2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第3页

A two-stage clustering technique for automatic biaxial gating of flow cytometry data 流式细胞术数据自动双轴门控的两阶段聚类技术

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359736

M. Pouyan, V. Jindal, J. Birjandtalab, M. Nourani

引用次数: 7

The Nonnegative Matrix Factorization and atomic deconvolution 非负矩阵分解与原子反褶积

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359923

Kazufumi Ito, A. K. Landi

引用次数: 0

Construction of signaling networks with incomplete RNAi data RNAi数据不完整的信号网络构建

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359674

Qiyao Wang, Yuanfang Ren, M. Hasan, A. Ay, Tamer Kahveci

引用次数: 0

On using Compressed Sensing and peak detection method for the Dynamic Instability parameters estimation for Microtubules modeled in three states 基于压缩感知和峰值检测方法的三态微管动态失稳参数估计

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359718

Shantia Yarahmadian, V. Menon, V. Rezania

引用次数: 6

Analysis of facial muscle activation in children with autism using 3D imaging 应用三维成像技术分析自闭症儿童面部肌肉活动

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359704

Manar D. Samad, Jonna Bobzien, J. Harrington, K. Iftekharuddin

{"title":"Analysis of facial muscle activation in children with autism using 3D imaging","authors":"Manar D. Samad, Jonna Bobzien, J. Harrington, K. Iftekharuddin","doi":"10.1109/BIBM.2015.7359704","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359704","url":null,"abstract":"Autism Spectrum Disorder (ASD) impairs an individual's non-verbal skills including natural and contextual facial expressions. Such impairments may manifest as odd facial expressions (facial oddity) based on subjective evaluations of facial images. A few studies conducted on individuals with ASD have focused on the physiology of facial muscle usage by employing eletrophysiological sensors in response to visual stimuli. The sensors are placed directly on the face and may inhibit or limit the spontaneous facial response which may be too subtle for subjective human evaluations. This study uses a non-intrusive 3D facial imaging sensor that captures detailed geometric information of the face to facilitate quantification and detection of subtle changes in facial expression based on the physiology of facial muscle. A novel computer vision and data mining approach is developed from curve-based geometric feature of 3D facial data to discern the changes in the facial muscle actions. A pilot study is conducted with sixteen subjects (8 subjects with ASD and 8 typically-developing controls) where 3D facial images have been captured in response to visual stimuli involving 3D facial expressions. Statistical analyses reveal a significantly asymmetric facial muscle action in subjects with ASD compared to the typically-developing controls. This study demonstrates feasibility of using non-intrusive facial imaging sensor data in evaluating possible physiology-based impairments.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127386278","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

Predictive and preventive models for diabetes prevention using clinical information in electronic health record 利用电子病历临床信息预防糖尿病的预测和预防模型

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359799

Ni Cao, Sisi Zeng, F. Shen, Chuandi Pan, Chengshui Chen, Thanh Nguyen, J. Chen

{"title":"Predictive and preventive models for diabetes prevention using clinical information in electronic health record","authors":"Ni Cao, Sisi Zeng, F. Shen, Chuandi Pan, Chengshui Chen, Thanh Nguyen, J. Chen","doi":"10.1109/BIBM.2015.7359799","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359799","url":null,"abstract":"In this work, we constructed diabetes predictive models using electronic health record data, which could potentially have better preventive power than other diabetes predictive models known according to our knowledge. Diabetes is one of the most common, costly and complicated diseases all over the world, including China. To tackle the complexity of diabetes, electronic health record has been widely used to support physicians in integrated care. However, diabetes predictive models using electronic health record may lack of preventive power when the clinical measurements directly related to diabetes diagnosis criteria are used. To overcome this limitation, we did not use glucose, insulin, C-peptide and HbA1C clinical measurements in classifying diabetes patients. We used decision-table and support vector machine algorithm to build predictive models. As the result, our decision-table-based model achieves accuracy of 0.879, AUC of 0.921, precision of 0.898 and recall of 0.904, which is comparable with any known definition to diabetes. Our support-vector-machine-based model achieves accuracy of 0.660, AUC of 0.584, precision of 0.652 and recall of 0.939. We also found 37 measurements significantly associated to diabetes, which are not directly related to diabetes diagnosis criteria.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114865709","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

Exhaustive analysis of dynamical properties of Biological Regulatory Networks with Answer Set Programming 用答案集规划详尽分析生物调节网络的动态特性

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359694

Emna Ben Abdallah, M. Folschette, O. Roux, M. Magnin

引用次数: 9

Audio signals encoding for cough classification using convolutional neural networks: A comparative study 基于卷积神经网络的咳嗽分类音频信号编码的比较研究

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359724

Hui-Hui Wang, Jia-Ming Liu, Mingyu You, Guozheng Li

引用次数: 26

Outlier detection in weight time series of connected scales 连通尺度权重时间序列的异常值检测

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359896

Saeed Mehrang, E. Helander, M. Pavel, A. Chieh, I. Korhonen

{"title":"Outlier detection in weight time series of connected scales","authors":"Saeed Mehrang, E. Helander, M. Pavel, A. Chieh, I. Korhonen","doi":"10.1109/BIBM.2015.7359896","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359896","url":null,"abstract":"In principle, connected sensors allow effortless long-term self-monitoring of health and wellness that can help maintain health and quality of life. However, data collected in the “wild” may be noisy and contain outliers, e.g., due to uncontrolled sources or data from different persons using the same device. The removal of the “outliers” is therefore critical for accurate interpretation of the data. In this paper we study the detection and elimination of outliers in self-weighing time series data obtained from connected weight scales. We examined three techniques: (1) a method based on autoregressive integrated moving average (ARIMA) time series modelling, (2) median absolute deviation (MAD) scale estimate, and (3) a method based on Rosner statistics. We applied these methods to both a data set with real outliers and a clean data set corrupted with simulated outliers. The results suggest that the simple MAD algorithm and ARIMA performed well with both test sets while the Rosner statistics was significantly less effective. In addition, the ARIMA approach appeared to be significantly less sensitive to long periods of missing data than MAD and Rosner statistics.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127293067","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: 23

Comparative utility of time and frequency HRV domains for automated classification of exercise exertion levels 时间和频率HRV域在运动强度自动分类中的比较效用

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359817

I. Jeong, J. Finkelstein

{"title":"Comparative utility of time and frequency HRV domains for automated classification of exercise exertion levels","authors":"I. Jeong, J. Finkelstein","doi":"10.1109/BIBM.2015.7359817","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359817","url":null,"abstract":"Exercise exertion results in activation of sympathetic nervous system. Heart rate variability (HRV) has been used to analyze activity of sympathetic nervous system (ANS). However, approaches to use HRV for exercise exertion analysis were not explored systematically. The main goal of this study was to develop classification algorithms to determine level of exercise exertion in real time and to compare potential of HRV time domain parameters versus HRV frequency domain parameters versus combined time and frequency parameter set. Discriminant analysis was used to identify optimal parameter sets and to develop algorithms for classification of exercise exertion levels. Time-domain HRV parameters demonstrated higher classification accuracy (95.6%) as compared to frequency-domain parameters (82.2%). Combing HRV parameters from time and frequency domains improves classification accuracy (97.8%). Our results suggested that HRV analysis can be used to automatically classify exercise exertion levels. Future studies should focus on more granular approach in identifying different stages of exercise process. Evaluation of classification algorithms should be based on larger sample of diverse representatives of different age, sex and health condition groups.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130121038","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