2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献_第4页

A multi-stage approach to detect gene-gene interactions associated with multiple correlated phenotypes 一种多阶段的方法来检测与多种相关表型相关的基因相互作用

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058563

Xiangdong Zhou, Keith C. C. Chan, Danhong Zhu

{"title":"A multi-stage approach to detect gene-gene interactions associated with multiple correlated phenotypes","authors":"Xiangdong Zhou, Keith C. C. Chan, Danhong Zhu","doi":"10.1109/CIBCB.2017.8058563","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058563","url":null,"abstract":"Multiple correlated phenotypes often appear in complex traits or complex diseases. These correlated phenotypes are useful in identifying gene-gene interactions associated with complex traits or complex disease more effectively. Some approaches have been proposed to use correlation among multiple phenotypes to identify gene-gene interactions that are common to multiple phenotypes. However these approaches either didn't find truly gene-gene interactions or got results which are hard to explain, especially by using all correlated phenotypes to identify gene-gene interactions, they made identified interactions unreliable. In this paper, we propose Multivariate Quantitative trait based Ordinal MDR (MQOMDR) algorithm to effectively identify gene-gene interactions associated with multiple correlated phenotypes by selecting the best classifier according to not only the training accuracy of the phenotype under consideration but also other phenotypes with weights determined mainly by their pair correlation with the phenotype under consideration and also by repeated selection process to make use of truly useful correlated phenotypes. Experimental results on two real datasets show that our algorithm has better performance in identifying gene-gene interactions associated with multiple correlated phenotypes.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125392482","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

Evaluation of the salmon algorithm 鲑鱼算法的评价

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058568

John Orth, S. Houghten, Lindsey Tulloch

引用次数: 0

Biclustering of gene expression microarray data using dynamic deme parallelized genetic algorithm (DdPGA) 基于动态deme并行遗传算法的基因表达微阵列数据双聚类研究

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058524

Shreya Mishra, Swati Vipsita

{"title":"Biclustering of gene expression microarray data using dynamic deme parallelized genetic algorithm (DdPGA)","authors":"Shreya Mishra, Swati Vipsita","doi":"10.1109/CIBCB.2017.8058524","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058524","url":null,"abstract":"Biclustering deals with creating a sub-matrix that shows a high similarity across both genes and conditions. Biclustering targets at identifying several biclusters that reveal potential local patterns from a microarray matrix. In this paper, initially sequential evolutionary algorithm (SEBI) is implemented and few drawbacks of the approach were identified. To overcome the drawbacks, parallel strategies such as condition based evolutionary biclustering (CBEB) and coarse grained parallel genetic algorithm (CgPGA) were implemented. To further improve the performance, a new parallel genetic algorithm using dynamic demes strategy is implemented. This method uses global parallelization (master-slave model) with coarse-grained GA with overlapping subpopulation model. The primary objective is to find biclusters with minimum overlapping, large row variance, low mean square residue (MSR) and covering almost every element of expression matrix, thus minimizing the overall fitness value. Sequential EA and condition based EA (CBEB) is implemented but it was observed that both took too much time to meet the stopping criteria. So, to improve the efficiency of the genetic algorithm (GA), Parallel GA has been implemented with dynamic deme strategy to reduce the execution time of GA and find good quality biclusters. DdPGA yielded good quality biclusters and search space could be increased by implementing this strategy. This experiment was implemented on yeast Saccharamyces dataset.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132081520","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

Attention estimation system via smart glasses 通过智能眼镜的注意力估计系统

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058565

O. Chen, Pin-Chih Chen, Yi-Ting Tsai

{"title":"Attention estimation system via smart glasses","authors":"O. Chen, Pin-Chih Chen, Yi-Ting Tsai","doi":"10.1109/CIBCB.2017.8058565","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058565","url":null,"abstract":"Attention plays a critical role in effective learning. By means of attention assessment, it helps learners improve and review their learning processes, and even discover Attention Deficit Hyperactivity Disorder (ADHD). Hence, this work employs modified smart glasses which have an inward facing camera for eye tracking, and an inertial measurement unit for head pose estimation. The proposed attention estimation system consists of eye movement detection, head pose estimation, and machine learning. In eye movement detection, the central point of the iris is found by the locally maximum curve via the Hough transform where the region of interest is derived by the identified left and right eye corners. The head pose estimation is based on the captured inertial data to generate physical features for machine learning. Here, the machine learning adopts Genetic Algorithm (GA)-Support Vector Machine (SVM) where the feature selection of Sequential Floating Forward Selection (SFFS) is employed to determine adequate features, and GA is to optimize the parameters of SVM. Our experiments reveal that the proposed attention estimation system can achieve the accuracy of 93.1% which is fairly good as compared to the conventional systems. Therefore, the proposed system embedded in smart glasses brings users mobile, convenient, and comfortable to assess their attention on learning or medical symptom checker.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125129368","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: 8

Optimizing bioengineered vascular systems: A genetic algorithm approach 优化生物工程血管系统:一种遗传算法方法

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058562

Sima Mehri, Curtis Larsen, G. Podgorski, N. Flann

{"title":"Optimizing bioengineered vascular systems: A genetic algorithm approach","authors":"Sima Mehri, Curtis Larsen, G. Podgorski, N. Flann","doi":"10.1109/CIBCB.2017.8058562","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058562","url":null,"abstract":"Efficient metabolism in bioengineered tissues requires a robust vascular system to provide healthy microenvironments to the cells and stroma. Such networks form spontaneously during embryogenesis from randomly distributed endothelial cells. There is a need to bioengineer endothelial cells so that network formation and operation is optimal for synthetic tissues. This work introduces a computational model that simulates de novo vascular development and assesses the effectiveness of the network in delivering nutrients and extracting waste from tissue. A genetic algorithm was employed to identify parameter values of the vaculogenesis model that lead to the most efficient and robust vascular structures. These parameter values control the behavior of cell-level mechanisms such as chemotaxis and adhesion. These studies demonstrate that genetic algorithms are effective at identifying model parameters that lead to near-optimal networks. This work suggests that computational modeling and optimization approaches may improve the effectiveness of engineered tissues by suggesting target cellular mechanisms for modification.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129545717","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

Inference of genetic networks from time-series of gene expression levels using random forests 利用随机森林从基因表达水平的时间序列推断遗传网络

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058522

Shuhei Kimura, M. Tokuhisa, Mariko Okada

引用次数: 1

Single-objective and multi-objective genetic algorithms for compression of biological networks 生物网络压缩的单目标和多目标遗传算法

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058564

T. K. Collins, A. Zakirov, J. A. Brown, S. Houghten

{"title":"Single-objective and multi-objective genetic algorithms for compression of biological networks","authors":"T. K. Collins, A. Zakirov, J. A. Brown, S. Houghten","doi":"10.1109/CIBCB.2017.8058564","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058564","url":null,"abstract":"Storage and processing of biological networks is challenging and costly due to the large sizes of many of these networks. Compression of such graphs is one possible solution to this problem. This study presents two single-objective genetic algorithms, along with one multi-objective algorithm, to address the problem of graph compression. The fitness functions were both based on the concept of merging nodes based on “similarity” but each defined that similarity in a different way. The multiobjective GA based on NSGA-II worked to find a balance between the compression ratio and the similarity. The methods were applied to three different biological networks with different characteristics. The single-objective GAs were first applied to these networks for a fixed compression ratio. Then based on the results of the multiobjective GA, target compression ratios were chosen for each graph and the single-objective GAs were applied to this target. Applying the single-objective GAs to a target identified in this manner was significantly more successful than using the results from the multiobjective GA for the same compression ratio.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123835256","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: 4

Generating the logicome from microarray data 从微阵列数据生成逻辑组

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058542

Charmi Panchal, V. Rogojin

引用次数: 0

Large residual multiple view 3D CNN for false positive reduction in pulmonary nodule detection 大残差多视角三维CNN在肺结节检测中的假阳性复位

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058549

A. Dobrenkii, R. Kuleev, Adil Khan, Adín Ramírez Rivera, A. Khattak

{"title":"Large residual multiple view 3D CNN for false positive reduction in pulmonary nodule detection","authors":"A. Dobrenkii, R. Kuleev, Adil Khan, Adín Ramírez Rivera, A. Khattak","doi":"10.1109/CIBCB.2017.8058549","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058549","url":null,"abstract":"Pulmonary nodules detection play a significant role in the early detection and treatment of lung cancer. False positive reduction is the one of the major parts of pulmonary nodules detection systems. In this study a novel method aimed at recognizing real pulmonary nodule among a large group of candidates was proposed. The method consists of three steps: appropriate receptive field selection, feature extraction and a strategy for high level feature fusion and classification. The dataset consists of 888 patient's chest volume low dose computer tomography (LDCT) scans, selected from publicly available LIDC-IDRI dataset. This dataset was marked by LUNA16 challenge organizers resulting in 1186 nodules. Trivial data augmentation and dropout were applied in order to avoid overfitting. Our method achieved high competition performance metric (CPM) of 0.735 and sensitivities of 78.8% and 83.9% at 1 and 4 false positives per scan, respectively. This study is also accompanied by detailed descriptions and results overview in comparison with the state of the art solutions.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129473535","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: 19

Leveraging data mining techniques to understand drivers of obesity 利用数据挖掘技术来了解肥胖的驱动因素

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058521

Reza Salehnejad, R. Allmendinger, Yu-wang Chen, Manhal Ali, A. Shahgholian, Paraskevas Yiapanis, Mohaimen Mansur

{"title":"Leveraging data mining techniques to understand drivers of obesity","authors":"Reza Salehnejad, R. Allmendinger, Yu-wang Chen, Manhal Ali, A. Shahgholian, Paraskevas Yiapanis, Mohaimen Mansur","doi":"10.1109/CIBCB.2017.8058521","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058521","url":null,"abstract":"Substantial research has been carried out to explain the effects of economic variables on obesity, typically considering only a few factors at a time, using parametric linear regression models. Recent studies have made a significant contribution by examining economic factors affecting body weight using the Behavioral Risk Factor Surveillance System data with 27 state-level variables for a period of 20 years (1990–2010). As elsewhere, the authors solely focus on individual effects of potential drivers of obesity than critical interactions among the drivers. We take some steps to extend the literature and gain a deeper understanding of the drivers of obesity. We employ state-of-the-art data mining techniques to uncover critical interactions that may exist among drivers of obesity in a data-driven manner. The state-of-the-art techniques reveal several complex interactions among economic and behavioral factors that contribute to the rise of obesity. Lower levels of obesity, measured by a body mass index (BMI), belong to female individuals who exercise outside work, enjoy higher levels of education and drink less alcohol. The highest level of obesity, in contrast, belongs to those who fail to exercise outside work, smoke regularly, consume more alcohol and come from lower income groups. These and other complementary results suggest that it is the joint complex interactions among various behavioral and economic factors that gives rise to obesity or lowers it; it is not simply the presence or absence of individual factors.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128495854","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