Real world data sets (as opposed to data from randomized, controlled clinical trials) are becoming increasing available from the healthcare industry. Large databases from EMRs/EHRs, insurance claims, pharmacy records, disease registries etc present unique challenges when they are utilized to support pharmaceutical R&D activities. Such "secondary use" of healthcare data usually starts with an exploratory phase when the researcher takes a high-level view of the available data and starts to "connect the dots". Data exploration is a highly dynamic process: exploratory paths change frequently, sometimes converging, other times diverging, and often resulting in dead ends. Only a small subset of exploratory results end up being formally analyzed to derive quantitative insights. Because of this dynamic nature of data exploration, it is critical that researchers who generate hypotheses, the domain experts, can directly explore in the available data space. Data exploration on large healthcare data sets is often a bottleneck because these data sets tend to be poorly understood in terms of their quality, completeness, consistency, etc. We will discuss this emerging landscape, focusing on case studies to illustrate the powerful convergence of real-world data and technological advancements to help leverage this data.
{"title":"Data Exploration in Secondary Use of Healthcare Data","authors":"Jian Wang","doi":"10.1109/BIBM.2011.129","DOIUrl":"https://doi.org/10.1109/BIBM.2011.129","url":null,"abstract":"Real world data sets (as opposed to data from randomized, controlled clinical trials) are becoming increasing available from the healthcare industry. Large databases from EMRs/EHRs, insurance claims, pharmacy records, disease registries etc present unique challenges when they are utilized to support pharmaceutical R&D activities. Such \"secondary use\" of healthcare data usually starts with an exploratory phase when the researcher takes a high-level view of the available data and starts to \"connect the dots\". Data exploration is a highly dynamic process: exploratory paths change frequently, sometimes converging, other times diverging, and often resulting in dead ends. Only a small subset of exploratory results end up being formally analyzed to derive quantitative insights. Because of this dynamic nature of data exploration, it is critical that researchers who generate hypotheses, the domain experts, can directly explore in the available data space. Data exploration on large healthcare data sets is often a bottleneck because these data sets tend to be poorly understood in terms of their quality, completeness, consistency, etc. We will discuss this emerging landscape, focusing on case studies to illustrate the powerful convergence of real-world data and technological advancements to help leverage this data.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"43 1","pages":"658-658"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90476152","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.2011.6112397
M. Maleki, Md. Mominul Aziz, L. Rueda
Identification and analysis of types of protein-protein interactions (PPI) is an important problem in molecular biology because of its key role in many biological processes in living cells. In this paper, we focus on obligate and non-obligate complexes, their prediction and analysis. We propose a feature selection scheme called MRMRpro which is based on Minimum Redundancy Maximum Relevance (MRMR) to focus on the most discriminative and relevant properties to distinguish between these two types of complexes. Our prediction approach uses desolvation energies of pairs of atoms or amino acids present in the interfaces of such complexes. Our results on two well-known datasets confirm that MRMRpro leads to significant improvements on performance by finding more relevant features for prediction. Furthermore, the prediction performance of our biologically guided feature selection methods demonstrate that hydrophobic amino acids are more discriminating than hydrophilic and amphipathic amino acids to distinguish between obligate and non-obligate complexes.
{"title":"Analysis of relevant physicochemical properties in obligate and non-obligate protein-protein interactions","authors":"M. Maleki, Md. Mominul Aziz, L. Rueda","doi":"10.1109/BIBMW.2011.6112397","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112397","url":null,"abstract":"Identification and analysis of types of protein-protein interactions (PPI) is an important problem in molecular biology because of its key role in many biological processes in living cells. In this paper, we focus on obligate and non-obligate complexes, their prediction and analysis. We propose a feature selection scheme called MRMRpro which is based on Minimum Redundancy Maximum Relevance (MRMR) to focus on the most discriminative and relevant properties to distinguish between these two types of complexes. Our prediction approach uses desolvation energies of pairs of atoms or amino acids present in the interfaces of such complexes. Our results on two well-known datasets confirm that MRMRpro leads to significant improvements on performance by finding more relevant features for prediction. Furthermore, the prediction performance of our biologically guided feature selection methods demonstrate that hydrophobic amino acids are more discriminating than hydrophilic and amphipathic amino acids to distinguish between obligate and non-obligate complexes.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"12 1","pages":"345-351"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81926538","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.2011.6112457
Iksoo Huh, Sohee Oh, T. Park
As a result of genotyping technologies, genome-wide association studies (GWAS) have been widely used to identify genetic variants associated with common complex traits. While most GWAS have focused on associations with single genetic variants, the investigation of multiple joint genetic variants is essential for understanding genetic architecture of complex traits because common complex traits are associated with multiple genetic variants. However, it is not easy to conduct the multiple joint genetic variants analysis and to identify high order interactions using a number of genetic variants in GWAS. In this study, we propose a stepwise method based on the Chi-square test in order to identify causal joint multiple genetic variants in GWAS. Through simulation studies, we examine the properties of the stepwise method and then apply the proposed method to a GWA data for detecting joint multiple genetic variants for age-related macular degeneration.
{"title":"A chi-square test for detecting multiple joint genetic variants in genome-wide association studies","authors":"Iksoo Huh, Sohee Oh, T. Park","doi":"10.1109/BIBMW.2011.6112457","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112457","url":null,"abstract":"As a result of genotyping technologies, genome-wide association studies (GWAS) have been widely used to identify genetic variants associated with common complex traits. While most GWAS have focused on associations with single genetic variants, the investigation of multiple joint genetic variants is essential for understanding genetic architecture of complex traits because common complex traits are associated with multiple genetic variants. However, it is not easy to conduct the multiple joint genetic variants analysis and to identify high order interactions using a number of genetic variants in GWAS. In this study, we propose a stepwise method based on the Chi-square test in order to identify causal joint multiple genetic variants in GWAS. Through simulation studies, we examine the properties of the stepwise method and then apply the proposed method to a GWA data for detecting joint multiple genetic variants for age-related macular degeneration.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"9 1","pages":"708-713"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76189876","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.2011.6112428
Pavani Davuluri, Jie Wu, Ashwin Belle, Charles Cockrell, Yang Tang, Kevin Ward, K. Najarian, R. H. Hargraves
Hemorrhage is the leading cause of death in patients with severe pelvic fractures within the first 24 hours after the injury. Hence, it is vital for physicians to quickly identify hemorrhage and assess bleeding severity. However, it is rather time consuming for physicians to evaluate all the CT images. Therefore, an automated hemorrhage segmentation system is needed to assist physicians. This paper proposes a hybrid approach for hemorrhage segmentation from pelvic CT scans. This approach utilizes region growing technique with integration of contrast information from the previous and subsequent slices. The results show that the method is able to segment hemorrhage well with acceptable results. Hemorrhage volume is also determined. A statistical t-test is conducted to determine if the calculated hemorrhage volume using the proposed method is significantly different from the manually detected volume.
{"title":"A hybrid approach for hemorrhage segmentation in pelvic CT scans","authors":"Pavani Davuluri, Jie Wu, Ashwin Belle, Charles Cockrell, Yang Tang, Kevin Ward, K. Najarian, R. H. Hargraves","doi":"10.1109/BIBMW.2011.6112428","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112428","url":null,"abstract":"Hemorrhage is the leading cause of death in patients with severe pelvic fractures within the first 24 hours after the injury. Hence, it is vital for physicians to quickly identify hemorrhage and assess bleeding severity. However, it is rather time consuming for physicians to evaluate all the CT images. Therefore, an automated hemorrhage segmentation system is needed to assist physicians. This paper proposes a hybrid approach for hemorrhage segmentation from pelvic CT scans. This approach utilizes region growing technique with integration of contrast information from the previous and subsequent slices. The results show that the method is able to segment hemorrhage well with acceptable results. Hemorrhage volume is also determined. A statistical t-test is conducted to determine if the calculated hemorrhage volume using the proposed method is significantly different from the manually detected volume.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"195 1","pages":"548-554"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76924767","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}
has sparse data with the number of time points being less than the number of proteins. Usually, each replicate is modeled separately, however, here all the information in each of the replicates is used to make a composite inference about the signal network. The composite inference comes from combining well structured Bayesian probabilistic modeling with a multi-faceted Markov Chain Monte Carlo algorithm. Based on simulations which investigate many different types of network interactions and experimental variabilities, the composite examination uncovers many important relationships within the network.
{"title":"Probabilistic Signal Network Models from Multiple Replicates of Sparse Time-Course Data","authors":"Kristopher L. Patton, D. J. John, J. Norris","doi":"10.1109/BIBM.2011.78","DOIUrl":"https://doi.org/10.1109/BIBM.2011.78","url":null,"abstract":"has sparse data with the number of time points being less than the number of proteins. Usually, each replicate is modeled separately, however, here all the information in each of the replicates is used to make a composite inference about the signal network. The composite inference comes from combining well structured Bayesian probabilistic modeling with a multi-faceted Markov Chain Monte Carlo algorithm. Based on simulations which investigate many different types of network interactions and experimental variabilities, the composite examination uncovers many important relationships within the network.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"37 1","pages":"450-455"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74039993","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.2011.6112424
Faiz M. Hasanuzzaman, Yingli Tian, Qingshan Liu
In this paper, we present a new framework of identifying medicine bottles using a combination of a video camera and Radio Frequency Identification (RFID) sensors for applications of monitoring the elderly's activities of daily living (ADLs) at home. RFID tags are attached to medicine bottles and first detected by RFID readers from the antenna. However, the RFID detection can only detect RFID tags within a certain range of the antenna. Once a medicine bottle is moved out of the range of the RFID antenna, a camera will be activated to continue detecting and tracking the medicine bottle for further action analysis based on moving object detection and color model of the medicine bottle. The experimental results demonstrate 100% detection accuracy for identifying medicine bottles.
{"title":"Identifying medicine bottles by incorporating RFID and video analysis","authors":"Faiz M. Hasanuzzaman, Yingli Tian, Qingshan Liu","doi":"10.1109/BIBMW.2011.6112424","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112424","url":null,"abstract":"In this paper, we present a new framework of identifying medicine bottles using a combination of a video camera and Radio Frequency Identification (RFID) sensors for applications of monitoring the elderly's activities of daily living (ADLs) at home. RFID tags are attached to medicine bottles and first detected by RFID readers from the antenna. However, the RFID detection can only detect RFID tags within a certain range of the antenna. Once a medicine bottle is moved out of the range of the RFID antenna, a camera will be activated to continue detecting and tracking the medicine bottle for further action analysis based on moving object detection and color model of the medicine bottle. The experimental results demonstrate 100% detection accuracy for identifying medicine bottles.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"11 1","pages":"528-529"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73242657","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.2011.6112399
I. Hashmi, Bahar Akbal-Delibas, Nurit Haspel, Amarda Shehu
Structural modeling of molecular assemblies lies at the heart of understanding molecular interactions and biological function. We present a method for docking protein molecules and elucidating native-like structures of protein dimers. Our method is based on geometric hashing to ensure the feasibility of searching the combined conformational space of dimeric structures. The search space is narrowed by focusing the sought rigid-body transformations around surface areas with evolutionary-conserved amino-acids. Recent analysis of protein assemblies reveals that many functional interfaces are significantly conserved throughout evolution. We test our method on a broad list of sixteen diverse protein dimers and compare the structures found to have lowest lRMSD to the known native dimeric structures to those reported by other groups. Our results show that focusing the search around evolutionary-conserved interfaces results in lower lRMSDs.
{"title":"Protein docking with information on evolutionary conserved interfaces","authors":"I. Hashmi, Bahar Akbal-Delibas, Nurit Haspel, Amarda Shehu","doi":"10.1109/BIBMW.2011.6112399","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112399","url":null,"abstract":"Structural modeling of molecular assemblies lies at the heart of understanding molecular interactions and biological function. We present a method for docking protein molecules and elucidating native-like structures of protein dimers. Our method is based on geometric hashing to ensure the feasibility of searching the combined conformational space of dimeric structures. The search space is narrowed by focusing the sought rigid-body transformations around surface areas with evolutionary-conserved amino-acids. Recent analysis of protein assemblies reveals that many functional interfaces are significantly conserved throughout evolution. We test our method on a broad list of sixteen diverse protein dimers and compare the structures found to have lowest lRMSD to the known native dimeric structures to those reported by other groups. Our results show that focusing the search around evolutionary-conserved interfaces results in lower lRMSDs.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"10 1","pages":"358-365"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73634890","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.2011.6112415
Hongwei Wu, Yaming Lin, Fun Choi Chan, R. Alba-Flores
Network analysis-based module detection has significant implications in many fields. In cellular/ molecular biology, module detection based on analyses of metabolic/regulatory networks will not only help us understand more about the function and evolution of cellular machinery of an organism, but will also provide tractable contextual information for potential drug targets and facilitate improvements in drug designs. We here present our preliminary study on the module detection for bacteria based on the spectral clustering of the protein-protein functional association networks. We first examined how the parameter of the spectral clustering algorithm (i.e., the number of clusters) affects our module detection results, and demonstrated that when the number of clusters was set too small or too large the resulting module collection deteriorate in terms of gene coverage and intra-module association. We then compared our predicted modules against the randomly generated modules, and demonstrated that our modules (i) have a higher ratio of the intra-module to inter-module gene-gene functional association scores and (ii) can better capture the modularization information inherent in the experimentally verified modules. Finally we compared the module collections of seven bacterial organisms, and observed that modules related to membrane transport and cell motility are among those that are conserved among multiple organisms. Because it is desirable from both scientific and technical points of view to study functional modules at various resolution levels, we believe that the spectral clustering algorithm, with the flexibility rendered by different parameter settings, provides an appropriate solution in terms of capturing the modularization properties of networks and computational affordability.
{"title":"Module detection for bacteria based on spectral clustering of protein-protein functional association networks","authors":"Hongwei Wu, Yaming Lin, Fun Choi Chan, R. Alba-Flores","doi":"10.1109/BIBMW.2011.6112415","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112415","url":null,"abstract":"Network analysis-based module detection has significant implications in many fields. In cellular/ molecular biology, module detection based on analyses of metabolic/regulatory networks will not only help us understand more about the function and evolution of cellular machinery of an organism, but will also provide tractable contextual information for potential drug targets and facilitate improvements in drug designs. We here present our preliminary study on the module detection for bacteria based on the spectral clustering of the protein-protein functional association networks. We first examined how the parameter of the spectral clustering algorithm (i.e., the number of clusters) affects our module detection results, and demonstrated that when the number of clusters was set too small or too large the resulting module collection deteriorate in terms of gene coverage and intra-module association. We then compared our predicted modules against the randomly generated modules, and demonstrated that our modules (i) have a higher ratio of the intra-module to inter-module gene-gene functional association scores and (ii) can better capture the modularization information inherent in the experimentally verified modules. Finally we compared the module collections of seven bacterial organisms, and observed that modules related to membrane transport and cell motility are among those that are conserved among multiple organisms. Because it is desirable from both scientific and technical points of view to study functional modules at various resolution levels, we believe that the spectral clustering algorithm, with the flexibility rendered by different parameter settings, provides an appropriate solution in terms of capturing the modularization properties of networks and computational affordability.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"12 1","pages":"465-472"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76261021","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.2011.6112443
Zainab Haydari, Yanqing Zhang, H. Soltanian-Zadeh
A novel algorithm is proposed for identifying epileptic features in electroencephalograph (EEG) signals automatically. The proposed algorithm is based on the combination of the Genetic Algorithm (GA) and the Wavelet transform. Optimal Wavelet basis functions that adapt the spikes of the EEG signal are first designed using GA. Then they are used as matched filters to identify the spikes related to seizure activity from the EEG recordings using Wavelet transform and a threshold-based estimation method. The method can estimate the number and the location of epileptic spikes in an EEG signal very fast and almost in real time. Hence, it is suitable for data mining of EEG recordings of epileptic patients for fundamental studies of epilepsy, prediction of seizures, and treatment of epilepsy. We have applied and evaluated the method using different samples of real clinical EEG data of epileptic patients, where it has shown a very high sensitivity (more than 90%) and selectivity (more than 90%).
{"title":"Semi-automatic epilepsy spike detection from EEG signal using Genetic Algorithm and Wavelet transform","authors":"Zainab Haydari, Yanqing Zhang, H. Soltanian-Zadeh","doi":"10.1109/BIBMW.2011.6112443","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112443","url":null,"abstract":"A novel algorithm is proposed for identifying epileptic features in electroencephalograph (EEG) signals automatically. The proposed algorithm is based on the combination of the Genetic Algorithm (GA) and the Wavelet transform. Optimal Wavelet basis functions that adapt the spikes of the EEG signal are first designed using GA. Then they are used as matched filters to identify the spikes related to seizure activity from the EEG recordings using Wavelet transform and a threshold-based estimation method. The method can estimate the number and the location of epileptic spikes in an EEG signal very fast and almost in real time. Hence, it is suitable for data mining of EEG recordings of epileptic patients for fundamental studies of epilepsy, prediction of seizures, and treatment of epilepsy. We have applied and evaluated the method using different samples of real clinical EEG data of epileptic patients, where it has shown a very high sensitivity (more than 90%) and selectivity (more than 90%).","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"20 1","pages":"635-638"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91169477","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.2011.6112536
C. B. Mukherjee, H. Winkler, Xiuwen Liu, M. Dutta, K. Roux, K. Taylor
The HIV/SIV envelope spikes initiate infection in the host cells and hence understanding their structure is one of the primary focus areas of modern research in structural biology. The structures of the native envelope spikes are still unknown. A mutant form of SIV virions has 80 to 90 envelope spikes per virion; HIV virions possess only 8–9 spikes. Thus, any electron tomographic study of the spike structure becomes very tedious if spikes are selected by hand for further processing. Consequently automating the process of spike selection is very important for determining the spike structure in situ. To this end, we have developed a nearly automated procedure for spike selection based on a “segmentation by classification” philosophy.
{"title":"An accurate and reliable method for automatic picking of HIV/SIV spikes","authors":"C. B. Mukherjee, H. Winkler, Xiuwen Liu, M. Dutta, K. Roux, K. Taylor","doi":"10.1109/BIBMW.2011.6112536","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112536","url":null,"abstract":"The HIV/SIV envelope spikes initiate infection in the host cells and hence understanding their structure is one of the primary focus areas of modern research in structural biology. The structures of the native envelope spikes are still unknown. A mutant form of SIV virions has 80 to 90 envelope spikes per virion; HIV virions possess only 8–9 spikes. Thus, any electron tomographic study of the spike structure becomes very tedious if spikes are selected by hand for further processing. Consequently automating the process of spike selection is very important for determining the spike structure in situ. To this end, we have developed a nearly automated procedure for spike selection based on a “segmentation by classification” philosophy.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"11 1","pages":"998-1000"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91195541","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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