Tissue imaging mass spectrometry (TIMS) is a data-intensive technique for spatial biochemical analysis. TIMS contributes both molecular and spatial information to tissue analysis. We propose and evaluate a similarity measure, based on the hyper geometric distribution, for comparing m/z images from TIMS datasets, with the goal of identifying m/z values with similar spatial distributions. We compare the formulation and properties of the proposed method with those of other similarity measures, and examine the performance of each measure on synthetic and biological data. This study demonstrates that the proposed hyper geometric similarity measure is effective in identifying similar m/z images, and may be a useful addition to current methods in TIMS data analysis.
{"title":"Hypergeometric Similarity Measure for Spatial Analysis in Tissue Imaging Mass Spectrometry","authors":"C. Kaddi, R. M. Parry, May D. Wang","doi":"10.1109/BIBM.2011.113","DOIUrl":"https://doi.org/10.1109/BIBM.2011.113","url":null,"abstract":"Tissue imaging mass spectrometry (TIMS) is a data-intensive technique for spatial biochemical analysis. TIMS contributes both molecular and spatial information to tissue analysis. We propose and evaluate a similarity measure, based on the hyper geometric distribution, for comparing m/z images from TIMS datasets, with the goal of identifying m/z values with similar spatial distributions. We compare the formulation and properties of the proposed method with those of other similarity measures, and examine the performance of each measure on synthetic and biological data. This study demonstrates that the proposed hyper geometric similarity measure is effective in identifying similar m/z images, and may be a useful addition to current methods in TIMS data analysis.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"14 1","pages":"604-607"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87511770","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 : 2011-11-12DOI: 10.1109/BIBMW.2011.6112441
Sérgio Lifschitz, Luciana S. A. Gomes, S. Rehen
Scientific Workflow Management Systems (SWfMS) are being widely used to represent and execute scientific experiments. One particular SWfMS feature that has received much attention by the scientific community is the automatic capture of provenance data. This allows users to track information about which resources and parameters were used to obtain such results, but also other important information to validate and publish an experiment. In the present work, we propose an approach for modeling and storing data that is consumed and produced by workflows executed by SWfMS. This approach has two main objectives: it aims (i) to support the strict reproducibility of an experiment and (ii) to allow the reuse of produced artifacts by keeping information about its origin.
{"title":"Dealing with reusability and reproducibility for scientific workflows","authors":"Sérgio Lifschitz, Luciana S. A. Gomes, S. Rehen","doi":"10.1109/BIBMW.2011.6112441","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112441","url":null,"abstract":"Scientific Workflow Management Systems (SWfMS) are being widely used to represent and execute scientific experiments. One particular SWfMS feature that has received much attention by the scientific community is the automatic capture of provenance data. This allows users to track information about which resources and parameters were used to obtain such results, but also other important information to validate and publish an experiment. In the present work, we propose an approach for modeling and storing data that is consumed and produced by workflows executed by SWfMS. This approach has two main objectives: it aims (i) to support the strict reproducibility of an experiment and (ii) to allow the reuse of produced artifacts by keeping information about its origin.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"31 1","pages":"625-632"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87819602","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 : 2011-11-12DOI: 10.1109/BIBMW.2011.6112473
Wen-Ran Zhang
The concept of YinYang bipolar atom is introduced as a mathematical model for quantum cellular automation. It is shown that the new model provides a springboard to an equilibrium-based interpretation of particle-wave complementarity and a potential unification of matter, antimatter, relativity and quantum mechanics. The impact of this work on the unification of TCM with Western medicine is discussed.
{"title":"YinYang bipolar atom and quantum cellular automation — A unification","authors":"Wen-Ran Zhang","doi":"10.1109/BIBMW.2011.6112473","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112473","url":null,"abstract":"The concept of YinYang bipolar atom is introduced as a mathematical model for quantum cellular automation. It is shown that the new model provides a springboard to an equilibrium-based interpretation of particle-wave complementarity and a potential unification of matter, antimatter, relativity and quantum mechanics. The impact of this work on the unification of TCM with Western medicine is discussed.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"78 1","pages":"791-797"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88370699","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}
Mass spectrometry (MS) has been used to generate protein profiles from human serum, and proteomic data obtained from MS have attracted great interest for the detection of cancer. Because MALDI-TOF MS provides high-resolution measurements, the biomarker identification has been limited by the unbalance problem between high-dimensional attributes and small sample-size. To deal with the multi-class problem in cancer prediction and biomarker identification, we propose a fast and robust multi-class cancer classification framework. A novel MS biomarker selection algorithm is provided by utilizing over sampled wavelet transform to extract wavelet coefficients and statistical testing to select features. The multi-class Gentle AdaBoost is used as a classifier due to its efficient classification procedure. Several experiments are deployed on real MALDI-TOF MS data in order to prove the superiority of proposed method compared to previous algorithms. The experimental results show that our proposed framework is an effective tool for analyzing MS data in cancer detection.
{"title":"A Novel Algorithm for Multi-class Cancer Diagnosis on MALDI-TOF Mass Spectra","authors":"Phuong Pham, Li Yu, Minh Nguyen","doi":"10.1109/BIBM.2011.50","DOIUrl":"https://doi.org/10.1109/BIBM.2011.50","url":null,"abstract":"Mass spectrometry (MS) has been used to generate protein profiles from human serum, and proteomic data obtained from MS have attracted great interest for the detection of cancer. Because MALDI-TOF MS provides high-resolution measurements, the biomarker identification has been limited by the unbalance problem between high-dimensional attributes and small sample-size. To deal with the multi-class problem in cancer prediction and biomarker identification, we propose a fast and robust multi-class cancer classification framework. A novel MS biomarker selection algorithm is provided by utilizing over sampled wavelet transform to extract wavelet coefficients and statistical testing to select features. The multi-class Gentle AdaBoost is used as a classifier due to its efficient classification procedure. Several experiments are deployed on real MALDI-TOF MS data in order to prove the superiority of proposed method compared to previous algorithms. The experimental results show that our proposed framework is an effective tool for analyzing MS data in cancer detection.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"7 1","pages":"398-401"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86106229","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}
Among the biological macromolecules, proteins have attracted special attention from the scientific community due to their rich functional roles. The ability to visualize and manipulate macromolecular structures on graphical display devices has facilitated the identification and analysis of these macromolecules. Structural analyses of the proteins often provide important insights into their biochemical functions. However, such analysis is often limited by the representation of protein structures and the corresponding computational resource requirements. In this study, we focus on the molecular surface of the proteins and investigate computationally and visually effective representations to serve a number of visualization and analysis purposes. Specifically, we "unfold" the protein surface onto a planar space, while preserving the local surface features as much as possible. In contrast to classical cartographic projections, our approach is able to preserve local shape features. Several biochemical properties associated with each surface point are mapped to generate a two dimensional map of these features. The 3D-2D mapping of the surface vertices has also been utilized to texture-map an arbitrary image back onto the protein structure to facilitate the visualization of the 3D structure.
{"title":"Protein Structure Visualization by Dimension Reduction and Texture Mapping","authors":"Heng Yang, R. Qureshi, A. Sacan","doi":"10.1109/BIBM.2011.29","DOIUrl":"https://doi.org/10.1109/BIBM.2011.29","url":null,"abstract":"Among the biological macromolecules, proteins have attracted special attention from the scientific community due to their rich functional roles. The ability to visualize and manipulate macromolecular structures on graphical display devices has facilitated the identification and analysis of these macromolecules. Structural analyses of the proteins often provide important insights into their biochemical functions. However, such analysis is often limited by the representation of protein structures and the corresponding computational resource requirements. In this study, we focus on the molecular surface of the proteins and investigate computationally and visually effective representations to serve a number of visualization and analysis purposes. Specifically, we \"unfold\" the protein surface onto a planar space, while preserving the local surface features as much as possible. In contrast to classical cartographic projections, our approach is able to preserve local shape features. Several biochemical properties associated with each surface point are mapped to generate a two dimensional map of these features. The 3D-2D mapping of the surface vertices has also been utilized to texture-map an arbitrary image back onto the protein structure to facilitate the visualization of the 3D structure.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"49 1","pages":"437-442"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82644462","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 : 2011-11-12DOI: 10.1109/BIBMW.2011.6112395
Sajal Dash, J. Snoeyink
Although hydrogen bonds are known to form cooperative networks, most protein structure prediction methods still model individual hydrogen bonds independently for computational efficiency. We are developing ways to identify and score networks of bonds, but need to determine the energies from such networks. In this paper we perform quantum calculations to compare energy profiles of individual hydrogen bonds to those of the simplest dependent interaction, bifurcated hydrogen bonds. When there are two lone pairs available for an acceptor to bond with two donors, then there is very little difference between the energies of two independent bonds and a bifurcated bond, but for one donor to bond to two acceptors is much harder. These results suggest that lone pair positions may be a better basis for hydrogen bond parameterization than atom positions.
{"title":"On the energy of bifurcated hydrogen bonds for protein structure prediction","authors":"Sajal Dash, J. Snoeyink","doi":"10.1109/BIBMW.2011.6112395","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112395","url":null,"abstract":"Although hydrogen bonds are known to form cooperative networks, most protein structure prediction methods still model individual hydrogen bonds independently for computational efficiency. We are developing ways to identify and score networks of bonds, but need to determine the energies from such networks. In this paper we perform quantum calculations to compare energy profiles of individual hydrogen bonds to those of the simplest dependent interaction, bifurcated hydrogen bonds. When there are two lone pairs available for an acceptor to bond with two donors, then there is very little difference between the energies of two independent bonds and a bifurcated bond, but for one donor to bond to two acceptors is much harder. These results suggest that lone pair positions may be a better basis for hydrogen bond parameterization than atom positions.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"10 1","pages":"334-337"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90182466","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}
Databases of medical records contain a wealth of information critical to many areas of research including drug safety, health outcomes, clinical epidemiology and translational medicine. Through commercially available databases, researchers can gain a better understanding of the impact of exposure to drugs and medical devices, identify populations at risk for adverse effects, estimate the prevalence and natural history of medical conditions, and assess drug utilization across different demographic groups. However, the daunting size and complexity of these databases as well as lack of convenient tools to mine them have made this information largely inaccessible to all but a few experts with advanced data management and statistical programming skills. Using a combination of a relational data management strategy and a graphical user front-end, we have developed an approach that allows any medical researcher to perform a number of common searches and analyses in a consistent, intuitive and interactive manner, without the assistance of an expert programmer. Moreover, the optimization work done on the database and application sides have dramatically reduced the time needed to analyze the data and, thus, increased the number of studies that can be performed. A crucial part of any such study is the selection of code lists for diseases, procedures, medications, etc., and we have supported this effort by allowing definitions to be queried using common ontologies and shared conveniently across the organization.
{"title":"Opening the Door to Electronic Medical Records: Using Informatics to Overcome Terabytes","authors":"Michael Farnum, V. Lobanov, F. Defalco, S. Cepeda","doi":"10.1109/BIBM.2011.130","DOIUrl":"https://doi.org/10.1109/BIBM.2011.130","url":null,"abstract":"Databases of medical records contain a wealth of information critical to many areas of research including drug safety, health outcomes, clinical epidemiology and translational medicine. Through commercially available databases, researchers can gain a better understanding of the impact of exposure to drugs and medical devices, identify populations at risk for adverse effects, estimate the prevalence and natural history of medical conditions, and assess drug utilization across different demographic groups. However, the daunting size and complexity of these databases as well as lack of convenient tools to mine them have made this information largely inaccessible to all but a few experts with advanced data management and statistical programming skills. Using a combination of a relational data management strategy and a graphical user front-end, we have developed an approach that allows any medical researcher to perform a number of common searches and analyses in a consistent, intuitive and interactive manner, without the assistance of an expert programmer. Moreover, the optimization work done on the database and application sides have dramatically reduced the time needed to analyze the data and, thus, increased the number of studies that can be performed. A crucial part of any such study is the selection of code lists for diseases, procedures, medications, etc., and we have supported this effort by allowing definitions to be queried using common ontologies and shared conveniently across the organization.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"22 1","pages":"659-659"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88900084","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 : 2011-11-12DOI: 10.1109/BIBMW.2011.6112512
Leina Lu, Liang Zhou, Eric Z. Chen, K. Sun, P. Jiang, Susie Su, Lijun Wang, Hao Sun, Huating Wang
MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression post-transcriptionally, and mounting evidences support the prevalence and functional significance of their interplay with transcription factors (TFs). Here we describe the identification of a regulatory circuit between muscle miRNAs (miR-1, miR-133 and miR-206) and Yin Yang 1 (YY1), an epigenetic repressor of skeletal myogenesis in mouse. Genome-wide identification of potential down-stream targets of YY1 by combining computational prediction with expression profiling data reveals a large number of putative miRNA targets of YY1 during skeletal myoblasts differentiation into myotubes with muscle miRs rank on top of the list. The subsequent experimental results demonstrate that YY1 indeed represses muscle miRs expression in myoblasts and the repression is mediated through multiple enhancers and recruitment of Polycomb complex to several YY1 binding sites. YY1 regulating miR-1 is functionally important for both C2C12 myogenic differentiation and injury-induced muscle regeneration. Furthermore, we demonstrate that miR-1 in turn targets YY1, thus forming a negative feedback loop. Together, these results identify a novel regulatory circuit required for skeletal myogenesis and reinforce the idea that regulatory circuitry involving miRNAs and TFs are prevalent mechanisms.
{"title":"Genome-wide identification of TF-miRNA regulatory networks in myogenesis","authors":"Leina Lu, Liang Zhou, Eric Z. Chen, K. Sun, P. Jiang, Susie Su, Lijun Wang, Hao Sun, Huating Wang","doi":"10.1109/BIBMW.2011.6112512","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112512","url":null,"abstract":"MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression post-transcriptionally, and mounting evidences support the prevalence and functional significance of their interplay with transcription factors (TFs). Here we describe the identification of a regulatory circuit between muscle miRNAs (miR-1, miR-133 and miR-206) and Yin Yang 1 (YY1), an epigenetic repressor of skeletal myogenesis in mouse. Genome-wide identification of potential down-stream targets of YY1 by combining computational prediction with expression profiling data reveals a large number of putative miRNA targets of YY1 during skeletal myoblasts differentiation into myotubes with muscle miRs rank on top of the list. The subsequent experimental results demonstrate that YY1 indeed represses muscle miRs expression in myoblasts and the repression is mediated through multiple enhancers and recruitment of Polycomb complex to several YY1 binding sites. YY1 regulating miR-1 is functionally important for both C2C12 myogenic differentiation and injury-induced muscle regeneration. Furthermore, we demonstrate that miR-1 in turn targets YY1, thus forming a negative feedback loop. Together, these results identify a novel regulatory circuit required for skeletal myogenesis and reinforce the idea that regulatory circuitry involving miRNAs and TFs are prevalent mechanisms.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"17 1","pages":"941-941"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79232524","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 : 2011-11-12DOI: 10.1109/BIBMW.2011.6112438
Hua Xu, S. Abdelrahman, Min Jiang, Jung-wei Fan, Yang Huang
Full parsing recognizes a sentence and generates a syntactic structure of it (a parse tree), which is useful for many natural language processing (NLP) applications. The Stanford Parser is one of the state-of-art parsers in the general English domain. However, there is no formal evaluation of its performance in clinical text that often contains ungrammatical structures. In this study, we randomly selected 50 sentences in the clinical corpus from 2010 i2b2 NLP challenge and manually annotated them to create a gold standard of parse trees. Our evaluation showed that the original Stanford Parser achieved a bracketing F-measure (BF) of 77% on the gold standard. Moreover, we assessed the effect of part-of-speech (POS) tags on parsing and our results showed that manually corrected POS tags achieved a maximum BF of 81%. Furthermore, we analyzed errors of the Stanford Parser and provided valuable insights to large-scale parse tree annotation for clinical text.
{"title":"An initial study of full parsing of clinical text using the Stanford Parser","authors":"Hua Xu, S. Abdelrahman, Min Jiang, Jung-wei Fan, Yang Huang","doi":"10.1109/BIBMW.2011.6112438","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112438","url":null,"abstract":"Full parsing recognizes a sentence and generates a syntactic structure of it (a parse tree), which is useful for many natural language processing (NLP) applications. The Stanford Parser is one of the state-of-art parsers in the general English domain. However, there is no formal evaluation of its performance in clinical text that often contains ungrammatical structures. In this study, we randomly selected 50 sentences in the clinical corpus from 2010 i2b2 NLP challenge and manually annotated them to create a gold standard of parse trees. Our evaluation showed that the original Stanford Parser achieved a bracketing F-measure (BF) of 77% on the gold standard. Moreover, we assessed the effect of part-of-speech (POS) tags on parsing and our results showed that manually corrected POS tags achieved a maximum BF of 81%. Furthermore, we analyzed errors of the Stanford Parser and provided valuable insights to large-scale parse tree annotation for clinical text.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"114 1","pages":"607-614"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79460405","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}
Aldrin Montana, Alex Dekhtyar, Emily R. Neal, Michael Black, C. Kitts
Hierarchical clustering is used in computational biology as a method of comparing sequenced bacterial strain DNA and determining bacterial isolates that belong to the same strain. However, the results of the hierarchical clustering are, at times, difficult to read and interpret. This paper is a case study for the use of a modified hierarchical clustering algorithm, which takes into account the underlying structure of the bacterial DNA isolate collection to which it is applied.
{"title":"Chronology-Sensitive Hierarchical Clustering of Pyrosequenced DNA Samples of E. coli: A Case Study","authors":"Aldrin Montana, Alex Dekhtyar, Emily R. Neal, Michael Black, C. Kitts","doi":"10.1109/BIBM.2011.99","DOIUrl":"https://doi.org/10.1109/BIBM.2011.99","url":null,"abstract":"Hierarchical clustering is used in computational biology as a method of comparing sequenced bacterial strain DNA and determining bacterial isolates that belong to the same strain. However, the results of the hierarchical clustering are, at times, difficult to read and interpret. This paper is a case study for the use of a modified hierarchical clustering algorithm, which takes into account the underlying structure of the bacterial DNA isolate collection to which it is applied.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"143 1","pages":"155-159"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83144452","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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