2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第8页

mAMBER: A CPU/MIC collaborated parallel framework for AMBER on Tianhe-2 supercomputer 天河二号超级计算机AMBER的CPU/MIC协同并行框架

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822595

Shaoliang Peng, Xiaoyu Zhang, Yutong Lu, Xiangke Liao, Kai Lu, Canqun Yang, Jie Liu, Weiliang Zhu, Dongqing Wei

{"title":"mAMBER: A CPU/MIC collaborated parallel framework for AMBER on Tianhe-2 supercomputer","authors":"Shaoliang Peng, Xiaoyu Zhang, Yutong Lu, Xiangke Liao, Kai Lu, Canqun Yang, Jie Liu, Weiliang Zhu, Dongqing Wei","doi":"10.1109/BIBM.2016.7822595","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822595","url":null,"abstract":"Molecular dynamics (MD) is a computer simulation method of studying physical movements of atoms and molecules that provide detailed microscopic sampling on molecular scale. With the continuous efforts and improvements, MD simulation gained popularity in materials science, biochemistry and biophysics with various application areas and expanding data scale. Assisted Model Building with Energy Refinement (AMBER) is one of the most widely used software packages for conducting MD simulations. However, the speed of AMBER MD simulations for system with millions of atoms in microsecond scale still need to be improved. In this paper, we propose a parallel acceleration strategy for AMBER on Tianhe-2 supercomputer. The parallel optimization of AMBER is carried out on three different levels: fine grained OpenMP parallel on a single MIC, single-node CPU/MIC collaborated parallel optimization and multi-node multi-MIC collaborated parallel acceleration. By the three levels of parallel acceleration strategy above, we achieved the highest speedup of 25–33 times compared with the original program. Source Code: https://github.com/tianhe2/mAMBER","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134490291","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

Mathematical and computational analysis of CRISPR Cas9 sgRNA off-target homologies CRISPR Cas9 sgRNA脱靶同源性的数学和计算分析

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822558

M. Zhou, Daisy Li, X. Huan, Joseph Manthey, E. Lioutikova, Hong Zhou

引用次数: 3

3D tracking swimming fish school using a master view tracking first strategy 采用主视图跟踪优先策略对游动鱼群进行三维跟踪

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822572

Shuohong Wang, Xiang Liu, Jingwen Zhao, Ye Liu, Y. Chen

引用次数: 11

An evaluation of data replication for bioinformatics workflows on NoSQL systems NoSQL系统中生物信息学工作流程的数据复制评估

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822644

Iasmini Lima, Matheus Oliveira, Diego S. Kieckbusch, M. Holanda, M. E. Walter, Aleteia P. F. Araujo, M. Victorino, Waldeyr M. C. Silva, Sérgio Lifschitz

引用次数: 3

Sparse singular value decomposition-based feature extraction for identifying differentially expressed genes 基于稀疏奇异值分解的差异表达基因特征提取

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822795

Jin-Xing Liu, Xiangzhen Kong, C. Zheng, J. Shang, Wei Zhang

{"title":"Sparse singular value decomposition-based feature extraction for identifying differentially expressed genes","authors":"Jin-Xing Liu, Xiangzhen Kong, C. Zheng, J. Shang, Wei Zhang","doi":"10.1109/BIBM.2016.7822795","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822795","url":null,"abstract":"Recently, feature extraction and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as genome data. In this paper, a new feature extraction method based on sparse singular value decomposition (SSVD) is developed. SSVD algorithm is applied to extract differentially expressed genes from two different genome datasets that are all from The Cancer Genome Atlas (TCGA), and then the extracted genes are evaluated by the tools based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. As a gene extraction method, SSVD is also compared with some existing feature extraction methods such as independent component analysis, the p-norm robust feature extraction and sparse principal component analysis. The experimental GO analysis results show that SSVD method outperforms the competitive algorithms. The KEGG analysis results demonstrate the genes which participate in the pathways in cancer. The elaborate experiments prove that SSVD is an effective feature selection method compared with the competitive methods. The KEGG analysis results may provide a meaningful reference to carry out further study for professionals in the field of biomedical science.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123004401","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

Modular reconfiguration of metabolic brain networks in health and cancer: A resting-state PET study 健康和癌症中代谢脑网络的模块化重构:静息状态PET研究

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822665

Zhijun Yao, Bin Hu, Xuejiao Chen, Yuanwei Xie, Lei Fang

{"title":"Modular reconfiguration of metabolic brain networks in health and cancer: A resting-state PET study","authors":"Zhijun Yao, Bin Hu, Xuejiao Chen, Yuanwei Xie, Lei Fang","doi":"10.1109/BIBM.2016.7822665","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822665","url":null,"abstract":"Recent studies suggested that cognitive impairments and memory difficulties in cancer survivors were associated with topology changes of brain network, particularly in terms of the functional and structural abnormalities. However, little is known about the modular reconfiguration of metabolic brain network among this population. In this study, we recruited 78 patients with pre-treatment cancer and 80 age- and gender-matched normal controls (NCs), and constructed the metabolic brain networks derived from resting-state 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to assess the alters of modularity pattern in cancer. The measurements of the participation index (PI) and mutual information (MI) were calculated for the cancer and NC groups. Compared with NC group, one module composed by the hippocampus, the amygdala and frontal and temporal regions was absented in cancer group. Moreover, cancer patients showed abnormal topology pattern in their metabolic networks (i.e., increased local efficiency and reduced global efficiency). Although node-wise PI shared positive correlated with normalized metabolism uptake in both groups, the more energy consumption were observed in metabolism network of cancer group that might be indicative of reduced capability of information processing. In addition, the between-group MIs were gradually increased over a range of thresholds. Our results suggested that modular pattern of the metabolic brain network seemed to re-shape its organization in cancer, which might uncover the neurobiological mechanisms underlying cancer-related cognitive dysfunction.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132394965","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

COLT: COnstrained Lineage Tree Generation from sequence data 从序列数据生成约束谱系树

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822500

Keke Chen, Venkata Sai Abhishek Gogu, Di Wu, Jiang Ning

引用次数: 3

Weighted multiview learning for predicting drug-disease associations 用于预测药物-疾病关联的加权多视图学习

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822603

S. N. Chandrasekaran, Jun Huan

{"title":"Weighted multiview learning for predicting drug-disease associations","authors":"S. N. Chandrasekaran, Jun Huan","doi":"10.1109/BIBM.2016.7822603","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822603","url":null,"abstract":"The paradigm of drug discovery has moved from finding new drugs that exhibit therapeutic properties for a disease to reusing existing approved drugs for a newer disease. The association between a drug and a disease involves a complex network of targets and pathways. In order to provide new insights, there has been a constant need for sophisticated tools that have the potential to discover new associations from the underlying drugs-disease interactions. In addition to computational tools, there has been an explosion of data available in terms of drugs, disease and their activity profiles. On one hand, researchers have been using existing machine learning tools that have shown great promise in predicting associations but on the other hand there has been a void in exploiting advance machine learning frameworks to handle this kind of data integration. In this paper, we propose a learning framework called weighted multi-view learning that is a variant of the Multi-view learning framework in which the views are assumed to contribute equally to the prediction whereas our method learns a weight for each view since we hypothesize that certain views might have better prediction capability than others.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"100 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134190462","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

Multi-label classification for intelligent health risk prediction 智能健康风险预测的多标签分类

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822657

Runzhi Li, Hongling Zhao, Yusong Lin, Andrew S. Maxwell, Chaoyang Zhang

{"title":"Multi-label classification for intelligent health risk prediction","authors":"Runzhi Li, Hongling Zhao, Yusong Lin, Andrew S. Maxwell, Chaoyang Zhang","doi":"10.1109/BIBM.2016.7822657","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822657","url":null,"abstract":"A Multi-Label Problem Transformation Joint Classification (MLPTJC) method is developed to solve the multi-label classification problem for the health and disease risk prediction based on physical examination records. We adopt a multi-class classification problem transformation method to transform the multi-label classification problem to a multi-class classification problem. Then We propose a Joint Decomposition Subset Classifier method to reduce the infrequent label sets to deal with the imbalance learning problem. Based on MLPTJC, existing cost-sensitive multi-class classification algorithms can be used to train the prediction models. We conduct some experiments to evaluate the performance of the MLPTJC method. The Support Vector Machine (SVM) and Random Forest (RF) algorithms are used for multi-class classification learning. We use the 10-fold cross-validation and metrics such as Average Accuracy, Precision, Recall and F-measure to evaluate the performance. The real physical examination records were employed, which include 62 examination items and 110, 300 anonymous patients. 8 types of diseases were predicted. The experimental results show that the MLPTJC method has better performance in terms of accuracy.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134600494","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

Effects of propafenone on KCNH2-linked short QT syndrome: A modelling study 普罗帕酮对kcnh2相关短QT综合征的影响:一项模型研究

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822744

Cunjin Luo, Kuanquan Wang, Henggui Zhang

{"title":"Effects of propafenone on KCNH2-linked short QT syndrome: A modelling study","authors":"Cunjin Luo, Kuanquan Wang, Henggui Zhang","doi":"10.1109/BIBM.2016.7822744","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822744","url":null,"abstract":"The identified genetic short QT syndrome (SQTS) is associated with an increased risk of arrhythmia and sudden death. This study was to investigate the potential effects of propafenone on KCNH2-linked short QT syndrome (SQT1) using a multi-scale biophysically detailed model of the heart developed by ten Tusscher and Panfilov. The ion electrical conductivities were reduced by propafenone in order to simulate the pharmacological effects in healthy and SQT1 cells. Based on the experimental data of McPate et al., the pharmacological effect of propafenone was modelled by dose-dependent IKr blocking. Action potential (AP) profiles and 1D tissue level were analyzed to predict the effects of propafenone on SQT1. Both low- and high- dose of propafenone prolonged APD and QT interval in SQT1 cells. It suggests the superior efficacy of high dose of propafenone on SQT1. However, propafenone did not significantly alter the healthy APD or QT interval at low dose, whereas markedly shortened them at high dose. Our simulation data show that propafenone has a dose-dependently anti-arrhythmic effect on SQT1, and a pro-arrhythmic effect on healthy cells. These computer simulations help to better understand the underlying mechanisms responsible for the initiation or termination of arrhythmias in healthy or SQT1 patients using propafenone.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132437700","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