Pub Date : 2001-03-04DOI: 10.1109/BIBE.2001.974424
J. Ponomarenko, T. Merkulova, G. Orlova, E. Gorshkova, Oleg N. Fokin, M. Ponomarenko
During the post genome era, single nucleotide polymorphism (SNP) analysis becomes the crossroad of bioinformatics, bioengineering and human health care. We have developed a data mining system, rSNP-Guide, http://wwmgs.bionet.nsc.ru/mgs/systems/rsnp/, devoted to predict the transcription factor (TF) binding sites on DNA, alterations of which are associated with disease. rSNP-Guide formalizes the disease-referred experimental data on the alterations in the DNA binding to unknown TF, estimates the abilities of the DNA with mutations associated with disease to bind to each known TFs examined so that to separate one of them, which TF site is altered by the mutations in the best consistence with that of the unknown TF experimentally associated with diseases. The rSNP-Guide has been control tested on the SNPs with known site-disease relationships. Two TF sites associated with diseases were predicted and confirmed experimentally, namely: GATA site in K-ras gene (lung tumor) and YY1 site in TDO2 gene (mental disorders).
{"title":"Mining genome variation to associate disease with transcription factor binding site alteration","authors":"J. Ponomarenko, T. Merkulova, G. Orlova, E. Gorshkova, Oleg N. Fokin, M. Ponomarenko","doi":"10.1109/BIBE.2001.974424","DOIUrl":"https://doi.org/10.1109/BIBE.2001.974424","url":null,"abstract":"During the post genome era, single nucleotide polymorphism (SNP) analysis becomes the crossroad of bioinformatics, bioengineering and human health care. We have developed a data mining system, rSNP-Guide, http://wwmgs.bionet.nsc.ru/mgs/systems/rsnp/, devoted to predict the transcription factor (TF) binding sites on DNA, alterations of which are associated with disease. rSNP-Guide formalizes the disease-referred experimental data on the alterations in the DNA binding to unknown TF, estimates the abilities of the DNA with mutations associated with disease to bind to each known TFs examined so that to separate one of them, which TF site is altered by the mutations in the best consistence with that of the unknown TF experimentally associated with diseases. The rSNP-Guide has been control tested on the SNPs with known site-disease relationships. Two TF sites associated with diseases were predicted and confirmed experimentally, namely: GATA site in K-ras gene (lung tumor) and YY1 site in TDO2 gene (mental disorders).","PeriodicalId":405124,"journal":{"name":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114497348","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 : 2001-03-04DOI: 10.1109/BIBE.2001.974431
A. Bansal
This paper extends previously described automated techniques by automatically integrating the information about automatically derived co-transcribed gene-groups, functionally similar gene-groups derived using automated pair-wise genome comparisons and automatically derived orthologs (functionally equivalent genes) to derive microbial metabolic pathways. The method integrates automatically derived co-transcribed gene-groups with orthologous and homologous gene-groups (http://www.mcs.kent.edu//spl sim/arvind/orthos.html), the biochemical pathway template available at the KEGG database. (http://www.genome.ad.jp), the enzyme information derived from the SwissProt enzyme database (http://expasy.hcuge.ch/) and Ligand database (http://www.genome.ad.jp). The technique refines existing pathways (based upon network of reactions of enzymes) by associating corresponding non-enzymatic, regulatory, and cotranscribed proteins to enzymes. The technique has been illustrated by deriving a major pathway of M. tuberculosis by comparison with seven microbial genomes including E coli and B. subtilis - two microbes well explored in wet laboratories.
{"title":"Integrating co-regulated gene-groups and pair-wise genome comparisons to automate reconstruction of microbial pathways","authors":"A. Bansal","doi":"10.1109/BIBE.2001.974431","DOIUrl":"https://doi.org/10.1109/BIBE.2001.974431","url":null,"abstract":"This paper extends previously described automated techniques by automatically integrating the information about automatically derived co-transcribed gene-groups, functionally similar gene-groups derived using automated pair-wise genome comparisons and automatically derived orthologs (functionally equivalent genes) to derive microbial metabolic pathways. The method integrates automatically derived co-transcribed gene-groups with orthologous and homologous gene-groups (http://www.mcs.kent.edu//spl sim/arvind/orthos.html), the biochemical pathway template available at the KEGG database. (http://www.genome.ad.jp), the enzyme information derived from the SwissProt enzyme database (http://expasy.hcuge.ch/) and Ligand database (http://www.genome.ad.jp). The technique refines existing pathways (based upon network of reactions of enzymes) by associating corresponding non-enzymatic, regulatory, and cotranscribed proteins to enzymes. The technique has been illustrated by deriving a major pathway of M. tuberculosis by comparison with seven microbial genomes including E coli and B. subtilis - two microbes well explored in wet laboratories.","PeriodicalId":405124,"journal":{"name":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126202223","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 : 2001-03-04DOI: 10.1109/BIBE.2001.974430
N. Tsekos, J. Shudy, E. Yacoub, Panagiotis V. Tsekos, I. Koutlas
The objective of this work was to develop a robotic apparatus for MR-guided biopsy and therapeutic interventions in the breast. This device facilitates (i) conditioning of the breast, by setting the orientation and degree of compression, (ii) definition of the interventional probe trajectory, by setting the height and angulation of a probe guide and (iii) positioning of an interventional probe, by setting the depth of insertion. The apparatus is fitted with appropriate computer-controlled degrees of freedom for optimal approach for delivering and monitoring interventions with MR-guidance, such as diagnostic or therapeutic trans-cannula or subcutaneous minimally invasive procedures. The entire device is constructed of MR compatible material, i.e. non-magnetic and non-conductive, to eliminate artifacts and distortion of the local magnetic field. The apparatus is remotely controlled by means of ultrasonic actuators and a graphics user interface, providing real-time MR-guided planning and monitoring of the operation.
{"title":"Development of a robotic device for MRI-guided interventions in the breast","authors":"N. Tsekos, J. Shudy, E. Yacoub, Panagiotis V. Tsekos, I. Koutlas","doi":"10.1109/BIBE.2001.974430","DOIUrl":"https://doi.org/10.1109/BIBE.2001.974430","url":null,"abstract":"The objective of this work was to develop a robotic apparatus for MR-guided biopsy and therapeutic interventions in the breast. This device facilitates (i) conditioning of the breast, by setting the orientation and degree of compression, (ii) definition of the interventional probe trajectory, by setting the height and angulation of a probe guide and (iii) positioning of an interventional probe, by setting the depth of insertion. The apparatus is fitted with appropriate computer-controlled degrees of freedom for optimal approach for delivering and monitoring interventions with MR-guidance, such as diagnostic or therapeutic trans-cannula or subcutaneous minimally invasive procedures. The entire device is constructed of MR compatible material, i.e. non-magnetic and non-conductive, to eliminate artifacts and distortion of the local magnetic field. The apparatus is remotely controlled by means of ultrasonic actuators and a graphics user interface, providing real-time MR-guided planning and monitoring of the operation.","PeriodicalId":405124,"journal":{"name":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115071136","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 : 2001-03-04DOI: 10.1109/BIBE.2001.974421
M. Wise, A. Osborn
Terminal-restriction fragment length polymorphism (T-RFLP) and length heterogeneity-polymerase chain reaction (LH-PCR) are DNA fingerprinting technologies which use PCR amplification of a gene of interest e.g. small subunit rRNA gene, to study microbial community structure and dynamics. Either one or both of the forward and reverse strand primers used to amplify the gene are fluorescently labelled. The products of restriction endonuclease digestion are electrophoresed with an automated sequencer that detects only the terminal (labelled) restriction fragments (T-RFs). In LH-PCR, products are electrophoresed without digestion, with different fragment lengths being due to inherent variation in the amplified sequence. A novel software system, TRUFFLER, has been developed to mimic this process in silico allowing comparison of experimental data against databases of theoretically determined T-RFs. As a given combination of forward and reverse primers and restriction endonuclease can yield identical T-RFs across a number of species, combinations of different endonucleases (and/or primers) are typically used In addition to fragment length data, data on fluorescence levels is also available. Computationally, this can be viewed as a constraint satisfaction problem which can be solved to allow identification of the dominant members of the microbial community, often down to individual species or at least genus level, and their relative proportions.
{"title":"TRUFFLER: programs to study microbial community composition and flux from fluorescent DNA fingerprinting data","authors":"M. Wise, A. Osborn","doi":"10.1109/BIBE.2001.974421","DOIUrl":"https://doi.org/10.1109/BIBE.2001.974421","url":null,"abstract":"Terminal-restriction fragment length polymorphism (T-RFLP) and length heterogeneity-polymerase chain reaction (LH-PCR) are DNA fingerprinting technologies which use PCR amplification of a gene of interest e.g. small subunit rRNA gene, to study microbial community structure and dynamics. Either one or both of the forward and reverse strand primers used to amplify the gene are fluorescently labelled. The products of restriction endonuclease digestion are electrophoresed with an automated sequencer that detects only the terminal (labelled) restriction fragments (T-RFs). In LH-PCR, products are electrophoresed without digestion, with different fragment lengths being due to inherent variation in the amplified sequence. A novel software system, TRUFFLER, has been developed to mimic this process in silico allowing comparison of experimental data against databases of theoretically determined T-RFs. As a given combination of forward and reverse primers and restriction endonuclease can yield identical T-RFs across a number of species, combinations of different endonucleases (and/or primers) are typically used In addition to fragment length data, data on fluorescence levels is also available. Computationally, this can be viewed as a constraint satisfaction problem which can be solved to allow identification of the dominant members of the microbial community, often down to individual species or at least genus level, and their relative proportions.","PeriodicalId":405124,"journal":{"name":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132047272","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 : 2001-03-04DOI: 10.1109/BIBE.2001.974416
Hadon Nash, Douglas Blair, J. Grefenstette
The first step in homology analysis is usually the comparison of sequences by similarity search. The explosive growth of genomic databases makes it increasingly important to develop more rapid approaches to the comparison of large sequence databases while using the most sensitive methods available. This paper explores the consequences of this trade-off, comparing the results produced by BLAST and Smith-Waterman on genoinic- scale sequence searches. Stich comparisons are now possible thanks to the development of novel distributed computing platforms. This study uses the Parabon Frontier/sup TM/ Internet computing platform, which enables the effective use of the vast supply of idle computer cycles on the Internet for high-performance computing. We have ported both Smith-Waterman and BLAST to the Frontier platform, enabling the efficient use of these algorithms on large sequence databases. In addition, we present a novel visualization tool along with quantitative metrics for comparing the results of alternative sequence alignment algorithms. Our results compare the sensitivity of Smith-Waterman and BLAST for identifying homologies on proteome databases.
{"title":"Comparing algorithms for large-scale sequence analysis","authors":"Hadon Nash, Douglas Blair, J. Grefenstette","doi":"10.1109/BIBE.2001.974416","DOIUrl":"https://doi.org/10.1109/BIBE.2001.974416","url":null,"abstract":"The first step in homology analysis is usually the comparison of sequences by similarity search. The explosive growth of genomic databases makes it increasingly important to develop more rapid approaches to the comparison of large sequence databases while using the most sensitive methods available. This paper explores the consequences of this trade-off, comparing the results produced by BLAST and Smith-Waterman on genoinic- scale sequence searches. Stich comparisons are now possible thanks to the development of novel distributed computing platforms. This study uses the Parabon Frontier/sup TM/ Internet computing platform, which enables the effective use of the vast supply of idle computer cycles on the Internet for high-performance computing. We have ported both Smith-Waterman and BLAST to the Frontier platform, enabling the efficient use of these algorithms on large sequence databases. In addition, we present a novel visualization tool along with quantitative metrics for comparing the results of alternative sequence alignment algorithms. Our results compare the sensitivity of Smith-Waterman and BLAST for identifying homologies on proteome databases.","PeriodicalId":405124,"journal":{"name":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114273367","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 : 2001-03-04DOI: 10.1109/BIBE.2001.974420
R. Murphy, M. Velliste, Jie Yao, G. Porreca
There is extensive interest in automating the collection, organization and analysis of biological data. Data in the form of images present special challenges for such efforts. Since fluorescence microscope images are a primary source of information about the location of proteins within cells, we have set as a long-term goal the building of a knowledge base system that can interpret such images in online journals. To this end, we first developed a robot that searches online journals and finds fluorescence microscope images of individual cells. We then characterized the applicability of pattern classification methods we have previously used on images obtained under controlled conditions to images from different sources and to images subjected to manipulations commonly performed during publication. The results indicate the feasibility of developing search engines to find fluorescence microscope images depicting particular subcellular patterns.
{"title":"Searching online journals for fluorescence microscope images depicting protein subcellular location patterns","authors":"R. Murphy, M. Velliste, Jie Yao, G. Porreca","doi":"10.1109/BIBE.2001.974420","DOIUrl":"https://doi.org/10.1109/BIBE.2001.974420","url":null,"abstract":"There is extensive interest in automating the collection, organization and analysis of biological data. Data in the form of images present special challenges for such efforts. Since fluorescence microscope images are a primary source of information about the location of proteins within cells, we have set as a long-term goal the building of a knowledge base system that can interpret such images in online journals. To this end, we first developed a robot that searches online journals and finds fluorescence microscope images of individual cells. We then characterized the applicability of pattern classification methods we have previously used on images obtained under controlled conditions to images from different sources and to images subjected to manipulations commonly performed during publication. The results indicate the feasibility of developing search engines to find fluorescence microscope images depicting particular subcellular patterns.","PeriodicalId":405124,"journal":{"name":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114826731","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 : 2001-03-04DOI: 10.1109/BIBE.2001.974411
Aaron Davidson
Sequence alignment is an important operation in computational biology. Both dynamic programming and A* heuristic search algorithms for optimal sequence alignment are discussed and evaluated Presented here are two new algorithms for optimal pairwise sequence alignment which outperform traditional methods on very large problem instances (hundreds of thousands of characters, for example). The technique combines the benefits of dynamic programming and A* heuristic search, with a minimal amount of additional overhead. The dynamic programming matrix is traversed along antidiagonals, bounding the computation to exclude portions of the matrix that cannot contain optimal paths. An admissible heuristic assists in pruning away unnecessary areas of the matrix, while preserving optimal solutions for any given scoring function. Since memory requirements are a major concern for large sequence alignment problems, it is shown how the standard algorithm (requiring quadratic space) can be reformulated as a divide and conquer algorithm (requiring only linear space, at the cost of some recomputuation).
{"title":"A fast pruning algorithm for optimal sequence alignment","authors":"Aaron Davidson","doi":"10.1109/BIBE.2001.974411","DOIUrl":"https://doi.org/10.1109/BIBE.2001.974411","url":null,"abstract":"Sequence alignment is an important operation in computational biology. Both dynamic programming and A* heuristic search algorithms for optimal sequence alignment are discussed and evaluated Presented here are two new algorithms for optimal pairwise sequence alignment which outperform traditional methods on very large problem instances (hundreds of thousands of characters, for example). The technique combines the benefits of dynamic programming and A* heuristic search, with a minimal amount of additional overhead. The dynamic programming matrix is traversed along antidiagonals, bounding the computation to exclude portions of the matrix that cannot contain optimal paths. An admissible heuristic assists in pruning away unnecessary areas of the matrix, while preserving optimal solutions for any given scoring function. Since memory requirements are a major concern for large sequence alignment problems, it is shown how the standard algorithm (requiring quadratic space) can be reformulated as a divide and conquer algorithm (requiring only linear space, at the cost of some recomputuation).","PeriodicalId":405124,"journal":{"name":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115258874","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 : 2001-03-04DOI: 10.1109/BIBE.2001.974407
M. Cornell, N. Paton, Shengli Wu, C. Goble, Crispin J. Miller, Paul Kirby, K. Eilbeck, A. Brass, A. Hayes, S. Oliver
Effective analysis of genome sequences and associated functional data requires access to many different kinds of biological information. For example, when analysing gene expression data, it may be useful to have access to the sequences upstream of the genes, or to the cellular location of their protein products. Such information is currently stored in different formats at different sites in a way that does not readily allow integrated analyses. The Genome Information Management System (GIMS) is an object database that integrates genome sequence data with functional data on the transcriptome and on protein-protein interactions in a single data warehouse. We have used GIMS to store the Saccharomyces cerevisiae (yeast) genome and to demonstrate how the integrated storage of diverse kinds of genomic data can be beneficial for analysing data using context-rich queries and analyses. GIMS allows data to be stored in a way that reflects the underlying mechanisms in the organism, and permits complex questions to be asked of the data. This paper provides an overview of the GIMS system and describes some analyses that illustrate its use for analysing functional data sets for S. cerevisiae.
{"title":"GIMS-a data warehouse for storage and analysis of genome sequence and functional data","authors":"M. Cornell, N. Paton, Shengli Wu, C. Goble, Crispin J. Miller, Paul Kirby, K. Eilbeck, A. Brass, A. Hayes, S. Oliver","doi":"10.1109/BIBE.2001.974407","DOIUrl":"https://doi.org/10.1109/BIBE.2001.974407","url":null,"abstract":"Effective analysis of genome sequences and associated functional data requires access to many different kinds of biological information. For example, when analysing gene expression data, it may be useful to have access to the sequences upstream of the genes, or to the cellular location of their protein products. Such information is currently stored in different formats at different sites in a way that does not readily allow integrated analyses. The Genome Information Management System (GIMS) is an object database that integrates genome sequence data with functional data on the transcriptome and on protein-protein interactions in a single data warehouse. We have used GIMS to store the Saccharomyces cerevisiae (yeast) genome and to demonstrate how the integrated storage of diverse kinds of genomic data can be beneficial for analysing data using context-rich queries and analyses. GIMS allows data to be stored in a way that reflects the underlying mechanisms in the organism, and permits complex questions to be asked of the data. This paper provides an overview of the GIMS system and describes some analyses that illustrate its use for analysing functional data sets for S. cerevisiae.","PeriodicalId":405124,"journal":{"name":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129807557","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 : 2001-03-04DOI: 10.1109/BIBE.2001.974410
Chun Tang, Li Zhang, A. Zhang, M. Ramanathan
DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Most research is focusing on interpretation of the meaning of the data. However, the majority of methods are supervised, with less attention having been paid to unsupervised approaches which are important when domain knowledge is incomplete or hard to obtain. In this paper we present a new framework for unsupervised analysis of gene expression data which applies an interrelated two-way clustering approach to the gene expression matrices. The goal of clustering is to find important gene patterns and perform cluster discovery on samples. The advantage of this approach is that we can dynamically use the relationships between the groups of genes and samples while iteratively clustering through both gene-dimension and sample-dimension. We illustrate the method on gene expression data from a study of multiple sclerosis patients. The experiments demonstrate the effectiveness of this approach.
{"title":"Interrelated two-way clustering: an unsupervised approach for gene expression data analysis","authors":"Chun Tang, Li Zhang, A. Zhang, M. Ramanathan","doi":"10.1109/BIBE.2001.974410","DOIUrl":"https://doi.org/10.1109/BIBE.2001.974410","url":null,"abstract":"DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Most research is focusing on interpretation of the meaning of the data. However, the majority of methods are supervised, with less attention having been paid to unsupervised approaches which are important when domain knowledge is incomplete or hard to obtain. In this paper we present a new framework for unsupervised analysis of gene expression data which applies an interrelated two-way clustering approach to the gene expression matrices. The goal of clustering is to find important gene patterns and perform cluster discovery on samples. The advantage of this approach is that we can dynamically use the relationships between the groups of genes and samples while iteratively clustering through both gene-dimension and sample-dimension. We illustrate the method on gene expression data from a study of multiple sclerosis patients. The experiments demonstrate the effectiveness of this approach.","PeriodicalId":405124,"journal":{"name":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126636944","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 : 2001-03-04DOI: 10.1109/BIBE.2001.974427
P. Mitra, G. Wiederhold
Resolving heterogeneity among the various biological information systems is a crucial problem if we wish to gain value from the many distributed resources available to us. For example, information from multiple protein databases (e.g., Swiss-Prot and PDB) might need to be composed to answer queries posed by end-users. Problems of heterogeneity in hardware, operating systems, interfaces and data structures have been widely addressed, but issues of diverse semantics have been handled mainly in an ad-hoc fashion. This paper highlights the ONION (ONtology compositION) system that enables semantic interoperation among various information sources by articulating the ontologies associated with them. An articulation focuses on the semantically relevant intersection of information resources. Although the generation of articulations (semantic correspondences between the ontologies) cannot be fully automated, we take a semi-automatic approach. ONION uses heuristic algorithms for the automatic generation of suggested articulations. This paper outlines an algebra for ontology composition based on their articulations. We show the properties of the algebraic operators and how they depend upon the articulation functions that generate the articulations. Query optimization is enabled based on the properties of the algebraic operators.
{"title":"An algebra for semantic interoperability of information sources","authors":"P. Mitra, G. Wiederhold","doi":"10.1109/BIBE.2001.974427","DOIUrl":"https://doi.org/10.1109/BIBE.2001.974427","url":null,"abstract":"Resolving heterogeneity among the various biological information systems is a crucial problem if we wish to gain value from the many distributed resources available to us. For example, information from multiple protein databases (e.g., Swiss-Prot and PDB) might need to be composed to answer queries posed by end-users. Problems of heterogeneity in hardware, operating systems, interfaces and data structures have been widely addressed, but issues of diverse semantics have been handled mainly in an ad-hoc fashion. This paper highlights the ONION (ONtology compositION) system that enables semantic interoperation among various information sources by articulating the ontologies associated with them. An articulation focuses on the semantically relevant intersection of information resources. Although the generation of articulations (semantic correspondences between the ontologies) cannot be fully automated, we take a semi-automatic approach. ONION uses heuristic algorithms for the automatic generation of suggested articulations. This paper outlines an algebra for ontology composition based on their articulations. We show the properties of the algebraic operators and how they depend upon the articulation functions that generate the articulations. Query optimization is enabled based on the properties of the algebraic operators.","PeriodicalId":405124,"journal":{"name":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","volume":"98 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133054682","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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