Pub Date : 2019-03-01DOI: 10.1109/ICBCB.2019.8854634
Yang Bai, Guang-hong Han, Jin-hai Yu
The aim of the study was to investigate the effectiveness of ultrasound in the evaluation of percutaneous balloon angioplasty (PTA) for the treatment of autogenous arteriovenous fistula (AVF) stenosis. 40 patients with AVF stenosis participated in the study who were treated with regular hemodialysis in the First Hospital of Jilin University. Among the many indicators, we selected radial blood flow, radial artery resistance index, and anastomotic diameter as monitoring indicators. The results of preoperative, immediate postoperative, postoperative 1 day, 3 days, 7 days, and 14 days were used to find the trend of the indicator and determine the best monitoring time point. Finally, we found morphological indicators and hemodynamic parameters changed significantly after operation; no obvious statistical difference between 1 day postoperative and other postoperative monitoring time points were founded. So, ultrasonography has unique advantages in hemodynamics and morphological examination. It can evaluate the functional status of AVF and the efficacy of PTA accurately, and we believe that the first day after surgery is the best time to monitor.
{"title":"Ultrasound Evaluation of Balloon Angioplasty for the Treatment of Autogenous Arteriovenous Fistula Anastomotic Stenosis: Ultrasound evaluation of PTA for AVF anastomotic stenosis","authors":"Yang Bai, Guang-hong Han, Jin-hai Yu","doi":"10.1109/ICBCB.2019.8854634","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854634","url":null,"abstract":"The aim of the study was to investigate the effectiveness of ultrasound in the evaluation of percutaneous balloon angioplasty (PTA) for the treatment of autogenous arteriovenous fistula (AVF) stenosis. 40 patients with AVF stenosis participated in the study who were treated with regular hemodialysis in the First Hospital of Jilin University. Among the many indicators, we selected radial blood flow, radial artery resistance index, and anastomotic diameter as monitoring indicators. The results of preoperative, immediate postoperative, postoperative 1 day, 3 days, 7 days, and 14 days were used to find the trend of the indicator and determine the best monitoring time point. Finally, we found morphological indicators and hemodynamic parameters changed significantly after operation; no obvious statistical difference between 1 day postoperative and other postoperative monitoring time points were founded. So, ultrasonography has unique advantages in hemodynamics and morphological examination. It can evaluate the functional status of AVF and the efficacy of PTA accurately, and we believe that the first day after surgery is the best time to monitor.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131155640","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854637
Jianhang Zhou, Qi Zhang, Bob Zhang
Diseases like hyperuricemia and hysteromyoma along with prediabetes (a serious health condition) are causing more suffering and hardship than ever before. Recently, computerized non-invasive diagnostic methods inspired by Traditional Chinese Medicine (TCM) have proved to be reasonable and effective using the face and/or tongue to perform disease detection. These methods no longer require bodily fluids to be extracted (e.g., a blood test), which further relieves the pain of patients and allows doctors to focus on more labor intensive activities. In this paper, we propose a novel classifier based on the fusion of the linear discriminant analysis (LDA) and the sparse representation based classifier (SRC) named L-SRC, to perform disease detection. Specifically, we collect facial images using a non-invasive capture device from those suffering from hyperuricemia, hysteromyoma and prediabetes, and feed it to the L-SRC classifier to perform classification. The experimental results show that L-SRC can discriminate samples belonging to the three classes with healthy control more effectively, achieving accuracies of 72%, 70.95% and 76.60% respectively. This indicates a promising application prospect in the future.
{"title":"Applying L-SRC for Non-invasive Disease Detection Using Facial Chromaticity and Texture Features","authors":"Jianhang Zhou, Qi Zhang, Bob Zhang","doi":"10.1109/ICBCB.2019.8854637","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854637","url":null,"abstract":"Diseases like hyperuricemia and hysteromyoma along with prediabetes (a serious health condition) are causing more suffering and hardship than ever before. Recently, computerized non-invasive diagnostic methods inspired by Traditional Chinese Medicine (TCM) have proved to be reasonable and effective using the face and/or tongue to perform disease detection. These methods no longer require bodily fluids to be extracted (e.g., a blood test), which further relieves the pain of patients and allows doctors to focus on more labor intensive activities. In this paper, we propose a novel classifier based on the fusion of the linear discriminant analysis (LDA) and the sparse representation based classifier (SRC) named L-SRC, to perform disease detection. Specifically, we collect facial images using a non-invasive capture device from those suffering from hyperuricemia, hysteromyoma and prediabetes, and feed it to the L-SRC classifier to perform classification. The experimental results show that L-SRC can discriminate samples belonging to the three classes with healthy control more effectively, achieving accuracies of 72%, 70.95% and 76.60% respectively. This indicates a promising application prospect in the future.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122017321","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854645
Clayton Barham, Mingyu Cha, X. Li, Haiyan Hu
MicroRNAs (miRNA) are ~22 base pair long RNAs that play important roles in regulating gene expression. Understanding the transcriptional regulation of miRNA is critical to gene regulation. However, it is often difficult to precisely identify miRNA transcription start sites (TSSs) due to miRNA-specific biogenesis. Existing computational methods cannot effectively predict miRNA TSSs. Here, we employed deep learning architectures incorporating Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to detect miRNA TSSs in regions of accessible chromatin. By testing on benchmark experimental data, we demonstrated that deep learning models outperform support vector machine and can accurately distinguish miRNA TSSs from both flanking regions and intergenic regions.
{"title":"Application of Deep Learning Models to MicroRNA Transcription Start Site Identification","authors":"Clayton Barham, Mingyu Cha, X. Li, Haiyan Hu","doi":"10.1109/ICBCB.2019.8854645","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854645","url":null,"abstract":"MicroRNAs (miRNA) are ~22 base pair long RNAs that play important roles in regulating gene expression. Understanding the transcriptional regulation of miRNA is critical to gene regulation. However, it is often difficult to precisely identify miRNA transcription start sites (TSSs) due to miRNA-specific biogenesis. Existing computational methods cannot effectively predict miRNA TSSs. Here, we employed deep learning architectures incorporating Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to detect miRNA TSSs in regions of accessible chromatin. By testing on benchmark experimental data, we demonstrated that deep learning models outperform support vector machine and can accurately distinguish miRNA TSSs from both flanking regions and intergenic regions.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125051388","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854653
Qian Liu, Anna Junker, K. Murakami, P. Hu
A stacked deep convolutional neural network (DCNN) model was generated to predict cell density maps and count cells. We treated the cell counting as a regression problem with a preprocessing step to generate cell density maps. We implemented this approach by integrating two trustworthy and state-of-art model architectures (U-net & VGG19). This method combines the advantages from both traditional segmentation-based and density-based methods. It overcomes the limitations such as cell clumping, overlapping, and it can also bypass the fine-tuning step which was necessary for previous density-based methods when applying to different datasets. A publicly available well-labeled dataset was used to train and test the model. An unlabeled real dataset which generated in-house was used to evaluate the performance.
{"title":"A Novel Convolutional Regression Network for Cell Counting","authors":"Qian Liu, Anna Junker, K. Murakami, P. Hu","doi":"10.1109/ICBCB.2019.8854653","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854653","url":null,"abstract":"A stacked deep convolutional neural network (DCNN) model was generated to predict cell density maps and count cells. We treated the cell counting as a regression problem with a preprocessing step to generate cell density maps. We implemented this approach by integrating two trustworthy and state-of-art model architectures (U-net & VGG19). This method combines the advantages from both traditional segmentation-based and density-based methods. It overcomes the limitations such as cell clumping, overlapping, and it can also bypass the fine-tuning step which was necessary for previous density-based methods when applying to different datasets. A publicly available well-labeled dataset was used to train and test the model. An unlabeled real dataset which generated in-house was used to evaluate the performance.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132290280","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854635
Zhiqiang Zhao, Yan Zhang, Qiaoli Zheng
Aiming at the problem of localization of eye centers in complex scenes, a method for quickly locating eye center is proposed in this paper. For the collected face images, this paper firstly uses bilateral filtering algorithm to remove the possible noise, and performs histogram equalization operation on the gray image to increase the dynamic range of the image grayscale and improve its distinguishability. Then, constructing cascaded strong classifier based on improved Ada Boost algorithm, and proposed three-layer eye detection. Finally, the method of canny operator edge detection and improved Hough circle detection is used to obtain the pupil center. The experimental results show that the algorithm can acquire the coordinates of the eye center quickly and accurately, and it is robust to eye location under illumination changes.
{"title":"Fast Localization Algorithm of Eye Centers Based on Improved Hough Transform","authors":"Zhiqiang Zhao, Yan Zhang, Qiaoli Zheng","doi":"10.1109/ICBCB.2019.8854635","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854635","url":null,"abstract":"Aiming at the problem of localization of eye centers in complex scenes, a method for quickly locating eye center is proposed in this paper. For the collected face images, this paper firstly uses bilateral filtering algorithm to remove the possible noise, and performs histogram equalization operation on the gray image to increase the dynamic range of the image grayscale and improve its distinguishability. Then, constructing cascaded strong classifier based on improved Ada Boost algorithm, and proposed three-layer eye detection. Finally, the method of canny operator edge detection and improved Hough circle detection is used to obtain the pupil center. The experimental results show that the algorithm can acquire the coordinates of the eye center quickly and accurately, and it is robust to eye location under illumination changes.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134273783","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854667
Zhiwei Wu, Kai Qiao, Lijie Zhang, Jinjin Hai, Ningning Liang, Linyuan Wang, Bin Yan
Nephropathy is a worldwide clinical and health problem that is getting more and more attention from the public. The gold standard for the diagnosis of nephropathy is still renal puncture biopsy, which is an invasive examination and has many contraindications. We proposed to analyze renal ultrasound images using deep learning method to achieve noninvasive assessment. However, the kidney ultrasound images with accurate pathological diagnosis are relatively difficult to collect, which belongs to the category of few-shot learning. To mitigate the impact of few data on performance, this paper proposed a conceptually simple, flexible, and mixed framework for aided diagnosis of nephropathy. Our method, called the PASnet, consists of pretrained network and siamese network. Pretrained network trained by abundant samples from ImageNet can achieve fast convergence and better performance on a new data set. Siamese network learns to converge or disperse image pairs in distance space according to whether it comes from the same class or not. PASnet combines the advantages of these two methods and obtains a better classification performance on nephropathy classification through joint training. Accuracy of PASnet increases by 5.89% compared to a single network.
{"title":"PASnet: A Joint Convolutional Neural Network for Noninvasive Renal Ultrasound Pathology Assessment","authors":"Zhiwei Wu, Kai Qiao, Lijie Zhang, Jinjin Hai, Ningning Liang, Linyuan Wang, Bin Yan","doi":"10.1109/ICBCB.2019.8854667","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854667","url":null,"abstract":"Nephropathy is a worldwide clinical and health problem that is getting more and more attention from the public. The gold standard for the diagnosis of nephropathy is still renal puncture biopsy, which is an invasive examination and has many contraindications. We proposed to analyze renal ultrasound images using deep learning method to achieve noninvasive assessment. However, the kidney ultrasound images with accurate pathological diagnosis are relatively difficult to collect, which belongs to the category of few-shot learning. To mitigate the impact of few data on performance, this paper proposed a conceptually simple, flexible, and mixed framework for aided diagnosis of nephropathy. Our method, called the PASnet, consists of pretrained network and siamese network. Pretrained network trained by abundant samples from ImageNet can achieve fast convergence and better performance on a new data set. Siamese network learns to converge or disperse image pairs in distance space according to whether it comes from the same class or not. PASnet combines the advantages of these two methods and obtains a better classification performance on nephropathy classification through joint training. Accuracy of PASnet increases by 5.89% compared to a single network.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133204693","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854630
Yang Bai, Guang-hong Han, Ying Sun
The aim of the study was to investigate the role of ultrasound in the diagnosis of acute rejection after allogeneic renal transplantation. Thirty-two renal transplant patients with acute rejection were enrolled in the rejection group, and 32 kidney transplant patients with no acute rejection matched with ageing, gender, and weight were selected to form a non-rejection group for comparative study. Finally, we found Renal volume, cortical echo, resistance index (RI), and end-diastolic velocity (EDV) were significantly different between groups (P<0.05). There was a significant difference in cortical thickness, resistance index, and perfusion flow between the patients with acute rejection and those with no significant improvement after symptomatic treatment (P<0.05). So, Color Doppler ultrasound has a high accuracy in the diagnosis of acute rejection after allogeneic renal transplantation, especially for the evaluation of the effect of acute rejection therapy.
{"title":"Color Doppler Ultrasound in the Diagnosis of Acute Rejection after Allogeneic Renal Transplantation","authors":"Yang Bai, Guang-hong Han, Ying Sun","doi":"10.1109/ICBCB.2019.8854630","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854630","url":null,"abstract":"The aim of the study was to investigate the role of ultrasound in the diagnosis of acute rejection after allogeneic renal transplantation. Thirty-two renal transplant patients with acute rejection were enrolled in the rejection group, and 32 kidney transplant patients with no acute rejection matched with ageing, gender, and weight were selected to form a non-rejection group for comparative study. Finally, we found Renal volume, cortical echo, resistance index (RI), and end-diastolic velocity (EDV) were significantly different between groups (P<0.05). There was a significant difference in cortical thickness, resistance index, and perfusion flow between the patients with acute rejection and those with no significant improvement after symptomatic treatment (P<0.05). So, Color Doppler ultrasound has a high accuracy in the diagnosis of acute rejection after allogeneic renal transplantation, especially for the evaluation of the effect of acute rejection therapy.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114281282","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854643
Xianghao Zhan, Xiaoqing Guan, Rumeng Wu, Zhan Wang, You Wang, Guang Li
As pharmacists attach great significance to geographical origins of herbal medicines, cheap, nondestructive and convenient methods for discriminating herbal medicines originated from diverse regions are much in need. This work proposes a method of using electronic nose to discriminate herbal medicines from different origins. With 5 categories of herbal medicines and 3 to 4 geographical origins for each category, 8 pattern recognition algorithms prove the feasibility of the classification task and SVM, LDA and BP neural network have shown better classification accuracy. Additionally, feature engineering approaches are used to facilitate classification, showing that normalization based on each feature and each sensor and centralization prove to be better normalization approaches for classifiers; a proper degree of noise addition help classifiers get better generalization ability; finally, feature selection with SNR could lead to more efficient classifiers by selecting the most meaningful features and disregarding unnecessary features. This work provides insights for future herbal medicine evaluation based on electronic nose with better combinations of pattern recognition algorithms and feature engineering approaches for optimal classification performances.
{"title":"Feature Engineering in Discrimination of Herbal Medicines from Different Geographical Origins with Electronic Nose","authors":"Xianghao Zhan, Xiaoqing Guan, Rumeng Wu, Zhan Wang, You Wang, Guang Li","doi":"10.1109/ICBCB.2019.8854643","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854643","url":null,"abstract":"As pharmacists attach great significance to geographical origins of herbal medicines, cheap, nondestructive and convenient methods for discriminating herbal medicines originated from diverse regions are much in need. This work proposes a method of using electronic nose to discriminate herbal medicines from different origins. With 5 categories of herbal medicines and 3 to 4 geographical origins for each category, 8 pattern recognition algorithms prove the feasibility of the classification task and SVM, LDA and BP neural network have shown better classification accuracy. Additionally, feature engineering approaches are used to facilitate classification, showing that normalization based on each feature and each sensor and centralization prove to be better normalization approaches for classifiers; a proper degree of noise addition help classifiers get better generalization ability; finally, feature selection with SNR could lead to more efficient classifiers by selecting the most meaningful features and disregarding unnecessary features. This work provides insights for future herbal medicine evaluation based on electronic nose with better combinations of pattern recognition algorithms and feature engineering approaches for optimal classification performances.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127977040","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}
The field of stem cells and regenerative medicine is one of the most attractive foci and research hot spots in the current biology and medicine. The international scientific community has made remarkable breakthroughs in the following issues: 1) The basic regulatory theory of stem cells; 2) iPS cells, targeted reprogramming to functional cells and new types of stem cells; 3) Gene edit technologies; 4) Tissue engineering and translational research, drug development using stem cells, nano-materials research and application in regenerative medicine etc. Scientists in institutes and the biology pharmaceutical industry are actively promoting the clinical translation by discovering new mechanisms, innovating technologies and creating new therapies, leading to a big scale market of the regenerative medicine nowadays. Traditional treatments such as drug therapy and surgery often have little effect on such diseases, and fail to meet the growing medical needs of this age-group. Stem cell-based regenerative medicine is expected to become the third treatment option after drug therapy and surgery. With the increasing financial supports and investments in China recent years, a series of important progress have been made to stem cells and regenerative medicine. In this issue, we investigated the stem cell and regenerative medicine industry in the world and china, such as clinical trial and research institutions distribution.
{"title":"An Investigation and Analysis of Clinical Trials and Research Centers on Regenerative Medicine Industry: Case comparison between China and other countries","authors":"Hongshen Pang, Ling Wei, Xiao-Chu Qin, Hong-Ming Hou, Haiyun Xu","doi":"10.1109/ICBCB.2019.8854658","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854658","url":null,"abstract":"The field of stem cells and regenerative medicine is one of the most attractive foci and research hot spots in the current biology and medicine. The international scientific community has made remarkable breakthroughs in the following issues: 1) The basic regulatory theory of stem cells; 2) iPS cells, targeted reprogramming to functional cells and new types of stem cells; 3) Gene edit technologies; 4) Tissue engineering and translational research, drug development using stem cells, nano-materials research and application in regenerative medicine etc. Scientists in institutes and the biology pharmaceutical industry are actively promoting the clinical translation by discovering new mechanisms, innovating technologies and creating new therapies, leading to a big scale market of the regenerative medicine nowadays. Traditional treatments such as drug therapy and surgery often have little effect on such diseases, and fail to meet the growing medical needs of this age-group. Stem cell-based regenerative medicine is expected to become the third treatment option after drug therapy and surgery. With the increasing financial supports and investments in China recent years, a series of important progress have been made to stem cells and regenerative medicine. In this issue, we investigated the stem cell and regenerative medicine industry in the world and china, such as clinical trial and research institutions distribution.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114983277","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854655
Ling-yun Liu, Hongguang Yang, Bin Cheng
Epitope prediction plays an important role in production of antibodies and disease treatment. There are mainly two research methods, namely experimental method and calculation method. Experimental method can obtain more accurate experimental results, but it takes a long time and the cost of manpower, material resources are relatively high. So it is not convenient to obtain experimental results more quickly. Calculation method mostly uses computer and machine learning methods for prediction. Calculation method improves prediction speed to some extent, but the result is not satisfactory. In order to further improve the accuracy of epitope prediction, this paper proposes a novel method of processing epitope characteristics. In this paper, we choose six properties to study. The six main physicochemical properties are converted into corresponding digital vectors, resulting in high-dimensional features. Then we use Principal Component Analysis (PCA) method to process them. Finally, dimensionality reduction features are used as input of Recurrent Neural Network (RNN) for epitope prediction, and good prediction results are obtained. PCA method reduces feature dimensions and facilitates the processing of features. At the same time, the prediction results obtained with dimensionality reduction features show that dimensionality reduction reduces dimensions, but it retains the main components of original features and improves the rate of successful prediction.
{"title":"Prediction of Linear B-cell Epitopes Based on PCA and RNN Network","authors":"Ling-yun Liu, Hongguang Yang, Bin Cheng","doi":"10.1109/ICBCB.2019.8854655","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854655","url":null,"abstract":"Epitope prediction plays an important role in production of antibodies and disease treatment. There are mainly two research methods, namely experimental method and calculation method. Experimental method can obtain more accurate experimental results, but it takes a long time and the cost of manpower, material resources are relatively high. So it is not convenient to obtain experimental results more quickly. Calculation method mostly uses computer and machine learning methods for prediction. Calculation method improves prediction speed to some extent, but the result is not satisfactory. In order to further improve the accuracy of epitope prediction, this paper proposes a novel method of processing epitope characteristics. In this paper, we choose six properties to study. The six main physicochemical properties are converted into corresponding digital vectors, resulting in high-dimensional features. Then we use Principal Component Analysis (PCA) method to process them. Finally, dimensionality reduction features are used as input of Recurrent Neural Network (RNN) for epitope prediction, and good prediction results are obtained. PCA method reduces feature dimensions and facilitates the processing of features. At the same time, the prediction results obtained with dimensionality reduction features show that dimensionality reduction reduces dimensions, but it retains the main components of original features and improves the rate of successful prediction.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130486922","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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