2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)最新文献_第3页

False-Positive Reduction of Pulmonary Nodule Detection Based on Deformable Convolutional Neural Networks 基于可变形卷积神经网络的肺结节检测假阳性降低

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459209

Yu Haiying, Fan Zhongwei, Dong Ding, Sun Zengyang

引用次数: 7

Study on Pathological Mechanism of Pneumonia Infected by Coronavirus Based on Time-Series Gene Co-expression Network Analysis 基于时间序列基因共表达网络分析的冠状病毒感染肺炎病理机制研究

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459223

Xingcheng Yi, Yan Zhang, Tong Xu, Xiao-yun Su, Cong Fu

引用次数: 1

Short-term Impact of Polarity Therapy on Physiological Signals in Chronic Anxiety Patients 极性疗法对慢性焦虑患者生理信号的短期影响

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459217

Comas Joaquim, Decky Aspandi, M. Ballester, Francesc Carreas, Lluís Ballester, Xavier Binefa

{"title":"Short-term Impact of Polarity Therapy on Physiological Signals in Chronic Anxiety Patients","authors":"Comas Joaquim, Decky Aspandi, M. Ballester, Francesc Carreas, Lluís Ballester, Xavier Binefa","doi":"10.1109/ICBCB52223.2021.9459217","DOIUrl":"https://doi.org/10.1109/ICBCB52223.2021.9459217","url":null,"abstract":"Increasing interest in complementary therapies prompts analysis of the objective impact on human physiology. Polarity Therapy (PT) is a branch of complementary medicine that relates to energy field therapies. Although previous clinical work has provided evidence of the impact on patients, the present work analyzes, for the first time, a short-term systematic investigation of such therapy. Several physiological signals were collected from 25 consecutive chronic anxiety patients seen in an outpatient clinic before and after PT, which included electrocardiographic analysis (ECG), galvanic skin response (GSR), blood volume pulse (BVP), and temperature. Also included was the analysis of facial expressions using state of the art deep learning-based models for automatic valence and arousal estimations. Using the recorded samples, we proceeded to calculate heart rate variability (HRV) analysis in a temporal, frequency and non-linear domain, which proves assessment of the autonomous nervous system. Fine analysis of the ECG was developed using wavelet-based techniques. The area under the T wave, recently described to correlate with electromagnetic ventricular activity, was also calculated. A psychological questionnaire assessed the clinical therapeutic impact before and after the session. Results revealed a positive impact of 30-minute therapy on blood pressure and heart rate reduction, direct evidence of down-regulation of sympathetic activity and up-regulation of parasympathetic activity, an inverse correlation of T-wave and BVP, which suggests improved cardiac coherence, and changes in facial expression which correlated with subjective perceptions of wellbeing. The combination of physiological variables, facial expression and self-assessment of wellbeing before and after a PT session revealed parallelism between the observed changes in physiological data and subjective feelings.","PeriodicalId":178168,"journal":{"name":"2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121445173","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

Achieving Optimal Tradeoff Adaptation Functionality for the Minimal Gene Regulatory Network 实现最小基因调控网络的最优权衡适应功能

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459221

Xiaona Huang, Jiaqi Li, Zhirong Zhang

引用次数: 0

Predicting the Stage of Non-small Cell Lung Cancer with Divergence Neural Network Using Pre-treatment Computed Tomography 应用发散神经网络预测非小细胞肺癌的分期

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459218

Choi， Jieun, Cho Hwan-ho, Park Hyunjin

{"title":"Predicting the Stage of Non-small Cell Lung Cancer with Divergence Neural Network Using Pre-treatment Computed Tomography","authors":"Choi， Jieun, Cho Hwan-ho, Park Hyunjin","doi":"10.1109/ICBCB52223.2021.9459218","DOIUrl":"https://doi.org/10.1109/ICBCB52223.2021.9459218","url":null,"abstract":"Determining the stage of non-small cell lung cancer (NSCLC) is important for treatment and prognosis. Staging includes a professional interpretation of imaging, thus we aimed to build an automatic process with deep learning (DL). We proposed an end-to-end DL method that uses pre-treatment computer tomography images to classify the early- and advanced-stage of NSCLC. DL models were developed and tested to classify the early- and advanced-stage using training (n = 58), validation (n = 7), and testing (n = 17) cohorts obtained from public domains. The network consists of three parts of encoder, decoder, and classification layer. Encoder and decoder layers are trained to reconstruct original images. Classification layers are trained to classify early- and advanced-stage NSCLC patients with a dense layer. Other machine learning-based approaches were compared. Our model achieved accuracy of 0.8824, sensitivity of 1.0, specificity of 0.6, and area under the curve (AUC) of 0.7333 compared with other approaches (AUC 0.5500 ─ 0.7167) in the test cohort for classifying between early- and advanced-stages. Our DL model to classify NSCLC patients into early-stage and advanced-stage showed promising results and could be useful in future NSCLC research.","PeriodicalId":178168,"journal":{"name":"2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123472708","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

Investigating Spatio-Temporal Cellular Interactions in Embryonic Morphogenesis by 4D Nucleus Tracking and Systematic Comparative Analysis — Taking Nematodes C. Elegans and C. Briggsae as Examples 利用4D核跟踪和系统比较分析研究胚胎形态发生中细胞间的时空相互作用——以秀丽线虫和布里格线虫为例

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459206

Guoye Guan, Wong Ming-Kin, Chang Lu-yan, Sze Ho Vincy Wing, An Xiaomeng, Zhao Zhongying, Chao Tang

{"title":"Investigating Spatio-Temporal Cellular Interactions in Embryonic Morphogenesis by 4D Nucleus Tracking and Systematic Comparative Analysis — Taking Nematodes C. Elegans and C. Briggsae as Examples","authors":"Guoye Guan, Wong Ming-Kin, Chang Lu-yan, Sze Ho Vincy Wing, An Xiaomeng, Zhao Zhongying, Chao Tang","doi":"10.1109/ICBCB52223.2021.9459206","DOIUrl":"https://doi.org/10.1109/ICBCB52223.2021.9459206","url":null,"abstract":"Metazoan embryonic morphogenesis is involved with spatio-temporal interactions between cells during embryogenesis from zygote to larva. These regulatory interactions (e.g. active and passive cell motion driven by cytomechanics) contribute to precise, robust and stereotypic embryo patterns among individuals of a species. To in-depth decipher the underlying mechanism and biological function of such interactions, in this work, we used two closely related species Caenorhabditis elegans and Caenorhabditis briggsae as examples and provided a general framework for system-level comparative analysis. We cultured and imaged 11 wild-type embryos in vivo using 3-dimensional time-lapse confocal microscope for each species, with following automatic nucleus-based cell tracking. We quantitatively constructed their normalized and comparable 4D cell-position atlas in silico, including information like each cell's division timing and migration trajectory during embryogenesis from 4- to 350-cell stage. With highly similar cell lineage in both C. elegans and C. briggsae, we compared their division-timing program and cell-arrangement pattern globally and locally, which revealed a turning point of regulation on positional variation among individuals, within one species as well as between two species. Moreover, this down regulation could rescue some cellular positional variation caused by division-order chaos between C. elegans and C. briggsae. Last but not least, the asynchrony of division between sister cells were found to be functional for local positioning of the newborn cells. Our information-rich dataset and the computational analytic methods could facilitate related research in developmental biology, evolutionary biology, and comparative biology.","PeriodicalId":178168,"journal":{"name":"2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128499656","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

Construction and Simulation Analysis of 3D Model Based on Bladder 基于膀胱的三维模型构建与仿真分析

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459224

Jiewen Deng, P. Ran, Y. Mao, Jingwen Wang

{"title":"Construction and Simulation Analysis of 3D Model Based on Bladder","authors":"Jiewen Deng, P. Ran, Y. Mao, Jingwen Wang","doi":"10.1109/ICBCB52223.2021.9459224","DOIUrl":"https://doi.org/10.1109/ICBCB52223.2021.9459224","url":null,"abstract":"For people with urinary incontinence who cannot control bladder urination freely, the principle of electrical impedance imaging was used to simulate the bladder state to select the electrode array method and excitation method suitable for bladder filling detection. In this paper, a three-dimensional human abdominal model including pelvis and bladder was constructed. The bladder filling degree is simulated by setting different bladder radii separately, and the electrodes are arranged on the surface of the model. The model designed in this paper is a common hierarchical array model and a rectangular array model based on bladder position design. The excitation, current of 5 mA is used to simulate the relative, interphase and adjacent excitation of two different electrode array models. Through the analysis of parameter voltage sensitivity and dynamic range, the evaluation of two-electrode array mode and three-electrode excitation was finally realized. The experimental results show that the rectangular array method and interphase excitation have more application value in bladder filling detection, which can more effectively obtain the voltage change caused by bladder volume change, which provides a basis for the design of bladder filling detection system.","PeriodicalId":178168,"journal":{"name":"2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130648926","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

iPRIns: A Tool with the Improved Precision and Recall for Insertion Detection in the Human Genome iPRIns:一种提高人类基因组插入检测精度和召回率的工具

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459220

Sakkayaphab Piwluang, D. Wichadakul

引用次数: 0

Efficient Clustering of Massive scRNA-seq Data Using a Modified PQk-Means Algorithm 基于改进PQk-Means算法的海量scRNA-seq数据高效聚类

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459216

Weinan Liu, Renyi Liu

引用次数: 0

A Drug-Target Interaction Prediction Based on GCN Learning 基于GCN学习的药物-靶标相互作用预测

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459231

Xiaodan Wang, Jihong Wang, Z. Wang

{"title":"A Drug-Target Interaction Prediction Based on GCN Learning","authors":"Xiaodan Wang, Jihong Wang, Z. Wang","doi":"10.1109/ICBCB52223.2021.9459231","DOIUrl":"https://doi.org/10.1109/ICBCB52223.2021.9459231","url":null,"abstract":"In recent years, the use of deep learning methods for drug-target interaction (DTI) prediction has become the mainstream research direction. Drugs, targets, and other related biological and chemical properties have constructed a very complex network structure. How to effectively extract network features and predict target has become a big challenge. Graph Convolutional Neural Network (GCN) is one of the effective deep learning methods for complex networks. It extends the convolution operation from traditional European space to non-Euclidean space, and can simultaneously perform end-to-end node attribute information and structural information. End-to-end learning, its core idea is to learn a function mapping, through which nodes in the mapping graph can aggregate their own features and its neighbor features to generate a new representation of the node. In this study, we introduce the GCN link prediction method decagon for DTI prediction research. The experimental data comes from the DTI-net model. By combining the drug-drug interaction relationship matrix, the target-target interaction relationship matrix and the drug-target interaction relationship matrix provided by DTI-net, the drug characteristics and target characteristics are expressed as the attribute characteristics of the network nodes, thereby obtaining DTI Heterogeneous Network. In order to improve the ability to predict the drug-target relationship, this article has done a lot of tuning experiments in parameter selection and optimization strategies, and analyzed and compared the prediction results. The best predicted AUC is 0.919, and the best AUPR is 0.922. In terms of traditional drug-target prediction methods, the GCN method can effectively extract the features contained in heterogeneous networks, which proves the feasibility of this method in predicting drug-target interactions.","PeriodicalId":178168,"journal":{"name":"2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126052061","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: 5