Pub Date : 2016-12-01DOI: 10.1109/BIBM.2016.7822540
Kouki Yonezawa, Tsukasa Mori, S. Shigeno, A. Ogura
Alternative splicing is a mechanism to produce gene expression diversity under the constraint of a limited number of genes, causing spatiotemporal gene expression in many tissues and developmental processes in organisms. This mechanism is well studied in model organisms but not in non-model organisms because the current standard method requires genomic sequences as well as fully annotated information of exons and introns, that are not accessible from non-model organisms. However, it is essential to uncover the landscape of alternative splicing of organisms to understand its evolutionary impacts and roles. We developed a method for condition-specific alternative splicing estimation based on de novo transcriptome assembly, and it would help to enlarge knowledge of alternative splicing functionalized in non-model organisms. The software is deposited to https://github.com/koukiyonezawa/DASE.
{"title":"DASE: Condition-specific differential alternative splicing variants estimation method without reference genome sequence, and its application to non-model organisms","authors":"Kouki Yonezawa, Tsukasa Mori, S. Shigeno, A. Ogura","doi":"10.1109/BIBM.2016.7822540","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822540","url":null,"abstract":"Alternative splicing is a mechanism to produce gene expression diversity under the constraint of a limited number of genes, causing spatiotemporal gene expression in many tissues and developmental processes in organisms. This mechanism is well studied in model organisms but not in non-model organisms because the current standard method requires genomic sequences as well as fully annotated information of exons and introns, that are not accessible from non-model organisms. However, it is essential to uncover the landscape of alternative splicing of organisms to understand its evolutionary impacts and roles. We developed a method for condition-specific alternative splicing estimation based on de novo transcriptome assembly, and it would help to enlarge knowledge of alternative splicing functionalized in non-model organisms. The software is deposited to https://github.com/koukiyonezawa/DASE.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115044240","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822783
Shinuk Kim, M. Kon, Hyowon Lee
Numerous computational studies related to cancer have been published, but increasing prediction accuracy of molecular datasets remains a challenge. Here we present a comparison of prediction based on a feature selection method combined with machine learning, for microRNA-Seq (miRNA-Seq) and mRNA-Seq data. We have tested three different approaches: support vector machine, decision tree and k nearest neighbors, under two different feature selection methods: fisher feature selection and infinite feature selection.
{"title":"Some comparisons of gene expression classifiers","authors":"Shinuk Kim, M. Kon, Hyowon Lee","doi":"10.1109/BIBM.2016.7822783","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822783","url":null,"abstract":"Numerous computational studies related to cancer have been published, but increasing prediction accuracy of molecular datasets remains a challenge. Here we present a comparison of prediction based on a feature selection method combined with machine learning, for microRNA-Seq (miRNA-Seq) and mRNA-Seq data. We have tested three different approaches: support vector machine, decision tree and k nearest neighbors, under two different feature selection methods: fisher feature selection and infinite feature selection.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124053063","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822814
Dan Meng, Guitao Cao, Zhihai He, W. Cao
Facial expression recognition plays an important role in lie detection, and computer-aided diagnosis. Many deep learning facial expression feature extraction methods have a great improvement in recognition accuracy and robutness than traditional feature extraction methods. However, most of current deep learning methods need special parameter tuning and ad hoc fine-tuning tricks. This paper proposes a novel feature extraction model called Locally Linear Embedding Network (LLENet) for facial expression recognition. The proposed LLENet first reconstructs image sets for the cropped images. Unlike previous deep convolutional neural networks that initialized convolutional kernels randomly, we learn multi-stage kernels from reconstructed image sets directly in a supervised way. Also, we create an improved LLE to select kernels, from which we can obtain the most representative feature maps. Furthermore, to better measure the contribution of these kernels, a new distance based on kernel Euclidean is proposed. After the procedure of multi-scale feature analysis, feature representations are finally sent into a linear classifier. Experimental results on facial expression datasets (CK+) show that the proposed model can capture most representative features and thus improves previous results.
{"title":"Facial expression recognition based on LLENet","authors":"Dan Meng, Guitao Cao, Zhihai He, W. Cao","doi":"10.1109/BIBM.2016.7822814","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822814","url":null,"abstract":"Facial expression recognition plays an important role in lie detection, and computer-aided diagnosis. Many deep learning facial expression feature extraction methods have a great improvement in recognition accuracy and robutness than traditional feature extraction methods. However, most of current deep learning methods need special parameter tuning and ad hoc fine-tuning tricks. This paper proposes a novel feature extraction model called Locally Linear Embedding Network (LLENet) for facial expression recognition. The proposed LLENet first reconstructs image sets for the cropped images. Unlike previous deep convolutional neural networks that initialized convolutional kernels randomly, we learn multi-stage kernels from reconstructed image sets directly in a supervised way. Also, we create an improved LLE to select kernels, from which we can obtain the most representative feature maps. Furthermore, to better measure the contribution of these kernels, a new distance based on kernel Euclidean is proposed. After the procedure of multi-scale feature analysis, feature representations are finally sent into a linear classifier. Experimental results on facial expression datasets (CK+) show that the proposed model can capture most representative features and thus improves previous results.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128906021","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822742
Yan Chen, Qinglin Zhao, Bin Hu, Jianpeng Li, Hua Jiang, Wenhua Lin, Yang Li, Shuangshuang Zhou, Hong Peng
Electroencephalogram (EEG) is a noninvasive method to record electrical activity of brain and it has been used extensively in research of brain function due to its high time resolution. However raw EEG is a mixture of signals, which contains noises such as Ocular Artifact (OA) that is irrelevant to the cognitive function of brain. To remove OAs from EEG, many methods have been proposed, such as Independent Components Analysis (ICA), Discrete Wavelet Transform (DWT), Adaptive Noise Cancellation (ANC) and Wavelet Packet Transform (WPT). In this paper, we present a novel hybrid de-noising method which uses Discrete Wavelet Transform (DWT) and Kalman Filtering to remove OAs in EEG. Firstly, we used this method on simulated data. The Mean Squared Error (MSE) of DWT-Kalman method was 0.0017, significantly lower compared to results using WPT-ICA and DWT-ANC, which were 0.0468 and 0.0052, respectively. Meanwhile, the Mean Absolute Error (MAE) using DWT-Kalman achieved an average of 0.0052, which also performed better than WPT-ICA and DWT-ANC, which were 0.0218 and 0.0115, respectively. Then we applied the proposed approach to the raw data collected by our prototype three-channel EEG collector and 64-channel Braincap from BRAIN PRODUCTS. On both data, our method achieved satisfying results. This method does not rely on any particular electrode or the number of electrodes in certain system, so it is recommended for ubiquitous applications.
{"title":"A method of removing Ocular Artifacts from EEG using Discrete Wavelet Transform and Kalman Filtering","authors":"Yan Chen, Qinglin Zhao, Bin Hu, Jianpeng Li, Hua Jiang, Wenhua Lin, Yang Li, Shuangshuang Zhou, Hong Peng","doi":"10.1109/BIBM.2016.7822742","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822742","url":null,"abstract":"Electroencephalogram (EEG) is a noninvasive method to record electrical activity of brain and it has been used extensively in research of brain function due to its high time resolution. However raw EEG is a mixture of signals, which contains noises such as Ocular Artifact (OA) that is irrelevant to the cognitive function of brain. To remove OAs from EEG, many methods have been proposed, such as Independent Components Analysis (ICA), Discrete Wavelet Transform (DWT), Adaptive Noise Cancellation (ANC) and Wavelet Packet Transform (WPT). In this paper, we present a novel hybrid de-noising method which uses Discrete Wavelet Transform (DWT) and Kalman Filtering to remove OAs in EEG. Firstly, we used this method on simulated data. The Mean Squared Error (MSE) of DWT-Kalman method was 0.0017, significantly lower compared to results using WPT-ICA and DWT-ANC, which were 0.0468 and 0.0052, respectively. Meanwhile, the Mean Absolute Error (MAE) using DWT-Kalman achieved an average of 0.0052, which also performed better than WPT-ICA and DWT-ANC, which were 0.0218 and 0.0115, respectively. Then we applied the proposed approach to the raw data collected by our prototype three-channel EEG collector and 64-channel Braincap from BRAIN PRODUCTS. On both data, our method achieved satisfying results. This method does not rely on any particular electrode or the number of electrodes in certain system, so it is recommended for ubiquitous applications.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128961348","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822582
Chuanchao Zhang, Juan Liu, Qianqian Shi, Xiangtian Yu, T. Zeng, Luonan Chen
Integration of different genomic profiles is challenging to understand complex diseases in a multi-view manner. Computational method is needed to preserve useful information of data types as well as correct bias. Thus, we proposed a novel framework pattern fusion analysis (PFA), to fuse the local sample patterns into a global pattern of patients with respect to the underlying data, by adaptively aligning the information in each type of biological data. In particular, PFA can adjust the distinct data types and achieve more robust sample pattern within different profiles. To validate the effectiveness of PFA, we tested PFA on various synthetic datasets and found that PFA is able to effectively capture the intrinsic clustering structure than the state-of-the-art integrative methods, such as moCluster, iClusterPlus and SNF. Moreover, in a case study on kidney cancer, PFA not only identified the multi-way feature modules among the prior-known disease associated genes, methylations and miRNAs, but also outperformed in cancer subtypes identification and could get effective clinical prognosis prediction. Totally, PFA not only provides new insights on the more holistic & systems-level sample pattern, but also supplies a new way for selecting more informative types of biological data.
{"title":"Integration of multiple heterogeneous omics data","authors":"Chuanchao Zhang, Juan Liu, Qianqian Shi, Xiangtian Yu, T. Zeng, Luonan Chen","doi":"10.1109/BIBM.2016.7822582","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822582","url":null,"abstract":"Integration of different genomic profiles is challenging to understand complex diseases in a multi-view manner. Computational method is needed to preserve useful information of data types as well as correct bias. Thus, we proposed a novel framework pattern fusion analysis (PFA), to fuse the local sample patterns into a global pattern of patients with respect to the underlying data, by adaptively aligning the information in each type of biological data. In particular, PFA can adjust the distinct data types and achieve more robust sample pattern within different profiles. To validate the effectiveness of PFA, we tested PFA on various synthetic datasets and found that PFA is able to effectively capture the intrinsic clustering structure than the state-of-the-art integrative methods, such as moCluster, iClusterPlus and SNF. Moreover, in a case study on kidney cancer, PFA not only identified the multi-way feature modules among the prior-known disease associated genes, methylations and miRNAs, but also outperformed in cancer subtypes identification and could get effective clinical prognosis prediction. Totally, PFA not only provides new insights on the more holistic & systems-level sample pattern, but also supplies a new way for selecting more informative types of biological data.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129923959","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822811
Hyojin Kang, Junehawk Lee, S. Yu
Differential Co-Expression Networks (DCENs) are graphical representations of genes showing differential co-expression pattern in response to experimental conditions or genetic changes. They have been successfully applied to identify condition-specific modules and provide a picture of the dynamic changes in gene regulatory networks. DCENs analysis investigates the differences among gene interconnections by calculating the expression correlation change of each gene pair between conditions. In this study, we collected many different datasets from NCBI GEO including 25 RNA-seq and 2,102 microarray samples derived from human brain and blood in Alzheimer's disease and performed differential co-expression analyses to identify functional modules responsible for the characterization of Alzheimer's disease. The DCENs were generated using Pearson correlation coefficient and meta-analysis was conducted using rank-based method. The preliminary results show that the structural characteristics of DCENs can provide new insights into the underlying gene regulatory dynamics in Alzheimer's disease. There is low size overlap between microarray- and RNA-seq-derived DCENs however, DCENs from RNA-seq would complement ones from microarray due to the higher coverage and dynamic range of RNA-seq.
{"title":"Differential Co-Expression Networks using RNA-seq and microarrays in Alzheimer's disease","authors":"Hyojin Kang, Junehawk Lee, S. Yu","doi":"10.1109/BIBM.2016.7822811","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822811","url":null,"abstract":"Differential Co-Expression Networks (DCENs) are graphical representations of genes showing differential co-expression pattern in response to experimental conditions or genetic changes. They have been successfully applied to identify condition-specific modules and provide a picture of the dynamic changes in gene regulatory networks. DCENs analysis investigates the differences among gene interconnections by calculating the expression correlation change of each gene pair between conditions. In this study, we collected many different datasets from NCBI GEO including 25 RNA-seq and 2,102 microarray samples derived from human brain and blood in Alzheimer's disease and performed differential co-expression analyses to identify functional modules responsible for the characterization of Alzheimer's disease. The DCENs were generated using Pearson correlation coefficient and meta-analysis was conducted using rank-based method. The preliminary results show that the structural characteristics of DCENs can provide new insights into the underlying gene regulatory dynamics in Alzheimer's disease. There is low size overlap between microarray- and RNA-seq-derived DCENs however, DCENs from RNA-seq would complement ones from microarray due to the higher coverage and dynamic range of RNA-seq.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131161465","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822685
Isaac Akogwu, Nan Wang, Chaoyang Zhang, Hwanseok Choi, H. Hong, P. Gong
Error correction is a critical initial step in next-generation sequencing (NGS) data analysis. Although more than 60 tools have been developed, there is no systematic evidence-based comparison with regard to their strength and weakness, especially in terms of correction accuracy. Here we report a full factorial simulation study to examine how NGS dataset characteristics (genome size, coverage depth and read length in particular) affect error correction performance (precision and F-score), as well as to compare performance sensitivity/resistance of six k-mer spectrum-based methods to variations in dataset characteristics. Multi-way ANOVA tests indicate that choice of correction method and dataset characteristics had significant effects on performance metrics. Overall, BFC, Bless, Bloocoo and Musket performed better than Lighter and Trowel on 27 synthetic datasets. For each chosen method, read length and coverage depth showed more pronounced impact on performance than genome size. This study shed insights to the performance behavior of error correction methods in response to the common variables one would encounter in real-world NGS datasets. It also warrants further studies of wet lab-generated experimental NGS data to validate findings obtained from this simulation study.
{"title":"Factorial analysis of error correction performance using simulated next-generation sequencing data","authors":"Isaac Akogwu, Nan Wang, Chaoyang Zhang, Hwanseok Choi, H. Hong, P. Gong","doi":"10.1109/BIBM.2016.7822685","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822685","url":null,"abstract":"Error correction is a critical initial step in next-generation sequencing (NGS) data analysis. Although more than 60 tools have been developed, there is no systematic evidence-based comparison with regard to their strength and weakness, especially in terms of correction accuracy. Here we report a full factorial simulation study to examine how NGS dataset characteristics (genome size, coverage depth and read length in particular) affect error correction performance (precision and F-score), as well as to compare performance sensitivity/resistance of six k-mer spectrum-based methods to variations in dataset characteristics. Multi-way ANOVA tests indicate that choice of correction method and dataset characteristics had significant effects on performance metrics. Overall, BFC, Bless, Bloocoo and Musket performed better than Lighter and Trowel on 27 synthetic datasets. For each chosen method, read length and coverage depth showed more pronounced impact on performance than genome size. This study shed insights to the performance behavior of error correction methods in response to the common variables one would encounter in real-world NGS datasets. It also warrants further studies of wet lab-generated experimental NGS data to validate findings obtained from this simulation study.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128689090","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822648
Borzou Alipourfard, Jean X. Gao, Olajide Babawale, Hanli Liu
Causality analysis of simultaneous measurements of the brain's electrical activity and its hemodynamic activity provides the opportunity to study the neural underpinning of hemodynamic fluctuations. This multimodal analysis can also be used to extract valuable information regarding the location of the generators of various electrical events such as Alpha rhythms or epileptiform activity. To best of our knowledge, we are the first propose a method to assess causality from EEG to the hemodynamic activity measured using functional near-infrared spectroscopy (fNIRs). The main challenge in studying causality within this setting arises from the low sampling rate of the fNIRs and the mixed frequency nature of the data. Our method of analysis consists of two parts. Through a simple modification of Geweke's formulation of contamination, we first show that the low sampling frequency of the fNIRs does not cause contamination in estimating causality from EEG to fNIRs. We then apply a novel causality test to avoid the down-sampling of the EEG when measuring for causality. The method of analysis proposed here can be generalized to study causality in other biomedical signal analysis applications and mixed frequency settings.
{"title":"Is EEG causal to fNIRs?","authors":"Borzou Alipourfard, Jean X. Gao, Olajide Babawale, Hanli Liu","doi":"10.1109/BIBM.2016.7822648","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822648","url":null,"abstract":"Causality analysis of simultaneous measurements of the brain's electrical activity and its hemodynamic activity provides the opportunity to study the neural underpinning of hemodynamic fluctuations. This multimodal analysis can also be used to extract valuable information regarding the location of the generators of various electrical events such as Alpha rhythms or epileptiform activity. To best of our knowledge, we are the first propose a method to assess causality from EEG to the hemodynamic activity measured using functional near-infrared spectroscopy (fNIRs). The main challenge in studying causality within this setting arises from the low sampling rate of the fNIRs and the mixed frequency nature of the data. Our method of analysis consists of two parts. Through a simple modification of Geweke's formulation of contamination, we first show that the low sampling frequency of the fNIRs does not cause contamination in estimating causality from EEG to fNIRs. We then apply a novel causality test to avoid the down-sampling of the EEG when measuring for causality. The method of analysis proposed here can be generalized to study causality in other biomedical signal analysis applications and mixed frequency settings.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114493302","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822549
Shaswati Roy, P. Maji
Indirect immunofluorescence (IIF) analysis is the most effective test for antinuclear autoantibodies (ANAs) analysis, in order to reveal the occurrence of some autoimmune diseases, such as connective tissue disorders. In the tests of antinuclear antibodies, the human epithelial type 2 (HEp-2) cells is mostly used as substrate. However, the recognition of the staining pattern of ANAs in the IIF image requires proper detection of the region of interest. In this regard, automatic segmentation of IIF images is an essential prerequisite as manual segmentation is labor intensive, time consuming, and subjective. Recently, rough-fuzzy clustering has been shown to provide significant results for image segmentation by handling different uncertainties present in the images. But, the existing robust rough-fuzzy clustering algorithm does not consider spatial distribution of the image. This is useful when the image is distorted by noise and other artifacts. In this regard, the paper proposes a segmentation algorithm by incorporating the spatial constraint with the advantages of robust rough-fuzzy clustering. In the current study, class label of a pixel is influenced by its neighboring pixels depending on their spatial distance. In this way, more number of neighboring pixels can be incorporated into the calculation of a pixel feature. The performance of the proposed method is evaluated on several HEp-2 cell images and compared with the existing algorithms by presenting both qualitative and quantitative results.
{"title":"A modified rough-fuzzy clustering algorithm with spatial information for HEp-2 cell image segmentation","authors":"Shaswati Roy, P. Maji","doi":"10.1109/BIBM.2016.7822549","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822549","url":null,"abstract":"Indirect immunofluorescence (IIF) analysis is the most effective test for antinuclear autoantibodies (ANAs) analysis, in order to reveal the occurrence of some autoimmune diseases, such as connective tissue disorders. In the tests of antinuclear antibodies, the human epithelial type 2 (HEp-2) cells is mostly used as substrate. However, the recognition of the staining pattern of ANAs in the IIF image requires proper detection of the region of interest. In this regard, automatic segmentation of IIF images is an essential prerequisite as manual segmentation is labor intensive, time consuming, and subjective. Recently, rough-fuzzy clustering has been shown to provide significant results for image segmentation by handling different uncertainties present in the images. But, the existing robust rough-fuzzy clustering algorithm does not consider spatial distribution of the image. This is useful when the image is distorted by noise and other artifacts. In this regard, the paper proposes a segmentation algorithm by incorporating the spatial constraint with the advantages of robust rough-fuzzy clustering. In the current study, class label of a pixel is influenced by its neighboring pixels depending on their spatial distance. In this way, more number of neighboring pixels can be incorporated into the calculation of a pixel feature. The performance of the proposed method is evaluated on several HEp-2 cell images and compared with the existing algorithms by presenting both qualitative and quantitative results.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121116899","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822709
Waldeyr M. C. Silva, Danilo Vilar, Daniel S. Souza, M. E. Walter, M. Brigido, M. Holanda
Terpenoids are involved in interactions as signaling for communication intra/inter species, signal molecules to attract pollinating insects, and defense against herbivores and microbes. Due to their chemical composition, many terpenoids possess vast pharmacological applicability in medicine and biotechnology, besides important roles in ecology, industry and commerce. The biosynthesis of terpenes has been widely studied over the years, and it is well known that they can be synthesized from two metabolic pathways: mevalonate pathway (MVA) and non-mevalonate pathway (MEP). On the other hand, genome-scale reconstruction of metabolic networks faces many challenges, including organizational data storage and data modeling, to properly represent the complexity of systems biology. Recent NoSQL database paradigms have introduced new concepts of scalable storage and data queries. Among them graph databases, which are versatile enough to cope with biological data. In this paper, we propose 2Path, a graph database model designed to represent terpenoid metabolic networks, with thousands of secondary metabolism reactions, such that it preserves important terpenoid biosynthesis characteristics.
{"title":"2Path: A terpenoid metabolic network modeled as graph database","authors":"Waldeyr M. C. Silva, Danilo Vilar, Daniel S. Souza, M. E. Walter, M. Brigido, M. Holanda","doi":"10.1109/BIBM.2016.7822709","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822709","url":null,"abstract":"Terpenoids are involved in interactions as signaling for communication intra/inter species, signal molecules to attract pollinating insects, and defense against herbivores and microbes. Due to their chemical composition, many terpenoids possess vast pharmacological applicability in medicine and biotechnology, besides important roles in ecology, industry and commerce. The biosynthesis of terpenes has been widely studied over the years, and it is well known that they can be synthesized from two metabolic pathways: mevalonate pathway (MVA) and non-mevalonate pathway (MEP). On the other hand, genome-scale reconstruction of metabolic networks faces many challenges, including organizational data storage and data modeling, to properly represent the complexity of systems biology. Recent NoSQL database paradigms have introduced new concepts of scalable storage and data queries. Among them graph databases, which are versatile enough to cope with biological data. In this paper, we propose 2Path, a graph database model designed to represent terpenoid metabolic networks, with thousands of secondary metabolism reactions, such that it preserves important terpenoid biosynthesis characteristics.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123752458","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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