2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献_第3页

Identification of chloroplast and sub-chloroplast proteins from sequence-attributed features using support vector machine and domain information 基于支持向量机和域信息的叶绿体和亚叶绿体蛋白质序列特征识别

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562787

Ravindra Kumar, Anjali Garg, B. Kumari, Aakriti Jain, Manish Kumar, Equal Contribution

{"title":"Identification of chloroplast and sub-chloroplast proteins from sequence-attributed features using support vector machine and domain information","authors":"Ravindra Kumar, Anjali Garg, B. Kumari, Aakriti Jain, Manish Kumar, Equal Contribution","doi":"10.1109/CIBCB49929.2021.9562787","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562787","url":null,"abstract":"Chloroplasts are one of the most important organelles of plants including algae. A number of vital biological pathways in them are exclusively confined to the chloroplast, thus, indicating the significance of chloroplastidic proteins. Hence, prediction of chloroplastidic proteins and their localization within the chloroplast (sub-chloroplastidial localization) can be of paramount importance in understanding the role of both novel and known chloroplastidic proteins. Several experimental methods have been developed to determine the subcellular localization of proteins; however, annotation of every protein using the experimental methods requires a lot of time and resources. To overcome these shortcomings of experimental approaches many computational methods have been proposed which minimize the amount of time and resources required. In pursuit of speeding up the prediction of chloroplastidic proteins and their sub-chloroplastidial localization besides maintaining efficiency, we developed Pfam domain and support vector machine based two level prediction frameworks, namely, SubChloroPred. At first level, SubChloroPred predicts the chloroplastidic proteins and at second level, their localization at sub-chloroplastidic locations such as thylakoid and stroma would be predicted. SubChloroPred has overall prediction accuracy of 94.86% at the first level and accuracies of 75.91% and 74.26% at the second level in thylakoid and stroma respectively. We have also developed a freely accessible webserver as well as standalone software for the use of scientific community, which can be accessed from the link http://proteininformatics.org/mkumar/SubChloroPred.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134546809","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}

引用次数: 0

Classification of Human Emotions using EEG-based Causal Connectivity Patterns 基于脑电图因果连接模式的人类情绪分类

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562837

J. S. Ramakrishna, N. Sinha, Hariharan Ramasangu

{"title":"Classification of Human Emotions using EEG-based Causal Connectivity Patterns","authors":"J. S. Ramakrishna, N. Sinha, Hariharan Ramasangu","doi":"10.1109/CIBCB49929.2021.9562837","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562837","url":null,"abstract":"Electroencephalography (EEG) signals, recorded from different channels, are used to study human brain activity in the context of emotion recognition and seizure detection. Most of the existing emotion recognition methods have focused on EEG characteristics at an electrode level and not on connectivity patterns. Causal connectivity refers to the understanding of the causal relationship between the channels. In this work, we have developed an emotion recognition model using EEG-based causal connectivity patterns. Granger causality is used to find the causal relationship of the EEG signals from different channels. The quantification of causal configurations between the channels is carried out using Transfer Entropy. The obtained Transfer Entropy values are used as features for the classification of emotions. The performance of the proposed method is validated using a publicly available SEED-IV dataset. The proposed technique achieves an average subject-specific classification accuracy of 90 % (using 18 channel signals). The proposed method achieves an improvement of 1 % over state-of-the-art techniques based on correlation using 62 channel signals and an improvement of 17 % compared to methods that use only 18 channel signals.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129654841","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}

引用次数: 3

Automatic Detection of Necrotizing Fasciitis: A Dataset and Early Results 自动检测坏死性筋膜炎:一个数据集和早期结果

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562936

Anik Das, Sumaiya Amin, J. Hughes

{"title":"Automatic Detection of Necrotizing Fasciitis: A Dataset and Early Results","authors":"Anik Das, Sumaiya Amin, J. Hughes","doi":"10.1109/CIBCB49929.2021.9562936","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562936","url":null,"abstract":"Necrotizing Fasciitis (NF), or Necrotizing Soft-Tissue Infection (NSTI), is a rare infection that poses a significant threat to health. In the absence of a proper diagnosis, the infection can spread rapidly causing extensive tissue necrosis and death - mortality rate of 20% - 35%. Due to inadequate resources, little progress has been made for the automatic detection of NF. We have prepared a novel dataset containing images of affected human organs by NF using an internet image search. The dataset contains 693 images in total, containing raw, augmented, and non-NF images. A system has been developed for performing automated detection of NF with an Artificial Neural Network. We have evaluated the YOLOv3 object recognition model for five arrangements of our dataset and compared the performance for these different data arrangements after running each five times. The datasets were split into 80% train data and 20% test data, and for performance measures, we have taken into account the evaluation metrics: Intersection over Union (IoU) and Average Precision (AP). We obtained the highest average AP score of 57.97% for the dataset with raw data and augmentation and the highest average IoU score of 61.94% for dataset with raw data, augmentation, and negative images. The initial finding of this work can be further improved and become a substantial contribution to clinical arrangements for the diagnosis and management of NF.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"5 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123531039","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}

引用次数: 0

Multi-distance based spectral embedding fusion for clustering single-cell methylation data 基于多距离谱嵌入融合的单细胞甲基化数据聚类

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562895

Qi Tian, Jianxiao Zou, Jianxiong Tang, Shicai Fan

{"title":"Multi-distance based spectral embedding fusion for clustering single-cell methylation data","authors":"Qi Tian, Jianxiao Zou, Jianxiong Tang, Shicai Fan","doi":"10.1109/CIBCB49929.2021.9562895","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562895","url":null,"abstract":"Advances in high throughput sequencing have enabled DNA methylation profiling at single-cell resolution. The generation of single-cell methylation sequencing (scM-Seq) data provides unprecedented opportunities for a comprehensive dissection of epigenetic heterogeneity. An important step of exploring epigenetic heterogeneity is clustering cells according to their single-cell methylation profiles. However, the inherent sparsity and stochastic measurement characteristic of the data make it challenging. To this end, we introduce SINCEF, using spectral embedding fusion to reconstruct cell-to-cell pairwise distance for clustering single-cell methylation data. SIN CEF first calculates multiple basic distance matrices to capture cell-to-cell methylation dissimilarity relationships according to the global methylation status. Then it adopts spectral embedding to transform these basic distance matrices into the latent representations, pooling information from the basic distance measures. Finally, it reconstructs a novel distance matrix and implements hierarchical clustering to yield cell partitions. Assessments on several public scM-Seq datasets demonstrated that SINCEF could generate a more appropriate distance matrix to measure the methylation distance between cells, which considerably improved the clustering performance. As an additional benefit, the reconstructed novel distance matrix could help to visually assess the heterogeneity across cell populations through presenting the block structures in the hierarchical clustering heat maps. SINCEF is freely available on GitHub at https://github.com/TQBio/SINCEF.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115300585","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}

引用次数: 1

A deep learning model to predict traumatic brain injury severity and outcome from MR images 从磁共振图像预测创伤性脑损伤严重程度和结果的深度学习模型

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562848

Dacosta Yeboah, Hung-Cuong Nguyen, D. Hier, G. Olbricht, Tayo Obafemi-Ajayi

{"title":"A deep learning model to predict traumatic brain injury severity and outcome from MR images","authors":"Dacosta Yeboah, Hung-Cuong Nguyen, D. Hier, G. Olbricht, Tayo Obafemi-Ajayi","doi":"10.1109/CIBCB49929.2021.9562848","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562848","url":null,"abstract":"For many neurological disorders, including traumatic brain injury (TBI), neuroimaging information plays a crucial role determining diagnosis and prognosis. TBI is a heterogeneous disorder that can result in lasting physical, emotional and cognitive impairments. Magnetic Resonance Imaging (MRI) is a non-invasive technique that uses radio waves to reveal fine details of brain anatomy and pathology. Although MRIs are interpreted by radiologists, advances are being made in the use of deep learning for MRI interpretation. This work evaluates a deep learning model based on a residual learning convolutional neural network that predicts TBI severity from MR images. The model achieved a high sensitivity and specificity on the test sample of subjects with varying levels of TBI severity. Six outcome measures were available on TBI subjects at 6 and 12 months. Group comparisons of outcomes between subjects correctly classified by the model with subjects misclassified suggested that the neural network may be able to identify latent predictive information from the MR images not incorporated in the ground truth labels. The residual learning model shows promise in the classification of MR images from subjects with TBI.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128433690","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}

引用次数: 1

An Oscillator Model for Interbrain Synchrony: Slow Interactional Rhythms Entrain Fast Neural Activity 脑间同步的振荡器模型:缓慢的相互作用节奏引发快速的神经活动

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562779

Chen Lam Loh, T. Froese

{"title":"An Oscillator Model for Interbrain Synchrony: Slow Interactional Rhythms Entrain Fast Neural Activity","authors":"Chen Lam Loh, T. Froese","doi":"10.1109/CIBCB49929.2021.9562779","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562779","url":null,"abstract":"Synchronization is a self-organizing process spanning multiple levels of organization. With advances in brain imaging and hyperscanning technologies, the pervasiveness of interbrain neural synchrony in widely different experimental settings is slowly attracting attention in the field. Despite its prevalence, the underlying mechanisms for interbrain neural synchrony remains largely unexplained, with recent interpretations attempting to approach the problem from the theory of mind, mentalization and shared intentionality perspective. One highlighted issue regarding the difficulty in providing a sound explanation is that neural activities are occurring at orders-of-magnitude faster timescales than the social interaction itself. Using two minimal evolutionary models based on Kuramoto coupled oscillators, we provide an alternative perspective as to how interbrain neural synchronization might occur. Borrowing concepts from recent hypotheses, we argue that 1) embodied engagements form a new autonomous “interaction” system, which 2) through its slower dynamics exert constraints on the faster neural dynamics within the heads. If this is true, the key to understanding interbrain neural synchrony could be through understanding the nature of the interaction itself.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123968264","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}

引用次数: 2

Predicting Drug-Drug Interactions Using Meta-path Based Similarities 使用基于元路径的相似性预测药物-药物相互作用

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562802

Farhan Tanvir, Muhammad Ifte Khairul Islam, Esra Akbas

{"title":"Predicting Drug-Drug Interactions Using Meta-path Based Similarities","authors":"Farhan Tanvir, Muhammad Ifte Khairul Islam, Esra Akbas","doi":"10.1109/CIBCB49929.2021.9562802","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562802","url":null,"abstract":"Drug-drug interaction (DDI) indicates the event where a particular drug's desired course of action is modified when taken together with other drugs (s). DDIs may hamper, enhance, or reduce the expected effect of either drug or, at the worst possible scenario, cause an adverse side effect. While it is crucial to identify drug-drug interactions, it is quite impossible to detect all possible DDIs for a new drug during the clinical trial. Therefore, many computational methods are proposed for this task. In this paper, we propose a novel method, HIN-DDI for discovering DDIs. This method considers drugs and other biomedical entities like proteins, pathways, and side effects, for DDI prediction. We design a heterogeneous information network (HIN) to model relations between these entities. Afterward, we extract the rich semantic relationships among these entities using different meta-path-based topological features. An extensive set of features are fed to different classifiers for DDI prediction. Moreover, we run extensive experiments to compare and evaluate the effectiveness of HIN-DD I with other methods. Results exhibit that HIN-DDI is quite effective in predicting new drugs as well as existing drugs. Unlike existing works, HIN-DDI can predict new drugs, and more importantly, it can impressively outmatch baseline methods by up to 63%.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123340374","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}

引用次数: 6

Using Evolved Neural Networks to Elucidate Nef Features Associated with HIV-1 Subtype Differentiation 利用进化神经网络阐明与HIV-1亚型分化相关的Nef特征

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562798

E. Liu, G. Fogel, D. Nolan, S. Lamers, M. McGrath

{"title":"Using Evolved Neural Networks to Elucidate Nef Features Associated with HIV-1 Subtype Differentiation","authors":"E. Liu, G. Fogel, D. Nolan, S. Lamers, M. McGrath","doi":"10.1109/CIBCB49929.2021.9562798","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562798","url":null,"abstract":"The genetically diverse HIV-1 Group M infecting subtypes can be observed as unique branches on a phylogenetic tree and arose due to independent cross-species transmissions between non-human primates and humans. As the HIV-1 pandemic has evolved, different infecting subtypes have prevailed in different geographic populations. The complex factors associated with the global establishment of specific subtypes remains largely unknown. The HIV-1 accessory protein Nef, demonstrates considerable genetic variability and several studies suggest that Nef variation is associated with disease progression. Here we use an evolved neural network approach applied to a well-curated database of HIV-1 Nef sequences from subtypes A1, C, and D, the most prominent subtypes in Uganda, Africa to elucidate functional properties associated with subtype diversity. Following the generation of over 1000 features associated with amino acids physicochemical properties, we use statistical pruning and evolved neural networks to identify key Nef features associated with subtype differentiation. As interest in Nef continues to grow in the research community, we hope that these features foster new understanding of the mechanisms associated with the spread of HIV -1 subtypes in populations.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122832583","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}

引用次数: 2

Modelling The Fitness Landscapes of a SCRaMbLEd Yeast Genome 酵母基因组的适应性景观建模

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562918

Bill Yang, Goksel Misirli, A. Wipat, J. Hallinan

引用次数: 2

Leveraging Neural Networks in Malaria Control 利用神经网络控制疟疾

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562789

Joseph Livesey, D. Wojtczak

引用次数: 0