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

DASE: Condition-specific differential alternative splicing variants estimation method without reference genome sequence, and its application to non-model organisms 无参考基因组序列的条件特异性差异选择性剪接变异体估计方法及其在非模式生物中的应用

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822540

Kouki Yonezawa, Tsukasa Mori, S. Shigeno, A. Ogura

引用次数: 0

Wide line detection with water flow 宽线检测水流

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822715

Yangyang Hu, Wenqiang Zhang, Hong Lu, Fufeng Li, Weifei Zhang

引用次数: 1

Is EEG causal to fNIRs? 脑电图与近红外光谱有因果关系吗?

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822648

Borzou Alipourfard, Jean X. Gao, Olajide Babawale, Hanli Liu

{"title":"Is EEG causal to fNIRs?","authors":"Borzou Alipourfard, Jean X. Gao, Olajide Babawale, Hanli Liu","doi":"10.1109/BIBM.2016.7822648","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822648","url":null,"abstract":"Causality analysis of simultaneous measurements of the brain's electrical activity and its hemodynamic activity provides the opportunity to study the neural underpinning of hemodynamic fluctuations. This multimodal analysis can also be used to extract valuable information regarding the location of the generators of various electrical events such as Alpha rhythms or epileptiform activity. To best of our knowledge, we are the first propose a method to assess causality from EEG to the hemodynamic activity measured using functional near-infrared spectroscopy (fNIRs). The main challenge in studying causality within this setting arises from the low sampling rate of the fNIRs and the mixed frequency nature of the data. Our method of analysis consists of two parts. Through a simple modification of Geweke's formulation of contamination, we first show that the low sampling frequency of the fNIRs does not cause contamination in estimating causality from EEG to fNIRs. We then apply a novel causality test to avoid the down-sampling of the EEG when measuring for causality. The method of analysis proposed here can be generalized to study causality in other biomedical signal analysis applications and mixed frequency settings.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"29 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":"114493302","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

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

2Path: A terpenoid metabolic network modeled as graph database 2 . path:一个以图数据库为模型的萜类代谢网络

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822709

Waldeyr M. C. Silva, Danilo Vilar, Daniel S. Souza, M. E. Walter, M. Brigido, M. Holanda

{"title":"2Path: A terpenoid metabolic network modeled as graph database","authors":"Waldeyr M. C. Silva, Danilo Vilar, Daniel S. Souza, M. E. Walter, M. Brigido, M. Holanda","doi":"10.1109/BIBM.2016.7822709","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822709","url":null,"abstract":"Terpenoids are involved in interactions as signaling for communication intra/inter species, signal molecules to attract pollinating insects, and defense against herbivores and microbes. Due to their chemical composition, many terpenoids possess vast pharmacological applicability in medicine and biotechnology, besides important roles in ecology, industry and commerce. The biosynthesis of terpenes has been widely studied over the years, and it is well known that they can be synthesized from two metabolic pathways: mevalonate pathway (MVA) and non-mevalonate pathway (MEP). On the other hand, genome-scale reconstruction of metabolic networks faces many challenges, including organizational data storage and data modeling, to properly represent the complexity of systems biology. Recent NoSQL database paradigms have introduced new concepts of scalable storage and data queries. Among them graph databases, which are versatile enough to cope with biological data. In this paper, we propose 2Path, a graph database model designed to represent terpenoid metabolic networks, with thousands of secondary metabolism reactions, such that it preserves important terpenoid biosynthesis characteristics.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"31 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":"123752458","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

Integration of multiple heterogeneous omics data 多个异构组学数据的集成

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822582

Chuanchao Zhang, Juan Liu, Qianqian Shi, Xiangtian Yu, T. Zeng, Luonan Chen

{"title":"Integration of multiple heterogeneous omics data","authors":"Chuanchao Zhang, Juan Liu, Qianqian Shi, Xiangtian Yu, T. Zeng, Luonan Chen","doi":"10.1109/BIBM.2016.7822582","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822582","url":null,"abstract":"Integration of different genomic profiles is challenging to understand complex diseases in a multi-view manner. Computational method is needed to preserve useful information of data types as well as correct bias. Thus, we proposed a novel framework pattern fusion analysis (PFA), to fuse the local sample patterns into a global pattern of patients with respect to the underlying data, by adaptively aligning the information in each type of biological data. In particular, PFA can adjust the distinct data types and achieve more robust sample pattern within different profiles. To validate the effectiveness of PFA, we tested PFA on various synthetic datasets and found that PFA is able to effectively capture the intrinsic clustering structure than the state-of-the-art integrative methods, such as moCluster, iClusterPlus and SNF. Moreover, in a case study on kidney cancer, PFA not only identified the multi-way feature modules among the prior-known disease associated genes, methylations and miRNAs, but also outperformed in cancer subtypes identification and could get effective clinical prognosis prediction. Totally, PFA not only provides new insights on the more holistic & systems-level sample pattern, but also supplies a new way for selecting more informative types of biological data.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 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":"129923959","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