2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第7页

Competitive pathway analysis using Structural Equation Models (CPA-SEM) for gene expression data 利用结构方程模型(CPA-SEM)对基因表达数据进行竞争通路分析

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359875

Sungkyoung Choi, Sungyoung Lee, Iksoo Huh, Heungsun Hwang, T. Park

{"title":"Competitive pathway analysis using Structural Equation Models (CPA-SEM) for gene expression data","authors":"Sungkyoung Choi, Sungyoung Lee, Iksoo Huh, Heungsun Hwang, T. Park","doi":"10.1109/BIBM.2015.7359875","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359875","url":null,"abstract":"There is an increasing interest in the pathway analysis of multiple genes and complex traits in association studies. Recently, a number of methods of pathway analysis have been developed to detect the novel pathways associated with human complex traits. In this paper, we propose a novel statistical approach for competitive pathway analysis based on Structural Equation Modeling (CPA-SEM), taking advantage of prior knowledge on existing relationships between genes in a pathway. Our CPA-SEM identifies pathways associated with traits of interest. The CPA-SEM approach is different from the previous SEM-based approaches in that it considers all possible sub-pathways into account and performs permutation based robust analysis. We applied the proposed CPA-SEM method to gene expression data of gastric cancer (GSE27342), and found that mTOR signaling pathway was significantly associated with gastric cancer. This pathway has previously been reported to be associated with gastric cancer. In conclusion, our CPA-SEM analysis provides a better understanding of biological mechanism by identifying pathways associated with a trait of interest.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"5 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":"121215422","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

VizEpis : A visualization and mapping tool for interpreting epistasis VizEpis:用于解释上位的可视化和映射工具

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359877

Min-Seok Kwon, Sungyoung Lee, Yongkang Kim, T. Park

引用次数: 3

Cross-validation and cross-study validation of chronic lymphocytic leukemia with exome sequences and machine learning 外显子组序列和机器学习对慢性淋巴细胞白血病的交叉验证和交叉研究验证

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1504/IJDMB.2016.10000562

N. Patel, Bharati Jhadav, Abdulrhman Aljouie, Usman Roshan

{"title":"Cross-validation and cross-study validation of chronic lymphocytic leukemia with exome sequences and machine learning","authors":"N. Patel, Bharati Jhadav, Abdulrhman Aljouie, Usman Roshan","doi":"10.1504/IJDMB.2016.10000562","DOIUrl":"https://doi.org/10.1504/IJDMB.2016.10000562","url":null,"abstract":"The era of genomics brings the potential of better DNA based risk prediction and treatment. While genome-wide association studies are extensively studied for risk prediction, the potential of using whole exome data for this purpose is unclear. We explore this problem for chronic lymphocytic leukemia that is one of the largest whole exome dataset of 186 case and 169 controls available from the NIH dbGaP database. We perform a standard next generation sequence procedure to obtain SNP variants on 153 cases and 144 controls after exclusion of samples with missing data. To evaluate their predictive power we first conduct a 50% training and 50% test cross-validation study on the full dataset with the support vector machine as the classifier. There we obtain a mean accuracy of 82% with top 20 ranked SNPs obtained by the Pearson correlation coefficient. We then perform a cross-study validation on case and controls from a lymphoma external study and just controls from head and neck cancer and breast cancer studies (all obtained from NIH dbGaP). On the external dataset we obtain an accuracy of 70% with top ranked SNPs obtained from the original dataset. We also find our top Pearson ranked SNPs to lie on previously implicated genes for this disease. Our study shows that even with a small sample size we can obtain moderate to high accuracy with exome sequences and is thus encouraging for future work.","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":"126253486","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

RREV: Reconfigurable Rendering Engine for visualization of clinically annotated polysomnograms RREV:用于临床注释多导睡眠图可视化的可重构渲染引擎

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359700

Catherine P. Jayapandian, Wei Wang, Michael G. Morrical, Dennis A. Dean, Shiqiang Tao, Daniel R. Mobley, Matthew Kim, M. Rueschman, K. Loparo, S. Redline, Guoqiang Zhang

{"title":"RREV: Reconfigurable Rendering Engine for visualization of clinically annotated polysomnograms","authors":"Catherine P. Jayapandian, Wei Wang, Michael G. Morrical, Dennis A. Dean, Shiqiang Tao, Daniel R. Mobley, Matthew Kim, M. Rueschman, K. Loparo, S. Redline, Guoqiang Zhang","doi":"10.1109/BIBM.2015.7359700","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359700","url":null,"abstract":"In sleep medicine, clinical studies often use their own data dictionaries for capturing clinical sleep events using proprietary signal analysis software [1][2]. Visualization of polysomnograms and their associated events from multiple distinct studies, such as for the National Sleep Research Resource (NSRR)[3], is an unresolved issue. Currently, there is no known visualization software for the European Data Format (EDF) that can be dynamically configured to support rendering of sleep events for multiple vendor formats. To address this challenge, domain ontology has been developed as a part of NSRR to model all sleep medicine terms and concepts to provide a common schema for addressing the structural and semantic heterogeneity of multiple vendor formats [4]. A Reconfigurable Rendering Engine using Abstract Factory pattern [5] and domain ontology provides a standard interface for accessing ontology-enabled clinical events for the visualization of electrophysiological signals. About 11,078 polysomnograms (8,444 SHHS, 860 CHAT, 591 HeartBEAT, 730 CFS, 453 SOF) [12] in EDF have been processed resulting in 1.1TB of web-accessible and reusable PSGs with NSRR standardized event annotations.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"72 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":"126349483","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

Discover potential adverse drug reactions using the skip-gram model 使用skip-gram模型发现潜在的药物不良反应

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359955

Mingzhen Zhao, Bo Xu, Hongfei Lin, Zhihao Yang, Jian Wang

引用次数: 2

Malphite: A convolutional neural network and ensemble learning based protein secondary structure predictor 基于卷积神经网络和集成学习的蛋白质二级结构预测器

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359861

Y. Li, T. Shibuya

{"title":"Malphite: A convolutional neural network and ensemble learning based protein secondary structure predictor","authors":"Y. Li, T. Shibuya","doi":"10.1109/BIBM.2015.7359861","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359861","url":null,"abstract":"We developed a convolution neural networks (CNN) and ensemble learning based method, called Malphite, to predict protein secondary structures. Maphite has three sub-models: the 1st CNN, PSI-PRED and the 2nd CNN. The 1st CNN and PSI-PRED are used to predict the initial secondary structure based on the position specific scoring matrix generated from PSIBLAST. The 2nd CNN performs ensemble learning by combining the prediction result of the 1st CNN and PSI-PRED and generate the final predictions. Malphite achieved a Q3 score of 82.3% and 82.6% for independently built dataset of 400 and 538 proteins respectively, and 82.6% ten-fold-cross validated accuracy for a dataset of 3000 proteins. In addition, Malphite accomplished a remarkable Q3 score of 83.6% for 122 targets from CASP10 (Critical Assessment of protein Structure Prediction), surpassing any secondary structure prediction technique to date. For all four datasets, Malphite consistently makes 2% more accurate prediction than PSI-PRED, which is a significantly step towards the estimated upper limit of protein secondary structure prediction accuracy of 90%.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"42 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":"128938529","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

Deep neural network based protein-protein interaction extraction from biomedical literature 基于深度神经网络的生物医学文献蛋白质相互作用提取

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359845

Zhehuan Zhao, Zhihao Yang, Ling Luo, Hongfei Lin, Jian Wang, Song Gao

引用次数: 2

Hybrid multi-threaded simulation of agent-based pandemic modeling using multiple GPUs 使用多个gpu的基于代理的流行病建模的混合多线程模拟

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359894

Barzan Shekh, E. Doncker, D. Prieto

{"title":"Hybrid multi-threaded simulation of agent-based pandemic modeling using multiple GPUs","authors":"Barzan Shekh, E. Doncker, D. Prieto","doi":"10.1109/BIBM.2015.7359894","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359894","url":null,"abstract":"Epidemiology computation models are crucial for the assessment and control of public health crises. Agent-based simulations of pandemic influenza forecast the infectious disease spreading in order to help public health policy makers during emergencies. In such emergencies, decisions are required for public health preparedness in cycles of less than a day, and the agent-based model should be adaptable and tractable for quick and simple calibration with low computational overhead. GPU accelerated computing involves the use of graphics processing units (GPUs) in combination with the CPU to perform heterogeneous computing by offloading compute-intensive portions of the program to the GPU while the remaining program runs on the CPU. In this paper, we demonstrate the utilization of the hardware environment and software tools and discuss strategies for porting agent-based simulations to multiple GPUs. We further compare the performance of simulations using two or four GPUs with the sequential execution on the CPU, in terms of time and speedup. The multi-GPU implementations exhibit great performance and support populations with up to 100 million individuals.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"22 1 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":"116857049","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

A new compact set of biomarkers for distinguishing among ten breast cancer subtypes 一组新的紧凑的生物标记物用于区分十种乳腺癌亚型

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359911

Forough Firoozbakht, Iman Rezaeian, A. Ngom, L. Rueda

引用次数: 2

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease 一种用于轻度认知障碍和阿尔茨海默病分类的结构MRI特征选择遗传算法

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359909

Alexander Luke Spedding, G. D. Fatta, M. Cannataro

引用次数: 9