2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献

The role of high performance, grid and cloud computing in high-throughput sequencing 高性能、网格和云计算在高通量测序中的作用

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-15 DOI: 10.1109/BIBM.2016.7822643

G. Lightbody, Fiona Browne, Huiru Zheng, Valeriia Haberland, J. Blayney

引用次数: 3

Disease-specific protein complex detection in the human protein interaction network with a supervised learning method 人类蛋白质相互作用网络中疾病特异性蛋白质复合物检测的监督学习方法

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822705

Ziwei Zhou, Yingyi Gui, Zhihao Yang, Xiaoxia Liu, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang

{"title":"Disease-specific protein complex detection in the human protein interaction network with a supervised learning method","authors":"Ziwei Zhou, Yingyi Gui, Zhihao Yang, Xiaoxia Liu, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang","doi":"10.1109/BIBM.2016.7822705","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822705","url":null,"abstract":"High-throughput experimental techniques have produced a large amount of human protein-protein interactions, making it possible to construct a large-scale human PPI network and detect human protein complexes from the network with computational approaches. However, most of current complex detection methods are based on graph theory which can't utilize the information of the known complexes. In this paper, we present a supervised learning method to detect protein complexes in a human PPI network. In this method, biological characteristics and properties of the network are taken into consideration to construct a rich feature set to train a regression model for protein complex detection. In addition, the specific disease related PPIs are extracted from biomedical literatures and then integrated into the original PPI network for detecting the disease-specific protein complexes more effectively. Experimental results show that the performance of our method is superior to other existing state-of-the-art methods. Furthermore, through the analysis of the breast cancer specific complexes detected with our method, more biological insights for breast cancer (e.g., some candidate susceptible genes of breast cancer) are provided.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"23 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":"115622720","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

Mining protein complexes based on topology potential from weighted dynamic PPI network 基于加权动态PPI网络拓扑势的蛋白质复合物挖掘

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822611

Xiu-juan Lei, Yuchen Zhang, Fang-Xiang Wu, A. Zhang

引用次数: 0

Experiences on quantitative cardiac PET analysis 心脏PET定量分析的经验

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822682

P. Vizza, P. Guzzi, P. Veltri, Annalisa Papa, G. Cascini, G. Sesti, E. Succurro

引用次数: 13

Epistasis detection using a permutation-based Gradient Boosting Machine 基于置换梯度增强机的上位性检测

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822697

Kai Che, Xiaoyan Liu, Maozu Guo, Junwei Zhang, Lei Wang, Yin Zhang

引用次数: 6

Network based study for the anti-rheumatic mechanism of Tibetan medicated-bath therapy 基于网络的藏药浴抗风湿机理研究

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822802

Jian Yang, Tianhong Wang, Xiaona Shen, Xing Chen, Kehui Zhao, Jing Wang, Yi Zhang, Jing Zhao, Yang Ga

引用次数: 0

Towards constructing “Super Gene Sets” regulatory networks 构建“超级基因集”调控网络

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822534

J. Chen, Z. Yue, Michael T. Neylon, Thanh Nguyen, Nafisa Bulsara, Itika Arora, Timothy Ratliff

{"title":"Towards constructing “Super Gene Sets” regulatory networks","authors":"J. Chen, Z. Yue, Michael T. Neylon, Thanh Nguyen, Nafisa Bulsara, Itika Arora, Timothy Ratliff","doi":"10.1109/BIBM.2016.7822534","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822534","url":null,"abstract":"In this article, we described a new computational framework to construct “Super Gene Sets”-Pathways, Annotated list, and Gene signatures (PAGs), regulatory (r-type) PAG-PAG relationships. To construct PAGs, we aggregate singleton PAGs (sPAGs) upstream/downstream of a common shared multi-gene PAG (mPAGs). Then, we iteratively remove a member gene to calculate its Cohesion Coefficient (CoCo), which helps assess the degree of biological relevance beyond random chance, until the CoCo score achieves the maximal value at a specific level. The new relationship between aggregated mPAG (m'PAG) and the shared mPAG will, therefore, have distinct m'PAG-mPAG relationships. Our results suggest the following. First, the new m'PAGs have sufficiently high CoCo scores, suggesting high biological relevance, and distinct gene ontology annotations different from their regulated PAG targets; however, there are significant enrichments of shared GO annotations between each pair of identified m'PAG-mPAG relationships. Second, new m'PAGs are relatively robust against data noise based on noise characteristic simulations. Third, by applying our framework to real cancer microarray analysis data, we demonstrated that our new framework is effective in helping build multi-scale biomolecular systems models that are easy to interpret by biologists.","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":"115327587","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

Drug side effect prediction through linear neighborhoods and multiple data source integration 基于线性邻域和多数据源集成的药物副作用预测

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822555

Wen Zhang, Yanlin Chen, Shikui Tu, Feng Liu, Qianlong Qu

{"title":"Drug side effect prediction through linear neighborhoods and multiple data source integration","authors":"Wen Zhang, Yanlin Chen, Shikui Tu, Feng Liu, Qianlong Qu","doi":"10.1109/BIBM.2016.7822555","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822555","url":null,"abstract":"predicting drug side effects is a critical task in the drug discovery, which attracts great attentions in both academy and industry. Although lots of machine learning methods have been proposed, great challenges arise with boom of precision medicine. On one hand, many methods are based on the assumption that similar drugs may share same side effects, but measuring the drug-drug similarity appropriately is challenging. One the other hand, multi-source data provide diverse information for the analysis of side effects, and should be integrated for the high-accuracy prediction. In this paper, we tackle the side effect prediction problem through linear neighborhoods and multi-source data integration. In the feature space, linear neighborhoods are constructed to extract the drug-drug similarity, namely “linear neighborhood similarity”. By transferring the similarity into the side effect space, known side effect information is propagated through the similarity-based graph. Thus, we propose the linear neighborhood similarity method (LNSM), which utilizes single-source data for the side effect prediction. Further, we extend LNSM to deal with multi-source data, and propose two data integration methods: similarity matrix integration method (LNSM-SMI) and cost minimization integration method (LNSM-CMI), which integrate drug substructure data, drug target data, drug transporter data, drug enzyme data, drug pathway data and drug indication data to improve the prediction accuracy. The proposed methods are evaluated on the benchmark datasets. The linear neighborhood similarity method (LNSM) can produce satisfying results on the single-source data. Data integration methods (LNSM-SMI and LNSM-CMI) can effectively integrate multi-source data, and outperform other state-of-the-art side effect prediction methods in the cross validation and independent test. The proposed methods are promising for the drug side effect prediction.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"48 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":"122705588","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: 53

Extreme Large Margin Distribution Machine and its applications for biomedical datasets 极大边际分布机及其在生物医学数据集上的应用

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822751

Zhiyong Yang, Jingcheng Lu, Taohong Zhang

{"title":"Extreme Large Margin Distribution Machine and its applications for biomedical datasets","authors":"Zhiyong Yang, Jingcheng Lu, Taohong Zhang","doi":"10.1109/BIBM.2016.7822751","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822751","url":null,"abstract":"Classification methods has become increasingly popular for biomedical and bioinformatical data analysis. However, due to the difficulty of data acquisition, sometimes we could only obtain small-scale datasets which may leads to unreasonable generalization performances. For SVM-like algorithms, we could resort to Large Margin theory to find out solutions for such dilemma. Recent studies on large margin theory show that, besides maximizing the minimum margin of a given training dataset, it is also necessary to optimization the margin distribution to boost the overall generalization ability. Correspondingly, a novel SVM-like algorithm called Large Margin Distribution Machine (LDM) realizes this idea by maximizing the average of margin and minimizing the variance of margin simultaneously. And a series of applications has been reported thereafter. There is another well-known machine learning algorithm called Extreme Learning Machine (ELM) which shares similar framework with SVM. It is believed in this paper ELM could also benefit from the virtues of margin distribution optimization. Bearing this in mind, a novel algorithm called Extreme Large Margin Distribution Machine(ELDM) is proposed in this paper by bridging the advantages of ELM and LDM. And an efficient extension of ELDM for multi-class classifications under One vs. All Scheme is proposed subsequently. Finally, the experiment results on both benchmark datasets and biomedical classification datasets show the effectiveness of our proposed algorithm.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"70 3 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":"122900879","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: 6

Development of a computer-aided system for an effective brain connectivity network 一种有效大脑连接网络的计算机辅助系统的开发

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822774

Yaoxin Nie, Linlin Zhu, Yipeng Su, Xudong Li, Zhendong Niu

{"title":"Development of a computer-aided system for an effective brain connectivity network","authors":"Yaoxin Nie, Linlin Zhu, Yipeng Su, Xudong Li, Zhendong Niu","doi":"10.1109/BIBM.2016.7822774","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822774","url":null,"abstract":"Currently, dynamic causal modeling (DCM) is one of the most widely used models for an effective brain connectivity network, but it also has some disadvantages (e.g., researchers' selection of cerebral regions of interest [ROIs] is subjective, a substantial time is required for computation, etc.). Statistical Parametric Mapping (SPM) is the most popular statistical data analysis software for brain function, but its settings cumbersome, especially the data preprocessing section. In response to these disadvantages of DCM and SPM, we designed and created a computer-aided system for an effective brain connectivity network, modularized the data preprocessing section of SPM, and we explored the cerebral ROIs and possible co-activation network based on our proposed approach. The co-activation network has as a prior interconnection relationship, and it is used to assist in the selection of ROIs in similar cognitive experiments; thus, the testing of meaningless noise connection modes by the DCM is prevented, the number of models DMC is decreased, and the accuracy of the conclusions and computational efficiency of the DCM are improved.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"36 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":"122852979","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