The increasing demand for wireless data transmission between wearable health care devices and central processing equipment has caused a consequential growth in the need for effective and reliable wireless body area network solution. The latest international standard, IEEE 802.15.6-2012 specifies the characteristics of the physical layer (PHY), multiple access control (MAC) layer, and security scheme. However, the system-level testbed gradually becomes a great challenge for the development of WBAN systems compared with other prevailing communication protocols such as Bluetooth and Zigbee. In this paper, we introduce a Software Defined Radio(SDR) based Testbed for WBAN. Benefiting from the rapid prototyping nature of the SDR platform, this testbed is shown to provide a solid testing environment for WBAN developers, as well as good testbed candidate for the future updated protocols.
{"title":"Software Defined Radio-Based Testbed for Wireless Body Area Network","authors":"Zhiyu Chen, Junchao Wang, Kaining Han, Z. Zilic","doi":"10.1109/BIBE.2018.00049","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00049","url":null,"abstract":"The increasing demand for wireless data transmission between wearable health care devices and central processing equipment has caused a consequential growth in the need for effective and reliable wireless body area network solution. The latest international standard, IEEE 802.15.6-2012 specifies the characteristics of the physical layer (PHY), multiple access control (MAC) layer, and security scheme. However, the system-level testbed gradually becomes a great challenge for the development of WBAN systems compared with other prevailing communication protocols such as Bluetooth and Zigbee. In this paper, we introduce a Software Defined Radio(SDR) based Testbed for WBAN. Benefiting from the rapid prototyping nature of the SDR platform, this testbed is shown to provide a solid testing environment for WBAN developers, as well as good testbed candidate for the future updated protocols.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127205104","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}
G. Nasser, A. F. El-Bab, H. Mohamed, A. Abouelsoud
Removing syringe pump from microfluidics droplet formation devices has many advantages such as using a small amount of biomedical sample directly from pipette to the inlet port of the channel, furthermore the system becomes smaller in size and lower in cost by abolishing the cost of the syringe pump. In this study, a quite simple and low-cost, power-free pumping method to form micro-droplets in a PMMA double T-Junction microfluidic channel is presented. The device was fabricated by direct write laser technique using PMMA substrate. By taking benefits of the suction pressure that comes from a single hand-operated suction syringe, the device generates droplet's volume about 2 Nano-liter consequently no complicated system is required. The low cost of the PMMA sheet and the fabrication process causes that one chip is about 30 cent. This makes it disposable as a single -use which is recommended in many medical analyses.
{"title":"[Regular Paper] Low Cost Micro-Droplet Formation Chip with a Hand-Operated Suction Syringe","authors":"G. Nasser, A. F. El-Bab, H. Mohamed, A. Abouelsoud","doi":"10.1109/BIBE.2018.00021","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00021","url":null,"abstract":"Removing syringe pump from microfluidics droplet formation devices has many advantages such as using a small amount of biomedical sample directly from pipette to the inlet port of the channel, furthermore the system becomes smaller in size and lower in cost by abolishing the cost of the syringe pump. In this study, a quite simple and low-cost, power-free pumping method to form micro-droplets in a PMMA double T-Junction microfluidic channel is presented. The device was fabricated by direct write laser technique using PMMA substrate. By taking benefits of the suction pressure that comes from a single hand-operated suction syringe, the device generates droplet's volume about 2 Nano-liter consequently no complicated system is required. The low cost of the PMMA sheet and the fabrication process causes that one chip is about 30 cent. This makes it disposable as a single -use which is recommended in many medical analyses.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126073876","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}
Yoshiaki Sota, S. Seno, Hironori Shigeta, N. Osato, M. Shimoda, S. Noguchi, H. Matsuda
Fusion genes make for one of the mechanisms of tumorigenesis. The identification of fusion genes by RNA-Seq has attracted attention. Various methods for detecting fusion genes have been proposed, but their accuracy is not sufficient. One of the causes of this problem is the relatively short reading length in RNA-Seq data. Therefore, before mapping RNA-Seq data, we proposed a method, which is based on shifted short-read clustering (SSC), to identify shifted reads of the same origin and extend them as representative sequences. As a result, we assumed that the percentage of uniquely mapped reads would be increased, and the detection rates of the fusion genes could be improved. To verify these hypotheses, we applied the SSC method to RNA-Seq data from three cell lines (BT-474, MCF-7, and SKBR-3). When only one base was shifted, the average read lengths of BT-474, MCF-7, and SKBR-3 were extended from 201 to 223 bases (111%), 201 to 214 bases (106%), and 201 to 213 bases (106%), respectively. Furthermore, the effectiveness of the SSC method is demonstrated by comparing the performances of a fusion gene detection tool's results, STAR-Fusion, with and without the SSC method of the reads. The percentage of uniquely mapped reads of BT-474, MCF-7, and SKBR-3 were improved from 88% to 93%, 88% to 94%, and 92% to 95%, respectively. Finally, the fusion gene detection rates of BT-474, MCF-7, and SKBR-3 were increased from 48% to 57%, 49% to 53%, and 50% to 53% respectively. The SSC method is considered to be an effective method not only for improving the percentage of uniquely mapped reads but also for fusion gene detection.
{"title":"Detection of Fusion Genes from Human Breast Cancer Cell-Line RNA-Seq Data Using Shifted Short Read Clustering","authors":"Yoshiaki Sota, S. Seno, Hironori Shigeta, N. Osato, M. Shimoda, S. Noguchi, H. Matsuda","doi":"10.1109/BIBE.2018.00038","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00038","url":null,"abstract":"Fusion genes make for one of the mechanisms of tumorigenesis. The identification of fusion genes by RNA-Seq has attracted attention. Various methods for detecting fusion genes have been proposed, but their accuracy is not sufficient. One of the causes of this problem is the relatively short reading length in RNA-Seq data. Therefore, before mapping RNA-Seq data, we proposed a method, which is based on shifted short-read clustering (SSC), to identify shifted reads of the same origin and extend them as representative sequences. As a result, we assumed that the percentage of uniquely mapped reads would be increased, and the detection rates of the fusion genes could be improved. To verify these hypotheses, we applied the SSC method to RNA-Seq data from three cell lines (BT-474, MCF-7, and SKBR-3). When only one base was shifted, the average read lengths of BT-474, MCF-7, and SKBR-3 were extended from 201 to 223 bases (111%), 201 to 214 bases (106%), and 201 to 213 bases (106%), respectively. Furthermore, the effectiveness of the SSC method is demonstrated by comparing the performances of a fusion gene detection tool's results, STAR-Fusion, with and without the SSC method of the reads. The percentage of uniquely mapped reads of BT-474, MCF-7, and SKBR-3 were improved from 88% to 93%, 88% to 94%, and 92% to 95%, respectively. Finally, the fusion gene detection rates of BT-474, MCF-7, and SKBR-3 were increased from 48% to 57%, 49% to 53%, and 50% to 53% respectively. The SSC method is considered to be an effective method not only for improving the percentage of uniquely mapped reads but also for fusion gene detection.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126142186","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}
This paper presents a novel graph-based methodology for development of structural and functional brain graphs. Using data from non-invasive neuroimaging modalities, such as MRI, fMRI and EEG, graphs that represent the brain architecture and functionality, are generated. Graph theory-based analysis has been applied with great success for studying the brain's connectivity, organization and dynamics. Towards this direction the presented Local Global (LG) graph methodology combines the local-regional information to compose a global topological representation.
{"title":"Brain Structural and Functional Representation Based on the Local Global Graph Methodology","authors":"Spyridon Manganas, N. Bourbakis, K. Michalopoulos","doi":"10.1109/BIBE.2018.00033","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00033","url":null,"abstract":"This paper presents a novel graph-based methodology for development of structural and functional brain graphs. Using data from non-invasive neuroimaging modalities, such as MRI, fMRI and EEG, graphs that represent the brain architecture and functionality, are generated. Graph theory-based analysis has been applied with great success for studying the brain's connectivity, organization and dynamics. Towards this direction the presented Local Global (LG) graph methodology combines the local-regional information to compose a global topological representation.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129781207","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}
Abnormal mRNA export causes many human diseases, including genetic diseases and tumorigenesis. However, the underlying mechanism was not well understood. In this study, we explored the roles of nuclear export factor2 (NXF2) in the development of hepatocellular carcinoma (HCC). We analyzed the expression level of NXF2 in HCC tissue and found down-regulation of NXF2 in HCC. Then we overexpressed NXF2 in HCC cell Huh-7 and found overexpression of NXF2 causes the down-regulation of migration and invasion in Huh-7 cells. Our results demonstrated loss-of-function of NXF2 in HCC clinical samples and the role of NXF2 in HCC tumorigenesis. Finally, we used Ingenuity Pathway Analysis (IPA) to evaluate the molecular interactions and pathway which were regulated by NXF2.
{"title":"The Role of mRNA Transporter in Human Cancer","authors":"Yu-Chia Chen, Chien-Chih Chiu, Han-Lin Chou, Jan-Gowth Chang","doi":"10.1109/BIBE.2018.00070","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00070","url":null,"abstract":"Abnormal mRNA export causes many human diseases, including genetic diseases and tumorigenesis. However, the underlying mechanism was not well understood. In this study, we explored the roles of nuclear export factor2 (NXF2) in the development of hepatocellular carcinoma (HCC). We analyzed the expression level of NXF2 in HCC tissue and found down-regulation of NXF2 in HCC. Then we overexpressed NXF2 in HCC cell Huh-7 and found overexpression of NXF2 causes the down-regulation of migration and invasion in Huh-7 cells. Our results demonstrated loss-of-function of NXF2 in HCC clinical samples and the role of NXF2 in HCC tumorigenesis. Finally, we used Ingenuity Pathway Analysis (IPA) to evaluate the molecular interactions and pathway which were regulated by NXF2.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124562616","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}
M. S. Khatun, M. Hasan, Md. Nurul Haque Mollah, H. Kurata
Zea mays (maize) is one of the most vital crops which are grown widely in the world. To understand the molecular structures and functions of maize, the identification of protein-protein interaction (PPI) is very important. PPI identification by wet lab experiments is time-consuming, expensive and laborious. These days in silico methods that accurately predict potential PPIs based on protein sequence information are highly demanded. Research on PPI prediction in maize is currently very limited, and no dedicated bioinformatics schemes are available. In this work, we proposed a novel approach, termed SIPMA (Systematic Identification of PPI in Maize using Autocorrelation). A machine learning random forest classifier was trained with autocorrelation features to build the prediction model. The SIPMA, which was tested by the experimentally verified PPI dataset of maize, yielded a prediction accuracy of 0.899 when the specificity was 0.969 on the training set. The SIPMA achieved promising performances on the test datasets. Compared with different sequence-based encoding and statistical learning methods, the SIPMA was a powerful computational resource for identifying PPIs in maize.
{"title":"SIPMA: A Systematic Identification of Protein-Protein Interactions in Zea mays Using Autocorrelation Features in a Machine-Learning Framework","authors":"M. S. Khatun, M. Hasan, Md. Nurul Haque Mollah, H. Kurata","doi":"10.1109/BIBE.2018.00030","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00030","url":null,"abstract":"Zea mays (maize) is one of the most vital crops which are grown widely in the world. To understand the molecular structures and functions of maize, the identification of protein-protein interaction (PPI) is very important. PPI identification by wet lab experiments is time-consuming, expensive and laborious. These days in silico methods that accurately predict potential PPIs based on protein sequence information are highly demanded. Research on PPI prediction in maize is currently very limited, and no dedicated bioinformatics schemes are available. In this work, we proposed a novel approach, termed SIPMA (Systematic Identification of PPI in Maize using Autocorrelation). A machine learning random forest classifier was trained with autocorrelation features to build the prediction model. The SIPMA, which was tested by the experimentally verified PPI dataset of maize, yielded a prediction accuracy of 0.899 when the specificity was 0.969 on the training set. The SIPMA achieved promising performances on the test datasets. Compared with different sequence-based encoding and statistical learning methods, the SIPMA was a powerful computational resource for identifying PPIs in maize.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124633011","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}
Electrophoresis (EP) test separates protein components based on their density. Patterns exhibited by this test mostly show very close approximation, making it difficult to examine test results within a short amount of time as it has many variations of patterns and requires a significant amount of knowledge to discern them accurately. To help clinical examiners save time and produce consistent results, a new deeplearning model optimized for EP graphic images was developed. Extending recent work on capsule network, which is a stateof- the-art deep learning model, this study was carried out to develop a best-performing model in classifying abnormal and normal electrophoresis patterns. Instead of extracting features from the image, we used the whole slide image as an input to the classifier. This study used 39,484 electrophoresis 2D graph images and utilized capsule network as the foundation of the deep learning architecture to learn the images without data augmentation. The formulated models were compared for a multitude of performance metrics including accuracy, sensitivity, and specificity. Overall, the study results show that our proposed architecture EP-CapsNet, which combines capsule network with Google’s inception module, is the best performing model, outperforming the baseline and alternative models in almost all comparisons.
{"title":"[Regular Paper] EP-CapsNet: Extending Capsule Network with Inception Module for Electrophoresis Binary Classification","authors":"Elizabeth Tobing, A. Murtaza, Keejun Han, M. Yi","doi":"10.1109/BIBE.2018.00071","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00071","url":null,"abstract":"Electrophoresis (EP) test separates protein components based on their density. Patterns exhibited by this test mostly show very close approximation, making it difficult to examine test results within a short amount of time as it has many variations of patterns and requires a significant amount of knowledge to discern them accurately. To help clinical examiners save time and produce consistent results, a new deeplearning model optimized for EP graphic images was developed. Extending recent work on capsule network, which is a stateof- the-art deep learning model, this study was carried out to develop a best-performing model in classifying abnormal and normal electrophoresis patterns. Instead of extracting features from the image, we used the whole slide image as an input to the classifier. This study used 39,484 electrophoresis 2D graph images and utilized capsule network as the foundation of the deep learning architecture to learn the images without data augmentation. The formulated models were compared for a multitude of performance metrics including accuracy, sensitivity, and specificity. Overall, the study results show that our proposed architecture EP-CapsNet, which combines capsule network with Google’s inception module, is the best performing model, outperforming the baseline and alternative models in almost all comparisons.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126424738","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}
This article presents a continuous time lowpass sigma-delta ADC for wide field of view (FOV) image sensor with a chain of integrators and with capacitive feedforward summation (CICFF) plus circuit which is an ideal function for implementation in low power applications. The summation of feedforward signals is achieved by capacitors plus, without the essential thing of any extra active components which can be used in virtual reality (VR) and augmented reality (AR) camera. The quantizer uses a comparator which can achieve high linearity easily. The chip was implemented in 1.8 V supply voltage which works as a part of CMOS active-pixel image sensor with global shutter and high dynamic range (HDR) operation. The proposed idea can support medical training.
{"title":"Sigma-Delta ADC for Image Sensor in Virtual and Augmented Reality Camera to Medical Training","authors":"W. Lai","doi":"10.1109/BIBE.2018.00080","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00080","url":null,"abstract":"This article presents a continuous time lowpass sigma-delta ADC for wide field of view (FOV) image sensor with a chain of integrators and with capacitive feedforward summation (CICFF) plus circuit which is an ideal function for implementation in low power applications. The summation of feedforward signals is achieved by capacitors plus, without the essential thing of any extra active components which can be used in virtual reality (VR) and augmented reality (AR) camera. The quantizer uses a comparator which can achieve high linearity easily. The chip was implemented in 1.8 V supply voltage which works as a part of CMOS active-pixel image sensor with global shutter and high dynamic range (HDR) operation. The proposed idea can support medical training.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128808715","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}
Yasmeen Naz Panhwar, F. Naghdy, D. Stirling, G. Naghdy, J. Potter
Assessing the frailty of older people quantitatively is critical to prevent potential accidents and to ensure their well-being. The older people with high frailty score are at the risk of fall, which increases the rate of hospitalization and reducing the number of independent activities carried out. The conventional clinical tools used for frailty assessment are subjective, qualitative and are prone to human error. The balance assessment, activity of daily living (ADL) and gait analysis are practiced as clinical and quantitative tools for risk of fall and frailty assessments. An objective approach to classify the frailty levels using ADL is proposed. The "pick up an object from floor" as an ADL is deployed to differentiate the signal patterns obtained through inertial measurement unit (IMU) for frail and non-frail subjects. The data from single inertial unit mounted on pelvis is analyzed. The experimental work is carried out on three groups of healthy/control, frail and non-frail subjects. The various signal attributes are used to classify the frailty quantitatively using IMU data and machine learning methods. The results demonstrate that frail subjects have clear irregularities in their signal trajectories. Using the proposed algorithm two classes of frailty (non-frail and frail) are identified objectively. The study demonstrates the potential of deploying IMU for advanced classification of frailty levels in older people.
{"title":"Quantitative Frailty Assessment Using Activity of Daily Living (ADL)","authors":"Yasmeen Naz Panhwar, F. Naghdy, D. Stirling, G. Naghdy, J. Potter","doi":"10.1109/BIBE.2018.00059","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00059","url":null,"abstract":"Assessing the frailty of older people quantitatively is critical to prevent potential accidents and to ensure their well-being. The older people with high frailty score are at the risk of fall, which increases the rate of hospitalization and reducing the number of independent activities carried out. The conventional clinical tools used for frailty assessment are subjective, qualitative and are prone to human error. The balance assessment, activity of daily living (ADL) and gait analysis are practiced as clinical and quantitative tools for risk of fall and frailty assessments. An objective approach to classify the frailty levels using ADL is proposed. The \"pick up an object from floor\" as an ADL is deployed to differentiate the signal patterns obtained through inertial measurement unit (IMU) for frail and non-frail subjects. The data from single inertial unit mounted on pelvis is analyzed. The experimental work is carried out on three groups of healthy/control, frail and non-frail subjects. The various signal attributes are used to classify the frailty quantitatively using IMU data and machine learning methods. The results demonstrate that frail subjects have clear irregularities in their signal trajectories. Using the proposed algorithm two classes of frailty (non-frail and frail) are identified objectively. The study demonstrates the potential of deploying IMU for advanced classification of frailty levels in older people.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116897743","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}
In this paper, we address the problem of finding sequence motifs in substrate proteins specific to E3 ubiquitin ligases (E3s). We formulated a posterior probability distribution of sites by designing a likelihood function based on amino acid indexing and a prior distribution based on the disorderness of protein sequences. These designs are derived from known characteristics of E3 binding sites in substrate proteins. Then, we devise a collapsed Gibbs sampling algorithm for the posterior probability distribution called DegSampler. We performed computational experiments using 36 sets of substrate proteins specific to E3s and compared the performance of DegSampler with those of popular motif finders, MEME and GLAM2. The results showed that DegSampler was superior to the others in finding E3 binding motifs. Thus, DegSampler is a promising tool for finding E3 motifs in substrate proteins.
在本文中，我们解决了在E3泛素连接酶(E3)特异性底物蛋白中寻找序列基序的问题。我们通过设计基于氨基酸索引的似然函数和基于蛋白质序列无序度的先验分布，建立了位点的后验概率分布。这些设计来源于底物蛋白中E3结合位点的已知特征。然后，我们设计了一种称为DegSampler的后验概率分布的折叠吉布斯抽样算法。我们使用36组E3s特异性底物蛋白进行了计算实验，并将DegSampler与流行的motif finder MEME和GLAM2的性能进行了比较。结果表明，DegSampler在寻找E3结合基序方面优于其他方法。因此，DegSampler是在底物蛋白中寻找E3基序的一个很有前途的工具。
{"title":"[Regular Paper] DegSampler: Collapsed Gibbs Sampler for Detecting E3 Binding Sites","authors":"O. Maruyama, Fumiko Matsuzaki","doi":"10.1109/BIBE.2018.00009","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00009","url":null,"abstract":"In this paper, we address the problem of finding sequence motifs in substrate proteins specific to E3 ubiquitin ligases (E3s). We formulated a posterior probability distribution of sites by designing a likelihood function based on amino acid indexing and a prior distribution based on the disorderness of protein sequences. These designs are derived from known characteristics of E3 binding sites in substrate proteins. Then, we devise a collapsed Gibbs sampling algorithm for the posterior probability distribution called DegSampler. We performed computational experiments using 36 sets of substrate proteins specific to E3s and compared the performance of DegSampler with those of popular motif finders, MEME and GLAM2. The results showed that DegSampler was superior to the others in finding E3 binding motifs. Thus, DegSampler is a promising tool for finding E3 motifs in substrate proteins.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130742699","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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