlation.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123603944","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 : 2003-03-10DOI: 10.1109/BIBE.2003.1188980
Chyun-Shin Cheng, Shiuan-Sz Wang
Facing the development of microarray technology, clustering is currently a leading technique to gene expression data analysis. In this paper we propose a novel algorithm called repulsive clustering, which is developed for the use of gene expression data analysis. Our performance demonstration on several synthetic and real gene expression data sets show that the repulsive clustering algorithm, compared with some other well-known clustering algorithms, is capable of not only producing even higher quality output, but also easier to implement for immediate use on various situations.
{"title":"A repulsive clustering algorithm for gene expression data","authors":"Chyun-Shin Cheng, Shiuan-Sz Wang","doi":"10.1109/BIBE.2003.1188980","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188980","url":null,"abstract":"Facing the development of microarray technology, clustering is currently a leading technique to gene expression data analysis. In this paper we propose a novel algorithm called repulsive clustering, which is developed for the use of gene expression data analysis. Our performance demonstration on several synthetic and real gene expression data sets show that the repulsive clustering algorithm, compared with some other well-known clustering algorithms, is capable of not only producing even higher quality output, but also easier to implement for immediate use on various situations.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115047899","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 : 2003-03-10DOI: 10.1109/BIBE.2003.1188974
Ozgur Ozturk, H. Ferhatosmanoğlu
We present a multi-dimensional indexing approach for fast sequence similarity search in DNA and protein databases. In particular, we propose effective transformations of subsequences into numerical vector domains and build efficient index structures on the transformed vectors. We then define distance functions in the transformed domain and examine properties of these functions. We experimentally compared their (a) approximation quality for k-Nearest Neighbor (k-NN) queries, (b) pruning ability and (c) approximation quality for E-range queries. Results for k-NN queries, which we present here, show that our proposed distances FD2 and WD2 (i.e. Frequency and Wavelet Distance functions for 2-grams) perform significantly better than the others. We then develop effective index structures, based on R-trees and scalar quantization, on top of transformed vectors and distance functions. Promising results from the experiments on real biosequence data sets are presented.
{"title":"Effective indexing and filtering for similarity search in large biosequence databases","authors":"Ozgur Ozturk, H. Ferhatosmanoğlu","doi":"10.1109/BIBE.2003.1188974","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188974","url":null,"abstract":"We present a multi-dimensional indexing approach for fast sequence similarity search in DNA and protein databases. In particular, we propose effective transformations of subsequences into numerical vector domains and build efficient index structures on the transformed vectors. We then define distance functions in the transformed domain and examine properties of these functions. We experimentally compared their (a) approximation quality for k-Nearest Neighbor (k-NN) queries, (b) pruning ability and (c) approximation quality for E-range queries. Results for k-NN queries, which we present here, show that our proposed distances FD2 and WD2 (i.e. Frequency and Wavelet Distance functions for 2-grams) perform significantly better than the others. We then develop effective index structures, based on R-trees and scalar quantization, on top of transformed vectors and distance functions. Promising results from the experiments on real biosequence data sets are presented.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132740458","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 : 2003-03-10DOI: 10.1109/BIBE.2003.1188932
T. Williams, Bernard M. E. Moret
We analyze the performance of likelihood-based approaches used to reconstruct phylogenetic trees. Unlike other techniques such as Neighbor-joining (NJ) and Maximum Parsimony (MP), relatively little is known regarding the behavior of algorithms founded on the principle of likelihood. We study the accuracy, speed, and likelihood scores of four representative likelihood-based methods (fastDNAml, Mr Bayes, PAUP*-ML, and TREE-PUZZLE) that use either Maximum Likelihood (ML) or Bayesian inference to find the optimal tree. NJ is also studied to provide a baseline comparison. Our simulation study is based on random birth-death trees, which are deviated from ultrametricity, and uses the Kimura 2-parameter +Gamma model of sequence evolution. We find that Mr Bayes (a Bayesian inference approach) consistently outperforms the other methods in terms of accuracy and running time.
{"title":"An investigation of phylogenetic likelihood methods","authors":"T. Williams, Bernard M. E. Moret","doi":"10.1109/BIBE.2003.1188932","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188932","url":null,"abstract":"We analyze the performance of likelihood-based approaches used to reconstruct phylogenetic trees. Unlike other techniques such as Neighbor-joining (NJ) and Maximum Parsimony (MP), relatively little is known regarding the behavior of algorithms founded on the principle of likelihood. We study the accuracy, speed, and likelihood scores of four representative likelihood-based methods (fastDNAml, Mr Bayes, PAUP*-ML, and TREE-PUZZLE) that use either Maximum Likelihood (ML) or Bayesian inference to find the optimal tree. NJ is also studied to provide a baseline comparison. Our simulation study is based on random birth-death trees, which are deviated from ultrametricity, and uses the Kimura 2-parameter +Gamma model of sequence evolution. We find that Mr Bayes (a Bayesian inference approach) consistently outperforms the other methods in terms of accuracy and running time.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122655867","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 : 2003-03-10DOI: 10.1109/BIBE.2003.1188975
William M. Shui, R. Wong
Automating the process of information retrieval and integration of heterogeneous biological data is complex and difficult. This paper describes an approach to solve this problem by using XML technologies such as XML Schema and an XML-based active rules system. Current limitations of active rule system for XML databases are discussed. We then propose a template for defining rules that is consistent with the current XQuery specification, a defacto standard language for querying XML data. Finally, an example scenario is used to illustrate how these techniques can come together in integrating heterogeneous biological data sources.
{"title":"Application of XML Schema and active rules system in management and integration of heterogeneous biological data","authors":"William M. Shui, R. Wong","doi":"10.1109/BIBE.2003.1188975","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188975","url":null,"abstract":"Automating the process of information retrieval and integration of heterogeneous biological data is complex and difficult. This paper describes an approach to solve this problem by using XML technologies such as XML Schema and an XML-based active rules system. Current limitations of active rule system for XML databases are discussed. We then propose a template for defining rules that is consistent with the current XQuery specification, a defacto standard language for querying XML data. Finally, an example scenario is used to illustrate how these techniques can come together in integrating heterogeneous biological data sources.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121023164","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 : 2003-03-10DOI: 10.1109/BIBE.2003.1188939
William M. Shui, N. Lam, R. Wong
Ability to keep track of records for various biological experiments allows for future validation of the current experiments and other non-experimental laboratory procedures. With the increasing popularity of publishing biological data in XML format, there arises the need for the control and management of this data, as well as dynamically exporting this data to various formats for reporting purposes. As such data is constantly changing, users want to be able to query previous versions, plotting data across different versions from history, query changes in documents, as well as to retrieve a particular document version efficiently. This paper proposes an XML-based version management system for tracking and analyzing data obtained from any laboratory experiments in an effective and meaningful manner. This includes experiments ranging from genomic, proteomic and protein structural. We also present methods for importing non XML data into the system as well as generating reports in multiple formats dynamically.
{"title":"A novel laboratory version management system for tracking complex biological experiments","authors":"William M. Shui, N. Lam, R. Wong","doi":"10.1109/BIBE.2003.1188939","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188939","url":null,"abstract":"Ability to keep track of records for various biological experiments allows for future validation of the current experiments and other non-experimental laboratory procedures. With the increasing popularity of publishing biological data in XML format, there arises the need for the control and management of this data, as well as dynamically exporting this data to various formats for reporting purposes. As such data is constantly changing, users want to be able to query previous versions, plotting data across different versions from history, query changes in documents, as well as to retrieve a particular document version efficiently. This paper proposes an XML-based version management system for tracking and analyzing data obtained from any laboratory experiments in an effective and meaningful manner. This includes experiments ranging from genomic, proteomic and protein structural. We also present methods for importing non XML data into the system as well as generating reports in multiple formats dynamically.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116704629","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 : 2003-03-10DOI: 10.1109/BIBE.2003.1188968
Yunlong Liu, H. Sun, H. Yokota
We described development of a novel genome-based model-driven strategy useful for regulating eukaryotic gene expression. In order to extract biologically meaningful information from a large volume of mRNA expression data, we built previously a PROmoter-Based Estimation (PROBE) model. The PROBE model allowed us to establish a quantitative relationship between transcription-factor binding motifs in regulatory DNA sequences and mRNA expression levels. Here, we extended PROBE formulation to derive an optimal control law for gene regulation. The responses to shear stress in human synovial cells were chosen as a model biological system, and the system dynamics was identified from the expression pattern of the genes involved in degradation and maintenance of extracellular matrix. In order to suppress the responses to mechanical stimuli, a Ricatti equation was solved and an admissible control law was derived. The approach presented here can be implemented in any biological process, and it would be useful to develop a transcription-mediated strategy for gene therapies and tissue engineering.
{"title":"Regulating gene expression using optimal control theory","authors":"Yunlong Liu, H. Sun, H. Yokota","doi":"10.1109/BIBE.2003.1188968","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188968","url":null,"abstract":"We described development of a novel genome-based model-driven strategy useful for regulating eukaryotic gene expression. In order to extract biologically meaningful information from a large volume of mRNA expression data, we built previously a PROmoter-Based Estimation (PROBE) model. The PROBE model allowed us to establish a quantitative relationship between transcription-factor binding motifs in regulatory DNA sequences and mRNA expression levels. Here, we extended PROBE formulation to derive an optimal control law for gene regulation. The responses to shear stress in human synovial cells were chosen as a model biological system, and the system dynamics was identified from the expression pattern of the genes involved in degradation and maintenance of extracellular matrix. In order to suppress the responses to mechanical stimuli, a Ricatti equation was solved and an admissible control law was derived. The approach presented here can be implemented in any biological process, and it would be useful to develop a transcription-mediated strategy for gene therapies and tissue engineering.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121705442","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 : 2003-03-10DOI: 10.1109/BIBE.2003.1188927
P. Clote
We generalize the familiar position-specific score matrix (PSSM), aka weight matrix, by considering a log-odds score for (nonadjacent) k-tuple frequencies, each k-tuple score weighted by the product of its mutual information and its statistical significance, as measured by a point estimator for the p-value of the mutual information. Performance of this new approach, along with other variants of generalized PSSM and profile methods, is measured by receiver-operating characteristic (ROC) curves for the specific problem of signal peptide cleavage site recognition. We additionally compare Vert's recent support vector machine string kernel, Brown's joint probability approximation algorithm and the method WAM. Similar algorithm comparisons are made, though not as extensively, in the case of disulfide bond recognition. While in the case of signal peptide cleavage site recognition, the monoresidue PSSM is essentially competitive, within the limits of statistical significance, even against Vert's support vector machine kernel, diresidue and triresidue PSSM methods display improved performance over monoresidue PSSM for disulfide bond recognition.
{"title":"Performance comparison of generalized PSSM in in signal peptide cleavage site and disulfide bond recognition","authors":"P. Clote","doi":"10.1109/BIBE.2003.1188927","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188927","url":null,"abstract":"We generalize the familiar position-specific score matrix (PSSM), aka weight matrix, by considering a log-odds score for (nonadjacent) k-tuple frequencies, each k-tuple score weighted by the product of its mutual information and its statistical significance, as measured by a point estimator for the p-value of the mutual information. Performance of this new approach, along with other variants of generalized PSSM and profile methods, is measured by receiver-operating characteristic (ROC) curves for the specific problem of signal peptide cleavage site recognition. We additionally compare Vert's recent support vector machine string kernel, Brown's joint probability approximation algorithm and the method WAM. Similar algorithm comparisons are made, though not as extensively, in the case of disulfide bond recognition. While in the case of signal peptide cleavage site recognition, the monoresidue PSSM is essentially competitive, within the limits of statistical significance, even against Vert's support vector machine kernel, diresidue and triresidue PSSM methods display improved performance over monoresidue PSSM for disulfide bond recognition.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"374 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122772236","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 : 2003-03-10DOI: 10.1109/BIBE.2003.1188925
Yu Chen, T. Joshi, Ying Xu, Dong Xu
Characterizing biological pathways at the genome scale is one of the most important and challenging tasks in the post genomic era. To address this challenge, we have developed a computational method to systematically and automatically derive partial biological pathways in yeast using high-throughput biological data, including yeast two hybrid data, protein complexes identified from mass spectroscopy, genetics interactions, and microarray gene expression data in yeast Saccharomyces cerevisiae. The inputs of the method are the upstream starting protein (e.g., a sensor of a signal) and the downstream terminal protein (e.g., a transcriptional factor that induces genes to respond the signal); the output of the method is the protein interaction chain between the two proteins. The high-throughput data are coded into a graph of interaction network, where each node represents a protein. The weight of an edge between two nodes models the "closeness" of the two represented proteins in the interaction network and it is defined by a rule-based formula according to the high-throughput data and modified by the protein function classification and subcellular localization information. The protein interaction cascade pathway in vivo is predicted as the shortest path identified from the graph of the interaction network using Dijkstra's algorithm. We have also developed a web server of this method (http://compbio.ornl.gov/structure/pathway) for public use. To our knowledge, our method is the first automated method to generally construct partial biological pathways using a suite of high-throughput biological data. This work demonstrates the proof of principle using computational approaches for discoveries of biological pathways with high-throughput data and biological annotation data.
{"title":"Towards automated derivation of biological pathways using high-throughput biological data","authors":"Yu Chen, T. Joshi, Ying Xu, Dong Xu","doi":"10.1109/BIBE.2003.1188925","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188925","url":null,"abstract":"Characterizing biological pathways at the genome scale is one of the most important and challenging tasks in the post genomic era. To address this challenge, we have developed a computational method to systematically and automatically derive partial biological pathways in yeast using high-throughput biological data, including yeast two hybrid data, protein complexes identified from mass spectroscopy, genetics interactions, and microarray gene expression data in yeast Saccharomyces cerevisiae. The inputs of the method are the upstream starting protein (e.g., a sensor of a signal) and the downstream terminal protein (e.g., a transcriptional factor that induces genes to respond the signal); the output of the method is the protein interaction chain between the two proteins. The high-throughput data are coded into a graph of interaction network, where each node represents a protein. The weight of an edge between two nodes models the \"closeness\" of the two represented proteins in the interaction network and it is defined by a rule-based formula according to the high-throughput data and modified by the protein function classification and subcellular localization information. The protein interaction cascade pathway in vivo is predicted as the shortest path identified from the graph of the interaction network using Dijkstra's algorithm. We have also developed a web server of this method (http://compbio.ornl.gov/structure/pathway) for public use. To our knowledge, our method is the first automated method to generally construct partial biological pathways using a suite of high-throughput biological data. This work demonstrates the proof of principle using computational approaches for discoveries of biological pathways with high-throughput data and biological annotation data.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131281054","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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