Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188970
Feng Luo, Kun Tang, L. Khan
Rapid development of biological technologies generates a huge amount of data, which provides a processing and global view of the gene expression levels across different conditions and over multiple stages. Analyzation and interpretation of these massive data is a challenging task. One of the most important steps is to extract useful and rational fundamental patterns of gene expression inherent in these huge data. Clustering technology is one of the useful and popular methods to obtain these patterns. In this paper we propose a new hierarchical clustering algorithm to obtain gene expression patterns. This algorithm constructs a hierarchy from top to bottom based on a self-organizing tree. It dynamically finds the number of clusters at each level. We compare our algorithm with the traditional hierarchical agglomerative clustering (HAC) algorithm. We apply our algorithm to an existing 112 rat central nervous system gene expression data. We observe that our algorithm extracts patterns with different levels of abstraction. Furthermore, our approach is useful on recognizing features in complex gene expression data.
{"title":"Hierarchical clustering of gene expression data","authors":"Feng Luo, Kun Tang, L. Khan","doi":"10.1109/BIBE.2003.1188970","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188970","url":null,"abstract":"Rapid development of biological technologies generates a huge amount of data, which provides a processing and global view of the gene expression levels across different conditions and over multiple stages. Analyzation and interpretation of these massive data is a challenging task. One of the most important steps is to extract useful and rational fundamental patterns of gene expression inherent in these huge data. Clustering technology is one of the useful and popular methods to obtain these patterns. In this paper we propose a new hierarchical clustering algorithm to obtain gene expression patterns. This algorithm constructs a hierarchy from top to bottom based on a self-organizing tree. It dynamically finds the number of clusters at each level. We compare our algorithm with the traditional hierarchical agglomerative clustering (HAC) algorithm. We apply our algorithm to an existing 112 rat central nervous system gene expression data. We observe that our algorithm extracts patterns with different levels of abstraction. Furthermore, our approach is useful on recognizing features in complex gene expression 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":"131148464","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.2003.1188935
Francis K. H. Quek, Vishwas Kulkarni, C. Kirbas
Curvature-based surface features are well suited for use in multimodal medical image registration. The accuracy of such feature-based registration techniques is dependent upon the reliability of the feature computation. The computation of curvature features requires second derivative information that is best obtained from a parametric surface representation. We present a method of explicitly parameterizing surfaces from volumetric data. Surfaces are extracted, without a global thresholding, using active contour models. A Mong basis for each surface patch is estimated and used to transform the patch into local, or parametric, coordinates. Surface patches are fit to first a bicubic polynomial and second to a Bezier polynomial. The bicubic polynomial is fit in local coordinates using least squares solved by singular value decomposition. Bezier polynomial is fit using de Casteljau algorithm. We tested our method by reconstructing surfaces from the surface model and analytically computing Gaussian and mean curvatures. The model was tested on analytical and medical data and the results of both methods are compared.
{"title":"Comparison of bicubic and Bezier polynomials for surface parameterization in volumetric images","authors":"Francis K. H. Quek, Vishwas Kulkarni, C. Kirbas","doi":"10.1109/BIBE.2003.1188935","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188935","url":null,"abstract":"Curvature-based surface features are well suited for use in multimodal medical image registration. The accuracy of such feature-based registration techniques is dependent upon the reliability of the feature computation. The computation of curvature features requires second derivative information that is best obtained from a parametric surface representation. We present a method of explicitly parameterizing surfaces from volumetric data. Surfaces are extracted, without a global thresholding, using active contour models. A Mong basis for each surface patch is estimated and used to transform the patch into local, or parametric, coordinates. Surface patches are fit to first a bicubic polynomial and second to a Bezier polynomial. The bicubic polynomial is fit in local coordinates using least squares solved by singular value decomposition. Bezier polynomial is fit using de Casteljau algorithm. We tested our method by reconstructing surfaces from the surface model and analytically computing Gaussian and mean curvatures. The model was tested on analytical and medical data and the results of both methods are compared.","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":"115731245","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.2003.1188937
D. Gui, N. Tsekos
A method is proposed for fast magnetic resonance imaging (MRI) acquisition of targeted specific structures. The method is based on the correlation between the inverse space (k-space) and the real space geometry of the imaged structure. Theoretical and simulation studies were performed with segments of straight and curved vessels. In cases when we are interested for only a segment of a vessel, as example for interventions, these studies show that as small as 1/8 of the whole k-space data is sufficient to reconstruct the interested vessel without compromise in the image quality.
{"title":"Structure-targeting fast magnetic resonance imaging angiography with partial collection of the inverse space (k-space) based on the orientation of the vessel in real space","authors":"D. Gui, N. Tsekos","doi":"10.1109/BIBE.2003.1188937","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188937","url":null,"abstract":"A method is proposed for fast magnetic resonance imaging (MRI) acquisition of targeted specific structures. The method is based on the correlation between the inverse space (k-space) and the real space geometry of the imaged structure. Theoretical and simulation studies were performed with segments of straight and curved vessels. In cases when we are interested for only a segment of a vessel, as example for interventions, these studies show that as small as 1/8 of the whole k-space data is sufficient to reconstruct the interested vessel without compromise in the image quality.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"120 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":"123229683","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.2003.1188941
S. Aghili, D. Agrawal, A. E. Abbadi
The problem of proximity search in biological databases is addressed. We study vector transformations and conduct the application of DFT (Discrete Fourier Transformation) and DWT (Discrete Wavelet Transformation, Haar) dimensionality reduction techniques for DNA sequence proximity search to reduce the search time of range queries. Our empirical results on a number of Prokaryote and Eukaryote DNA contig databases demonstrate up to 50-fold filtration ratio of the search space, up to 13 times faster filtration. The proposed transformation techniques may easily be integrated as a preprocessing phase on top of the current existing similarity search heuristics such as BLAST, PattenHunter, FastA, QUASAR and to efficiently prune non-relevant sequences. We study the precision of applying dimensionality reduction techniques for faster and more efficient range query searches, and discuss the imposed trade-offs.
{"title":"Filtration of string proximity search via transformation","authors":"S. Aghili, D. Agrawal, A. E. Abbadi","doi":"10.1109/BIBE.2003.1188941","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188941","url":null,"abstract":"The problem of proximity search in biological databases is addressed. We study vector transformations and conduct the application of DFT (Discrete Fourier Transformation) and DWT (Discrete Wavelet Transformation, Haar) dimensionality reduction techniques for DNA sequence proximity search to reduce the search time of range queries. Our empirical results on a number of Prokaryote and Eukaryote DNA contig databases demonstrate up to 50-fold filtration ratio of the search space, up to 13 times faster filtration. The proposed transformation techniques may easily be integrated as a preprocessing phase on top of the current existing similarity search heuristics such as BLAST, PattenHunter, FastA, QUASAR and to efficiently prune non-relevant sequences. We study the precision of applying dimensionality reduction techniques for faster and more efficient range query searches, and discuss the imposed trade-offs.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"33 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":"121356628","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.2003.1188934
Mike Hu, P. Kearney, J. Badger
As the amount of sequencing efforts and genomic data volume continue to increase at an accelerated rate, phylogenetic analysis provides an evolutionary context for understanding and interpreting this growing set of complex data. We introduce a novel quartet based method for inferring molecular based phylogeny called hypercleaning* (HC*). The HC* method is based on the hypercleaning (HC) technique, which possesses an interesting property of recovering edges (of a phylogenetic tree) that are best supported by the witness quartet set. HC* extends HC in two regards: i) whereas HC constrains the input quartet set to be unweighted (binary valued), HC* allows any positive valued quartet scores, enabling more informative quartets to be defined. ii) HC* employs a novel collapsing technique which significantly speeds up the inference stage, making it empirically on par with quartet puzzling in terms of speed, while still guaranteeing optimal edge recovery as in HC. This paper is primarily aimed at presenting the algorithmic construction of HC*. We also report some preliminary studies on an implementation of HC* as a potentially powerful approximation scheme for maximum likelihood based inference.
{"title":"A collapsing method for the efficient recovery of optimal edges in phylogenetic trees","authors":"Mike Hu, P. Kearney, J. Badger","doi":"10.1109/BIBE.2003.1188934","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188934","url":null,"abstract":"As the amount of sequencing efforts and genomic data volume continue to increase at an accelerated rate, phylogenetic analysis provides an evolutionary context for understanding and interpreting this growing set of complex data. We introduce a novel quartet based method for inferring molecular based phylogeny called hypercleaning* (HC*). The HC* method is based on the hypercleaning (HC) technique, which possesses an interesting property of recovering edges (of a phylogenetic tree) that are best supported by the witness quartet set. HC* extends HC in two regards: i) whereas HC constrains the input quartet set to be unweighted (binary valued), HC* allows any positive valued quartet scores, enabling more informative quartets to be defined. ii) HC* employs a novel collapsing technique which significantly speeds up the inference stage, making it empirically on par with quartet puzzling in terms of speed, while still guaranteeing optimal edge recovery as in HC. This paper is primarily aimed at presenting the algorithmic construction of HC*. We also report some preliminary studies on an implementation of HC* as a potentially powerful approximation scheme for maximum likelihood based inference.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"61 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":"126206594","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 : 2002-01-28DOI: 10.1109/BIBE.2003.1188961
Ling Liu, David J. Buttler, T. Critchlow, Wei Han, H. Paques, C. Pu, D. Rocco
Modern Bioinformatics data sources are widely used by molecular biologists for homology searching and new drug discovery. User-friendly and yet responsive access is one of the most desirable properties for integrated access to the rapidly growing, heterogeneous, and distributed collection of data sources. The increasing volume and diversity of digital information related to bioinformatics (such as genomes, protein sequences, protein structures, etc.) have led to a growing problem that conventional data management systems do not have, namely finding which information sources out of many candidate choices are the most relevant and most accessible to answer a given user query. We refer to this problem as the query routing problem. In this paper we introduce the notation and issues of query routing, and present a practical solution for designing a scalable query routing system based on multi-level progressive pruning strategies. The key idea is to create and maintain source capability profiles independently, and to provide algorithms that can dynamically discover relevant information sources for a given query through the smart use of source profiles. Compared to the keyword-based indexing techniques adopted in most of the search engines and software, our approach offers fine-granularity of interest matching, thus it is more powerful and effective for handling queries with complex conditions.
{"title":"BioSeek: exploiting source-capability information for integrated access to multiple bioinformatics data sources","authors":"Ling Liu, David J. Buttler, T. Critchlow, Wei Han, H. Paques, C. Pu, D. Rocco","doi":"10.1109/BIBE.2003.1188961","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188961","url":null,"abstract":"Modern Bioinformatics data sources are widely used by molecular biologists for homology searching and new drug discovery. User-friendly and yet responsive access is one of the most desirable properties for integrated access to the rapidly growing, heterogeneous, and distributed collection of data sources. The increasing volume and diversity of digital information related to bioinformatics (such as genomes, protein sequences, protein structures, etc.) have led to a growing problem that conventional data management systems do not have, namely finding which information sources out of many candidate choices are the most relevant and most accessible to answer a given user query. We refer to this problem as the query routing problem. In this paper we introduce the notation and issues of query routing, and present a practical solution for designing a scalable query routing system based on multi-level progressive pruning strategies. The key idea is to create and maintain source capability profiles independently, and to provide algorithms that can dynamically discover relevant information sources for a given query through the smart use of source profiles. Compared to the keyword-based indexing techniques adopted in most of the search engines and software, our approach offers fine-granularity of interest matching, thus it is more powerful and effective for handling queries with complex conditions.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128561803","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 : 1900-01-01DOI: 10.1109/BIBE.2003.1188956
T.A. El Doker
A geometric deformable model is presented for iterative segmentation and recognition of boundaries belonging to anatomic structures in medical imagery. The model utilizes a conventional edge detection algorithm for the extraction of potential boundaries. B-spline descriptors for the boundaries are then calculated. Next, geometric quasi-invariants of the control point sets, describing the B-splines are used to match potential boundaries with that of a prototype template stored in memory. Such a template is part of a novel second-order B-spline prototype templates library where the boundaries of anatomic structures are stored as sets of control points instead of storing the images themselves. The utilization of a control point set for segmentation and recognition reduces computational complexity and improves the accuracy and efficiency of the process. Once a match has been found, segmentation is done again with the parameters of the matching template. Utilizing these parameters minimizes noise and other unwanted features. This model does not suffer from many of the drawbacks associated with other deformable templates and snake models that are currently used, such as computational complexity, user interaction, sensitivity to initial conditions and others. Furthermore, unlike most deformable model templates, this algorithm is not limited to a few images and does not require huge storage space since control point sets are used to describe templates in the library. Experiments performed on medical images confirm the efficiency and robustness of this algorithm.
{"title":"Unsupervised iterative segmentation and recognition of anatomic structures in medical imagery using second-order B-spline descriptors and geometric quasi-invariants","authors":"T.A. El Doker","doi":"10.1109/BIBE.2003.1188956","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188956","url":null,"abstract":"A geometric deformable model is presented for iterative segmentation and recognition of boundaries belonging to anatomic structures in medical imagery. The model utilizes a conventional edge detection algorithm for the extraction of potential boundaries. B-spline descriptors for the boundaries are then calculated. Next, geometric quasi-invariants of the control point sets, describing the B-splines are used to match potential boundaries with that of a prototype template stored in memory. Such a template is part of a novel second-order B-spline prototype templates library where the boundaries of anatomic structures are stored as sets of control points instead of storing the images themselves. The utilization of a control point set for segmentation and recognition reduces computational complexity and improves the accuracy and efficiency of the process. Once a match has been found, segmentation is done again with the parameters of the matching template. Utilizing these parameters minimizes noise and other unwanted features. This model does not suffer from many of the drawbacks associated with other deformable templates and snake models that are currently used, such as computational complexity, user interaction, sensitivity to initial conditions and others. Furthermore, unlike most deformable model templates, this algorithm is not limited to a few images and does not require huge storage space since control point sets are used to describe templates in the library. Experiments performed on medical images confirm the efficiency and robustness of this algorithm.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117255202","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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