2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)最新文献_第8页

Extracting Modifiable Risk Factors from Narrative Preventive Healthcare Guidelines for EHR Integration 从叙述预防性医疗保健指南中提取可修改的风险因素，用于EHR整合

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.000-2

Setu Shah, Xiao Luo

{"title":"Extracting Modifiable Risk Factors from Narrative Preventive Healthcare Guidelines for EHR Integration","authors":"Setu Shah, Xiao Luo","doi":"10.1109/BIBE.2017.000-2","DOIUrl":"https://doi.org/10.1109/BIBE.2017.000-2","url":null,"abstract":"General criteria of preventive healthcare based on the preventive care guidelines have been integrated with Electronic Health Record (EHR) systems through decision support systems and led to improved performance in healthcare delivery. Advanced integration which considers factors such as ethnicity, social history, medical history, family history need to be investigated. Integrating the preventive healthcare guidelines with the EHR based on above factors requires the extraction of the relevant information from these guidelines using text mining and natural language processing techniques. In this research, we propose a framework to extract information according to the EHR modules. Our results show that the proposed framework successfully extracts terms and concepts, and adequately maps them to the proposed data interchange structure that is based on the EHR functional modules. The extracted information and the populated data interchange structures eases the integration of the modifiable risk factors with the patient's records in the EHR. The proposed framework can be extended to other clinical healthcare guidelines where modifiable risk factors are critical.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125118624","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

Finite Element Analysis of Femoral Implant Under Static Load 静载荷作用下股骨假体的有限元分析

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00012

Aleksandra Vulovic, T. Šušteršič, N. Filipovic

引用次数: 3

Directed Hungary Greedy Algorithm for Biomolecular Networks Alignment 生物分子网络对齐的定向匈牙利贪婪算法

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-18

Jiang Xie, Jiaxin Li, Dongfang Lu, Jiao Wang, Wu Zhang

{"title":"Directed Hungary Greedy Algorithm for Biomolecular Networks Alignment","authors":"Jiang Xie, Jiaxin Li, Dongfang Lu, Jiao Wang, Wu Zhang","doi":"10.1109/BIBE.2017.00-18","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-18","url":null,"abstract":"There are many algorithms for conducting alignments between undirected biomolecular networks (UBNs), including protein-protein interaction networks (PINs). However, none of them are specialized for directed biomolecular networks (DBNs), such as gene regulatory networks (GRNs) and metabolic networks (MNs). It is challenging and meaningful to achieve optimal mapping in DBN alignment. In this article, we propose a new algorithm, referred to as Directed Hungary Greedy Algorithm (DHGA), for the alignment of DBNs. DHGA focuses on a directed graph and catches information on the direction of edges. Furthermore, both the homology of biomolecules and the similarities of the network topologies are taken into consideration. In DHGA, expert knowledge can be brought in to pre-match biomolecules. We verified the effectiveness and robustness of DHGA using simulation datasets. Our experiments demonstrate that the performance of DHGA is clearly improved when expert knowledge is introduced. Moreover, we conducted DHGA on two metabolic pathway maps from KEGG and identified 21 pairs of similar cell cycle regulatory relationships between human and yeast, 12 of which were supported by references indicating that the paired relationships have the same function.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123636634","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

A Novel Gaussian Discriminant Analysis-based Computer Aided Diagnosis System for Screening Different Stages of Alzheimer's Disease 基于高斯判别分析的阿尔茨海默病不同阶段筛查计算机辅助诊断系统

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-41

Chen Fang, Chunfei Li, M. Cabrerizo, A. Barreto, J. Andrian, D. Loewenstein, R. Duara, M. Adjouadi

{"title":"A Novel Gaussian Discriminant Analysis-based Computer Aided Diagnosis System for Screening Different Stages of Alzheimer's Disease","authors":"Chen Fang, Chunfei Li, M. Cabrerizo, A. Barreto, J. Andrian, D. Loewenstein, R. Duara, M. Adjouadi","doi":"10.1109/BIBE.2017.00-41","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-41","url":null,"abstract":"This study introduces a novel Gaussian discriminant analysis (GDA)-based computer aided diagnosis (CAD) system using structural magnetic resonance imaging (MRI) data uniquely as input for screening different stages of Alzheimers disease (AD) involving its prodromal stage of mild cognitive impairment (MCI) in relation to the cognitive normal control group (CN). Taking advantage of multiple modalities of biomarkers, over the past few years, several machine learning-based CAD approaches have been proposed to address this high-dimensional classification problem. This study presents a novel GDA-based CAD system on the basis of a tenfold cross validation and a held-out test data set. Subjects considered in this study included 187 CN, 301 MCI, and 131 AD subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. In the tenfold cross validation, the proposed system achieved an average F1 score of 97.20%, accuracy of 96.00%, sensitivity of 99.14%, and specificity of 88.67% for discriminating together the MCI and AD groups from the CN group; and an average F1 score of 79.82%, accuracy of 87.43%, sensitivity of 79.09%, and specificity of 91.25% for discriminating AD from MCI. By testing on the held-out test data, for discriminating MCI and AD from CN, an accuracy of 93.28%, a sensitivity of 98.78%, and a specificity of 81.08% were obtained. These results also show that by separating left and right hemispheres of the brain into two decisional spaces, and then combining their outputs, the GDA-based CAD system demonstrates a high potential for clinical application.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127930871","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: 11

A Simulation Study on Light Scattering Effect on Water-borne Bacteriophage Virus Using Mie Analysis 用Mie分析法模拟光散射对水传播噬菌体病毒的影响

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-45

Tamanna Motahar, Rummana Rahman, Rafiya Hossain

引用次数: 0

Streamlining the Genomics Processing Pipeline via Named Pipes and Persistent Spark Satasets 通过命名管道和持久Spark数据集简化基因组学处理管道

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-82

W. Blair, L. Joao, Larry Davis, Paul E. Anderson

引用次数: 1

Towards Automated Distortion and Health Correlation for Age-Related Macular Degeneration 年龄相关性黄斑变性的自动变形与健康相关性研究

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.000-7

A. D. Chakravarthy, M. Subramaniam, P. Chundi, Muhammad Hassan, Q. D. Nguyen

引用次数: 1

Simulation of Dual Modality Probe 双模态探针的仿真

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-26

Mahmut Unan, A. E. Sonmez

引用次数: 0

Modeling and Experimental Validation of Large Scale Fluorescence Sensor Networks 大规模荧光传感器网络的建模与实验验证

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-52

Vishwa Nellore, C. Dwyer

{"title":"Modeling and Experimental Validation of Large Scale Fluorescence Sensor Networks","authors":"Vishwa Nellore, C. Dwyer","doi":"10.1109/BIBE.2017.00-52","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-52","url":null,"abstract":"Fluorescence microscopy is by far the dominant assay used to measure molecular scale interactions in a wide range of disciplines including biochemistry, biophysics, bioengineering, biomedical imaging and clinical diagnostics. However, the technique can probe only a small number of molecular interactions with previous attempts at detecting more than 11 fluorophores simultaneously resulting in barcodes that are too big for in vivo analysis, expensive and involve time-consuming detection schemes. Here, we create DNA self-assembled Resonance Energy Transfer networks that generate a unique time-resolved fluorescence signature when probed by a series of light pulses. An experimentally informed theoretical model predicts that networks containing up to 125 fluorophores may be distinguished from other extremely similar networks. Through the largest experimental survey of RET networks, we demonstrate that minor changes made to the RET network result in a unique, experimentally resolvable optical signature. We show that we can generate over 300 unique signatures using only 3 fluorophores. Furthermore, from 1296 time-resolved fluorescence signatures, we show that the optical signatures are reproducible 99.48% of the time. The ability to simultaneously detect multiple biological entities, the high spatial information density and the high repeatability of the synthetic RET networks will potentially find use in many biological and clinical applications.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133889125","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

Convolutional Neural Network for Combined Classification of Fluorescent Biomarkers and Expert Annotations using White Light Images 基于卷积神经网络的荧光生物标记物分类与白光图像专家注释

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-37

Gregory Yauney, Keith Angelino, David Edlund, Pratik Shah

{"title":"Convolutional Neural Network for Combined Classification of Fluorescent Biomarkers and Expert Annotations using White Light Images","authors":"Gregory Yauney, Keith Angelino, David Edlund, Pratik Shah","doi":"10.1109/BIBE.2017.00-37","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-37","url":null,"abstract":"Fluorescent biomarkers are important indicators of disease, but imaging them can require specialized and often-expensive devices. Periodontal and dental diseases resulting from microbial plaque biofilms, if diagnosed early with biomarker images and expert knowledge, can be treated to prevent occurrences of serious systemic illnesses. We report two convolutional neural network classifiers trained with dentist annotations of disease signatures and fluorescent porphyrin biomarker images to identify dental plaque in white light images as a per-pixel binary classification task. The classifiers were trained and tested with millions of image patches from two datasets collected from 27 consenting adults using handheld intraoral cameras. The areas under the receiver operating characteristic curves for the test sets were calculated to be 0.7694 and 0.8720. Once trained, the classifiers predict the location of plaque in white light images without requiring specialized biomarker imaging devices or expert intervention. This generalized approach can be useful in other domains where diagnostic biomarker predicting can augment expert knowledge using standard white light images.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124253913","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: 15