Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188982
G. Querrec, V. Rodin, J. Abgrall, S. Kerdélo, J. Tisseau
Since emergence of molecular biology, one has improved knowledge about intracellular networks controlling cell behavior. In parallel, advances in mathematic and computer science allow one to simulate such complex phenomena. Moreover, most methods need a global resolution of the system which makes it difficult to be created and modified. We proposed, in this study, a distributed approach by multiagent system (MAS), to simulate the MAPK pathway. Our results show that such simulation is possible and allows "in virtuo" experimentation, i.e. model perturbation during its execution.
{"title":"Uses of multiagents systems for simulation of MAPK pathway","authors":"G. Querrec, V. Rodin, J. Abgrall, S. Kerdélo, J. Tisseau","doi":"10.1109/BIBE.2003.1188982","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188982","url":null,"abstract":"Since emergence of molecular biology, one has improved knowledge about intracellular networks controlling cell behavior. In parallel, advances in mathematic and computer science allow one to simulate such complex phenomena. Moreover, most methods need a global resolution of the system which makes it difficult to be created and modified. We proposed, in this study, a distributed approach by multiagent system (MAS), to simulate the MAPK pathway. Our results show that such simulation is possible and allows \"in virtuo\" experimentation, i.e. model perturbation during its execution.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"66 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":"129101263","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.2003.1188933
John Bluis, Dong-Guk Shin
Maintaining a phylogenetic relationship repository requires the development of tools that are useful for mining the data stored in the repository. One way to query a database of phylogenetic information would be to compare phylogenetic trees. Because the only existing tree comparison methods are computationally intensive, this is not a reasonable task. Presented here is the nodal distance algorithm which has significantly less computation time than the most widely used comparison method, the partition metric. When the metric is calculated for trees where one species has been repositioned to a distant part of the tree no further computation is required as is needed for the partition metric. The nodal distance algorithm provides a method for comparing large sets of phylogenetic trees in a reasonable amount of time.
{"title":"Nodal distance algorithm: calculating a phylogenetic tree comparison metric","authors":"John Bluis, Dong-Guk Shin","doi":"10.1109/BIBE.2003.1188933","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188933","url":null,"abstract":"Maintaining a phylogenetic relationship repository requires the development of tools that are useful for mining the data stored in the repository. One way to query a database of phylogenetic information would be to compare phylogenetic trees. Because the only existing tree comparison methods are computationally intensive, this is not a reasonable task. Presented here is the nodal distance algorithm which has significantly less computation time than the most widely used comparison method, the partition metric. When the metric is calculated for trees where one species has been repositioned to a distant part of the tree no further computation is required as is needed for the partition metric. The nodal distance algorithm provides a method for comparing large sets of phylogenetic trees in a reasonable amount of time.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"28 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":"123026454","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.2003.1188929
Xin Huang, Shu‐Ching Chen, M. Shyu
In this paper, a powerful open Multiple Instance Learning (MIL) framework is proposed. Such an open framework is powerful since different sub-methods can be plugged into the framework to generate different specific Multiple Instance Learning algorithms. In our proposed framework, the Multiple Instance Learning problem is first converted to an unconstrained optimization problem by the Minimum Square Error (MSE) criterion, and then the framework can be constructed with an open form of hypothesis and gradient search method. The proposed Multiple Instance Learning framework is applied to the drug activity problems in bioinformatics applications. Specifically, experiments are conducted on the Musk-I dataset to predict the binding activity of drug molecules. In the experiments, an algorithm with the exponential hypothesis model and the Quasi-Newton method is embedded into our proposed framework. We compare our proposed framework with other existing algorithms and the experimental results show that our proposed framework yields a good accuracy of classification, which demonstrates the feasibility and effectiveness of our framework.
{"title":"An open multiple instance learning framework and its application in drug activity prediction problems","authors":"Xin Huang, Shu‐Ching Chen, M. Shyu","doi":"10.1109/BIBE.2003.1188929","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188929","url":null,"abstract":"In this paper, a powerful open Multiple Instance Learning (MIL) framework is proposed. Such an open framework is powerful since different sub-methods can be plugged into the framework to generate different specific Multiple Instance Learning algorithms. In our proposed framework, the Multiple Instance Learning problem is first converted to an unconstrained optimization problem by the Minimum Square Error (MSE) criterion, and then the framework can be constructed with an open form of hypothesis and gradient search method. The proposed Multiple Instance Learning framework is applied to the drug activity problems in bioinformatics applications. Specifically, experiments are conducted on the Musk-I dataset to predict the binding activity of drug molecules. In the experiments, an algorithm with the exponential hypothesis model and the Quasi-Newton method is embedded into our proposed framework. We compare our proposed framework with other existing algorithms and the experimental results show that our proposed framework yields a good accuracy of classification, which demonstrates the feasibility and effectiveness of our framework.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"51 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":"121361398","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.2003.1188957
C. Kirbas, Francis K. H. Quek
Vessel segmentation algorithms are critical components of circulatory blood vessel analysis systems. We present a survey of vessel extraction techniques and algorithms, putting the various approaches and techniques in perspective by means of a classification of the existing research. While we target mainly the extraction of blood vessels, neurovascular structure in particular we also review some of the segmentation methods for the tubular objects that show similar characteristics to vessels. We divide vessel segmentation algorithms and techniques into six main categories: (1) pattern recognition techniques, (2) model-based approaches, (3) tracking-based approaches, (4) artificial intelligence-based approaches, (5) neural network-based approaches, and (6) miscellaneous tube-like object detection approaches. Some of these categories are further divided into sub-categories. A table compares the papers against such criteria as dimensionality, input type, preprocessing, user interaction, and result type.
{"title":"Vessel extraction techniques and algorithms: a survey","authors":"C. Kirbas, Francis K. H. Quek","doi":"10.1109/BIBE.2003.1188957","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188957","url":null,"abstract":"Vessel segmentation algorithms are critical components of circulatory blood vessel analysis systems. We present a survey of vessel extraction techniques and algorithms, putting the various approaches and techniques in perspective by means of a classification of the existing research. While we target mainly the extraction of blood vessels, neurovascular structure in particular we also review some of the segmentation methods for the tubular objects that show similar characteristics to vessels. We divide vessel segmentation algorithms and techniques into six main categories: (1) pattern recognition techniques, (2) model-based approaches, (3) tracking-based approaches, (4) artificial intelligence-based approaches, (5) neural network-based approaches, and (6) miscellaneous tube-like object detection approaches. Some of these categories are further divided into sub-categories. A table compares the papers against such criteria as dimensionality, input type, preprocessing, user interaction, and result type.","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":"131809087","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.2003.1188946
B. Larson, N. Tsekos, A. Erdman
We have developed a device to perform minimally invasive interventions in the breast with realtime MRI guidance for the early detection and treatment of breast cancer. The device uses five computer-controlled degrees of freedom to perform minimally invasive interventions inside a closed MRI scanner. Typically the intervention would consist of a biopsy of the suspicious lesion for diagnosis, but may involve therapies to destroy or remove malignant tissue in the breast. The procedure proceeds with: (a) conditioning of the breast along a prescribed orientation, (b) definition of an insertion vector by its height and pitch angle, and (c) insertion into the breast. The entire device is made of materials compatible with MRI, avoiding artifacts and distortion of the local magnetic field. The device is remotely controlled via a graphical user interface. This is the first surgical robotic device to perform real-time MRI-guided breast interventions in the United States.
{"title":"A robotic device for minimally invasive breast interventions with real-time MRI guidance","authors":"B. Larson, N. Tsekos, A. Erdman","doi":"10.1109/BIBE.2003.1188946","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188946","url":null,"abstract":"We have developed a device to perform minimally invasive interventions in the breast with realtime MRI guidance for the early detection and treatment of breast cancer. The device uses five computer-controlled degrees of freedom to perform minimally invasive interventions inside a closed MRI scanner. Typically the intervention would consist of a biopsy of the suspicious lesion for diagnosis, but may involve therapies to destroy or remove malignant tissue in the breast. The procedure proceeds with: (a) conditioning of the breast along a prescribed orientation, (b) definition of an insertion vector by its height and pitch angle, and (c) insertion into the breast. The entire device is made of materials compatible with MRI, avoiding artifacts and distortion of the local magnetic field. The device is remotely controlled via a graphical user interface. This is the first surgical robotic device to perform real-time MRI-guided breast interventions in the United States.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"156 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":"116604188","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.2003.1188969
Jiong Yang, Haixun Wang, Wei Wang, Philip S. Yu
Microarrays are one of the latest breakthroughs in experimental molecular biology, which provide a powerful tool by which the expression patterns of thousands of genes can be monitored simultaneously and are already producing huge amount of valuable data. The concept of bicluster was introduced by Cheng and Church (2000) to capture the coherence of a subset of genes and a subset of conditions. A set of heuristic algorithms were also designed to either find one bicluster or a set of biclusters, which consist of iterations of masking null values and discovered biclusters, coarse and fine node deletion, node addition, and the inclusion of inverted data. These heuristics inevitably suffer from some serious drawback. The masking of null values and discovered biclusters with random numbers may result in the phenomenon of random interference which in turn impacts the discovery of high quality biclusters. To address this issue and to further accelerate the biclustering process, we generalize the model of bicluster to incorporate null values and propose a probabilistic algorithm (FLOC) that can discover a set of k possibly overlapping biclusters simultaneously. Furthermore, this algorithm can easily be extended to support additional features that suit different requirements at virtually little cost. Experimental study on the yeast gene expression data shows that the FLOC algorithm can offer substantial improvements over the previously proposed algorithm.
{"title":"Enhanced biclustering on expression data","authors":"Jiong Yang, Haixun Wang, Wei Wang, Philip S. Yu","doi":"10.1109/BIBE.2003.1188969","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188969","url":null,"abstract":"Microarrays are one of the latest breakthroughs in experimental molecular biology, which provide a powerful tool by which the expression patterns of thousands of genes can be monitored simultaneously and are already producing huge amount of valuable data. The concept of bicluster was introduced by Cheng and Church (2000) to capture the coherence of a subset of genes and a subset of conditions. A set of heuristic algorithms were also designed to either find one bicluster or a set of biclusters, which consist of iterations of masking null values and discovered biclusters, coarse and fine node deletion, node addition, and the inclusion of inverted data. These heuristics inevitably suffer from some serious drawback. The masking of null values and discovered biclusters with random numbers may result in the phenomenon of random interference which in turn impacts the discovery of high quality biclusters. To address this issue and to further accelerate the biclustering process, we generalize the model of bicluster to incorporate null values and propose a probabilistic algorithm (FLOC) that can discover a set of k possibly overlapping biclusters simultaneously. Furthermore, this algorithm can easily be extended to support additional features that suit different requirements at virtually little cost. Experimental study on the yeast gene expression data shows that the FLOC algorithm can offer substantial improvements over the previously proposed algorithm.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"8 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":"121084402","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.2003.1188931
M. Blanchette, Samson Kwong, M. Tompa
Phylogenetic footprinting is an increasingly popular comparative genomics method for detecting regulatory elements in DNA sequences. With the profusion of possible methods to use for phylogenetic footprinting, the biologist needs some guidance to choose the most appropriate tool. We present methods for comparing tools on phylogenetic footprinting data. More specifically, we discuss two different classes of comparative experiments: those on simulated data and those on real orthologous promoter regions. We then report the results of a series of such empirical comparisons. The tools compared are the alignment-based methods using ClustalW and Dialign, and the motif-finding programs MEME and FootPrinter. Our results show that methods taking the species' phylogenetic relationships into consideration obtain better accuracy.
{"title":"An empirical comparison of tools for phylogenetic footprinting","authors":"M. Blanchette, Samson Kwong, M. Tompa","doi":"10.1109/BIBE.2003.1188931","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188931","url":null,"abstract":"Phylogenetic footprinting is an increasingly popular comparative genomics method for detecting regulatory elements in DNA sequences. With the profusion of possible methods to use for phylogenetic footprinting, the biologist needs some guidance to choose the most appropriate tool. We present methods for comparing tools on phylogenetic footprinting data. More specifically, we discuss two different classes of comparative experiments: those on simulated data and those on real orthologous promoter regions. We then report the results of a series of such empirical comparisons. The tools compared are the alignment-based methods using ClustalW and Dialign, and the motif-finding programs MEME and FootPrinter. Our results show that methods taking the species' phylogenetic relationships into consideration obtain better accuracy.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"14 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":"130934847","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.2003.1188947
Fang-Xiang Wu, W. Zhang, A. Kusalik
In order to sequence a target DNA, it is first cleaved into many shorter overlapping fragments by chemical or physical techniques. The nucleotide sequence of each fragment is then determined (read) by established methods. The set of all read fragments which cover the target DNA sequence is called its spectrum. It is believed that the shortest superstring of a spectrum is the best candidate for the target DNA sequence. The general problem of finding the shortest superstring for any given set of strings s is NP-hard. Fortunately, the biological instance of this problem is easier. It is not likely that two read fragments, each consisting of several hundred letters, which come from consecutive locations on the target DNA sequence have an overlap of only a few letters; typically, the overlap will be longer. Thus one may reasonably assume that two strings in the spectrum have significant overlap (connectivity) if they come from consecutive locations on the target DNA sequence. A class of important instances satisfying this assumption are those whose spectra are from DNA microarrays. This assumption allows us to claim and show the following: if the spectrum S of a target DNA sequence is substring-free and connected at level t, and the target DNA sequence has no repeats of size t or larger, then there exists an algorithm to reconstruct the target DNA sequence in the linear time O(|S|) after an overlap graph of the spectrum is built.
{"title":"An algorithm to reconstruct a target DNA sequence from its spectrum connected at a given level","authors":"Fang-Xiang Wu, W. Zhang, A. Kusalik","doi":"10.1109/BIBE.2003.1188947","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188947","url":null,"abstract":"In order to sequence a target DNA, it is first cleaved into many shorter overlapping fragments by chemical or physical techniques. The nucleotide sequence of each fragment is then determined (read) by established methods. The set of all read fragments which cover the target DNA sequence is called its spectrum. It is believed that the shortest superstring of a spectrum is the best candidate for the target DNA sequence. The general problem of finding the shortest superstring for any given set of strings s is NP-hard. Fortunately, the biological instance of this problem is easier. It is not likely that two read fragments, each consisting of several hundred letters, which come from consecutive locations on the target DNA sequence have an overlap of only a few letters; typically, the overlap will be longer. Thus one may reasonably assume that two strings in the spectrum have significant overlap (connectivity) if they come from consecutive locations on the target DNA sequence. A class of important instances satisfying this assumption are those whose spectra are from DNA microarrays. This assumption allows us to claim and show the following: if the spectrum S of a target DNA sequence is substring-free and connected at level t, and the target DNA sequence has no repeats of size t or larger, then there exists an algorithm to reconstruct the target DNA sequence in the linear time O(|S|) after an overlap graph of the spectrum is built.","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":"134073678","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.2003.1188978
D. Jiang, J. Pei, A. Zhang
Clustering the time series gene expression data is an important task in bioinformatics research and biomedical applications. Recently, some clustering methods have been adapted or proposed. However, some concerns still remain, such as the robustness of the mining methods, as well as the quality and the interpretability of the mining results. In this paper, we tackle the problem of effectively clustering time series gene expression data by proposing algorithm DHC, a density-based, hierarchical clustering method. We use a density-based approach to identify the clusters such that the clustering results are of high quality and robustness. Moreover, the mining result is in the form of a density tree, which uncovers the embedded clusters in a data set. The inner-structures, the borders and the outliers of the clusters can be further investigated using the attraction tree, which is an intermediate result of the mining. By these two trees, the internal structure of the data set can be visualized effectively. Our empirical evaluation using some real-world data sets show that the method is effective, robust and scalable. It matches the ground truth provided by bioinformatics experts very well in the sample data sets.
{"title":"DHC: a density-based hierarchical clustering method for time series gene expression data","authors":"D. Jiang, J. Pei, A. Zhang","doi":"10.1109/BIBE.2003.1188978","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188978","url":null,"abstract":"Clustering the time series gene expression data is an important task in bioinformatics research and biomedical applications. Recently, some clustering methods have been adapted or proposed. However, some concerns still remain, such as the robustness of the mining methods, as well as the quality and the interpretability of the mining results. In this paper, we tackle the problem of effectively clustering time series gene expression data by proposing algorithm DHC, a density-based, hierarchical clustering method. We use a density-based approach to identify the clusters such that the clustering results are of high quality and robustness. Moreover, the mining result is in the form of a density tree, which uncovers the embedded clusters in a data set. The inner-structures, the borders and the outliers of the clusters can be further investigated using the attraction tree, which is an intermediate result of the mining. By these two trees, the internal structure of the data set can be visualized effectively. Our empirical evaluation using some real-world data sets show that the method is effective, robust and scalable. It matches the ground truth provided by bioinformatics experts very well in the sample data sets.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"126 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":"114581068","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.2003.1188923
M. Shah, S. Passovets, Dongsup Kim, K. Ellrott, Li Wang, Inna Vokler, P. LoCascio, Dong Xu, Ying Xu
Traditionally, protein 3D structures are solved using experimental techniques, like X-ray crystallography or nuclear magnetic resonance (NMR). While these experimental techniques have been the main workhorse for protein structure studies in the past few decades, it is becoming increasingly apparent that they alone cannot keep up with the production rate of protein sequences. Fortunately, computational techniques for protein structure predictions have matured to such a level that they can complement the existing experimental techniques. In this paper, we present an automated pipeline for protein structure prediction. The centerpiece of the pipeline is a threading-based protein structure prediction system, called PROSPECT, which we have been developing for the past few years. The pipeline consists of seven logical phases, utilizing a dozen tools. The pipeline has been implemented to run in a heterogeneous computational environment as a client/server system with a web interface. A number of genome-scale applications have been carried out on microbial genomes. Here we present one genome-scale application on Caenorhabditis elegans.
{"title":"A computational pipeline for protein structure prediction and analysis at genome scale","authors":"M. Shah, S. Passovets, Dongsup Kim, K. Ellrott, Li Wang, Inna Vokler, P. LoCascio, Dong Xu, Ying Xu","doi":"10.1109/BIBE.2003.1188923","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188923","url":null,"abstract":"Traditionally, protein 3D structures are solved using experimental techniques, like X-ray crystallography or nuclear magnetic resonance (NMR). While these experimental techniques have been the main workhorse for protein structure studies in the past few decades, it is becoming increasingly apparent that they alone cannot keep up with the production rate of protein sequences. Fortunately, computational techniques for protein structure predictions have matured to such a level that they can complement the existing experimental techniques. In this paper, we present an automated pipeline for protein structure prediction. The centerpiece of the pipeline is a threading-based protein structure prediction system, called PROSPECT, which we have been developing for the past few years. The pipeline consists of seven logical phases, utilizing a dozen tools. The pipeline has been implemented to run in a heterogeneous computational environment as a client/server system with a web interface. A number of genome-scale applications have been carried out on microbial genomes. Here we present one genome-scale application on Caenorhabditis elegans.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"98 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":"121572553","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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