Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112350
Bryan Bai, S. C. Kremer
We examine the problem of classifying biological sequences, and in particular the challenge of generalizing results to novel input data. We observe that the high-dimensionality of sequence data representations results in an extremely sparsely populated input space. This motivates a need for regularization (a form of inductive bias), in order to achieve generalization. We discuss regularization in the context of regular neural networks, deep belief networks and support vector machines, and provide experimental results for these architectures. Our results support the importance of using an effective regularization method and identify which methods work well on a real-world dataset.
{"title":"Regularization of sequence data for machine learning","authors":"Bryan Bai, S. C. Kremer","doi":"10.1109/BIBMW.2011.6112350","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112350","url":null,"abstract":"We examine the problem of classifying biological sequences, and in particular the challenge of generalizing results to novel input data. We observe that the high-dimensionality of sequence data representations results in an extremely sparsely populated input space. This motivates a need for regularization (a form of inductive bias), in order to achieve generalization. We discuss regularization in the context of regular neural networks, deep belief networks and support vector machines, and provide experimental results for these architectures. Our results support the importance of using an effective regularization method and identify which methods work well on a real-world dataset.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"13 1","pages":"19-25"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87631016","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}
Jianxin Wang, Xiaoqing Peng, Min Li, Yong Luo, Yi Pan
In recent years, more and more attentions are focused on modelling and analyzing dynamic network. Some researchers attempted to extract dynamic network by combining the dynamic information from gene expression data or sub cellular localization data with protein network. However, the dynamics of proteins' presence does not guarantee the dynamics of interactions, since the presence of a protein does not indicate the protein's activity. The activity of a protein is closely connected with its function. Thus only the dynamics of proteins activity ensure the dynamics of interaction. The gene expression of a cellular process or cycle carries more information than only the dynamics of proteins' presence. We assume that a protein is active when its expression values are near its maximum expression value, since the expression quantity will decrease after it has performed its function that leads a feedback for controlling the expression quantity. In this paper, we proposed a method to identify active time points for each protein in a cellular process or cycle by using a 3-sigma principle to compute an active threshold for each gene according to the characteristics of its expression curve. Combined the activity information and protein interaction network, we can construct an active protein interaction network (APPI). To demonstrate the efficiency of APPI network model, we applied it on complex detection. Compared with single threshold time series networks, APPI network achieves a better performance on protein complex prediction.
{"title":"Active Protein Interaction Network and Its Application on Protein Complex Detection","authors":"Jianxin Wang, Xiaoqing Peng, Min Li, Yong Luo, Yi Pan","doi":"10.1109/BIBM.2011.45","DOIUrl":"https://doi.org/10.1109/BIBM.2011.45","url":null,"abstract":"In recent years, more and more attentions are focused on modelling and analyzing dynamic network. Some researchers attempted to extract dynamic network by combining the dynamic information from gene expression data or sub cellular localization data with protein network. However, the dynamics of proteins' presence does not guarantee the dynamics of interactions, since the presence of a protein does not indicate the protein's activity. The activity of a protein is closely connected with its function. Thus only the dynamics of proteins activity ensure the dynamics of interaction. The gene expression of a cellular process or cycle carries more information than only the dynamics of proteins' presence. We assume that a protein is active when its expression values are near its maximum expression value, since the expression quantity will decrease after it has performed its function that leads a feedback for controlling the expression quantity. In this paper, we proposed a method to identify active time points for each protein in a cellular process or cycle by using a 3-sigma principle to compute an active threshold for each gene according to the characteristics of its expression curve. Combined the activity information and protein interaction network, we can construct an active protein interaction network (APPI). To demonstrate the efficiency of APPI network model, we applied it on complex detection. Compared with single threshold time series networks, APPI network achieves a better performance on protein complex prediction.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"12 1","pages":"37-42"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90168797","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}
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}
Protein Modeling conceptualizes the protein energy landscape as a funnel with the native structure at the low-energy minimum. Current protein structure prediction algorithms seek the global minimum by searching for low-energy conformations in the hope that some of these reside in local minima near the native structure. The search techniques employed, however, fail to explicitly model these local minima. This work proposes a memetic algorithm which combines methods from evolutionary computation with cutting-edge structure prediction protocols. The Protein Local Optima Walk (PLOW) algorithm proposed here explores the space of local minima by explicitly projecting each move in the conformation space to a nearby local minimum. This allows PLOW to jump over local energy barriers and more effectively sample near-native conformations. Analysis across a broad range of proteins shows that PLOW outperforms an MMC-based method and compares favorably against other published abini to structure prediction algorithms.
蛋白质建模将蛋白质能量景观概念化为具有低能量最小值的天然结构的漏斗。目前的蛋白质结构预测算法通过寻找低能构象来寻求全局最小值，并希望其中一些位于本地结构附近的局部最小值。然而，所采用的搜索技术不能明确地模拟这些局部最小值。本文提出了一种模因算法，该算法将进化计算方法与前沿结构预测协议相结合。本文提出的蛋白质局部最优行走(PLOW)算法通过显式地将构象空间中的每次移动映射到附近的局部最小值来探索局部最小值空间。这使得PLOW能够跳过局部能量障碍，更有效地采样接近原生的构象。对多种蛋白质的分析表明，PLOW优于基于mmc的方法，并且与其他已发表的abini - to - structure预测算法相比具有优势。
{"title":"Populating Local Minima in the Protein Conformational Space","authors":"Brian S. Olson, Amarda Shehu","doi":"10.1109/BIBM.2011.22","DOIUrl":"https://doi.org/10.1109/BIBM.2011.22","url":null,"abstract":"Protein Modeling conceptualizes the protein energy landscape as a funnel with the native structure at the low-energy minimum. Current protein structure prediction algorithms seek the global minimum by searching for low-energy conformations in the hope that some of these reside in local minima near the native structure. The search techniques employed, however, fail to explicitly model these local minima. This work proposes a memetic algorithm which combines methods from evolutionary computation with cutting-edge structure prediction protocols. The Protein Local Optima Walk (PLOW) algorithm proposed here explores the space of local minima by explicitly projecting each move in the conformation space to a nearby local minimum. This allows PLOW to jump over local energy barriers and more effectively sample near-native conformations. Analysis across a broad range of proteins shows that PLOW outperforms an MMC-based method and compares favorably against other published abini to structure prediction algorithms.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"93 1","pages":"114-117"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77205213","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.2011.6112549
Lijing Xu, R. Homayouni, E. George
Recent research has focused on incorporating biological function and pathway information into the analysis of gene expression data, partly as a means of compensating for insufficient experimental replications, low signal to noise, lack of reproducibility and/or multiple testing confounds. A Bayesian approach seems to be ideal for incorporating functional information into gene expression data analysis. In this study, we tested the feasibility of using literature derived gene relationships in a Bayesian model to analyze gene expression data. Prior distributions were constructed based on gene associations derived from the biomedical literature using Latent Semantic Indexing (LSI). The LSI model was built using more than 1 million Medline abstracts corresponding to 22,000 human and mouse genes. A key advantage of LSI is that both explicit and implicit gene relationships can be derived from the literature. Gene neighborhoods were determined using latent Gaussian Markov random fields and logistic transformation of the latent variables. We tested the procedure on a microarray dataset for interferon-stimulated genes in mouse embryonic fibroblasts. By integrating functional information from literature, Bayesian approach identified relevant genes that previously did not meet the 0.05 significance level. In comparison to a standard mixture model, spatial mixture model has more power for identifying direct and indirect interferon regulated genes. The spatial model enhanced the ranks of some genes which are known to be affected by interferon treatment, such as Nmi (NMI N-myc and STAT interactor) and ifi35 (interferon-induced protein 35). It also identified some genes that previously were ignored because of the marginal p-values, such as dpysl2, map2k1, msn, Psck5, and Il6st. Interestingly, these genes appear to be indirectly related to interferon treatment. In summary, we show that our procedure increases statistical power and produces more biologically meaningful gene lists. These results suggest that Bayesian methods which incorporate functional information from the literature may improve analysis of gene expression data.
{"title":"Literature based Bayesian analysis of gene expression data","authors":"Lijing Xu, R. Homayouni, E. George","doi":"10.1109/BIBMW.2011.6112549","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112549","url":null,"abstract":"Recent research has focused on incorporating biological function and pathway information into the analysis of gene expression data, partly as a means of compensating for insufficient experimental replications, low signal to noise, lack of reproducibility and/or multiple testing confounds. A Bayesian approach seems to be ideal for incorporating functional information into gene expression data analysis. In this study, we tested the feasibility of using literature derived gene relationships in a Bayesian model to analyze gene expression data. Prior distributions were constructed based on gene associations derived from the biomedical literature using Latent Semantic Indexing (LSI). The LSI model was built using more than 1 million Medline abstracts corresponding to 22,000 human and mouse genes. A key advantage of LSI is that both explicit and implicit gene relationships can be derived from the literature. Gene neighborhoods were determined using latent Gaussian Markov random fields and logistic transformation of the latent variables. We tested the procedure on a microarray dataset for interferon-stimulated genes in mouse embryonic fibroblasts. By integrating functional information from literature, Bayesian approach identified relevant genes that previously did not meet the 0.05 significance level. In comparison to a standard mixture model, spatial mixture model has more power for identifying direct and indirect interferon regulated genes. The spatial model enhanced the ranks of some genes which are known to be affected by interferon treatment, such as Nmi (NMI N-myc and STAT interactor) and ifi35 (interferon-induced protein 35). It also identified some genes that previously were ignored because of the marginal p-values, such as dpysl2, map2k1, msn, Psck5, and Il6st. Interestingly, these genes appear to be indirectly related to interferon treatment. In summary, we show that our procedure increases statistical power and produces more biologically meaningful gene lists. These results suggest that Bayesian methods which incorporate functional information from the literature may improve analysis of gene expression data.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"14 1","pages":"1032-1032"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74476114","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.2011.6112415
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.2011.6112428
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}
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