2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第8页

Integrating multiple sources of genomic data by multiplex network reconstruction 多路网络重构整合多源基因组数据

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359926

Shang Gao, W. Zou, Yuanyuan Liu, Xingwang Wang, Y. Zhuang, X. Wei, R. Alhajj

引用次数: 1

A two-step logistic regression algorithm for identifying individual-cancer-related genes 用于识别个体癌症相关基因的两步逻辑回归算法

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359680

Bolin Chen, Xuequn Shang, Min Li, Jianxin Wang, Fang-Xiang Wu

{"title":"A two-step logistic regression algorithm for identifying individual-cancer-related genes","authors":"Bolin Chen, Xuequn Shang, Min Li, Jianxin Wang, Fang-Xiang Wu","doi":"10.1109/BIBM.2015.7359680","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359680","url":null,"abstract":"The identification of cancer-related genes is important towards the understanding of complex genetic diseases. Although many machine learning algorithms are proposed to identify disease-related genes, they often either have poor performance to identify locus heterogeneity cancer-related genes or are not applicable to predict individual-disease-related genes due to the lack of positive instances (imbalanced classification). To overcome these two issues, a two-step logistic regression (LR) based algorithm is proposed in this study for identifying individual-cancer-related genes. A set of high potential cancer-class-related genes is first generated in step 1, followed by a second round of LR-based algorithm conducted on this smaller dataset for identifying individual-cancer-related genes. Numerical experiments show that the proposed two-step LR-based algorithm not only works well for locus heterogeneity data, but also has good performance to handle the imbalanced classification problem. The individual-cancer-related gene identification experiments achieve AUC values of around 0.85 when the threshold of posterior probability is chosen between 0.3 and 0.6. All evaluations are conducted by using the leave-one-out cross validation method.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"45 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":"134055657","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: 10

Finding Frequent Approximate Subgraphs in medical image database 在医学图像数据库中寻找频繁近似子图

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359821

Linlin Gao, Haiwei Pan, Qilong Han, Xiaoqin Xie, Zhiqiang Zhang, Xiao Zhai, Pengyuan Li

引用次数: 6

RF-Phos: Random forest-based prediction of phosphorylation sites RF-Phos:基于随机森林的磷酸化位点预测

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359670

Ahoi Jones, Hamid D. Ismail, J. H. Kim, R. Newman, B. K.C.Dukka

引用次数: 3

Supporting HIV literature screening with data sampling and supervised learning 通过数据抽样和监督学习支持艾滋病文献筛选

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359733

Hayda Almeida, Marie-Jean Meurs, Leila Kosseim, A. Tsang

{"title":"Supporting HIV literature screening with data sampling and supervised learning","authors":"Hayda Almeida, Marie-Jean Meurs, Leila Kosseim, A. Tsang","doi":"10.1109/BIBM.2015.7359733","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359733","url":null,"abstract":"This paper presents a supervised learning approach to support the screening of HIV literature. The manual screening of biomedical literature is an important task in the process of systematic reviews. Researchers and curators have the very demanding, time-consuming and error-prone task of manually identifying documents that must be included in a systematic review concerning a specific problem. We implemented a supervised learning approach to support screening tasks, by automatically flagging potentially selected documents in a list retrieved by a literature database search. To overcome the main issues associated with the automatic literature screening task, we evaluated the use of data sampling, feature combinations, and feature selection methods, generating a total of 105 classification models. The models yielding best results were composed by the Logistic Model Trees classifier, a fairly balanced training set, and feature combination of Bag-Of-Words and MeSH terms. According to our results, the system correctly labels the great majority of relevant documents, and it could be used to support HIV systematic reviews to allow researchers to assess a greater number of documents in less time.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"24 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":"133425509","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

Accelerating large-scale biological database search on Xeon Phi-based neo-heterogeneous architectures 在基于Xeon phi的新异构架构上加速大规模生物数据库搜索

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359735

Haidong Lan, Weiguo Liu, B. Schmidt, Bingqiang Wang

{"title":"Accelerating large-scale biological database search on Xeon Phi-based neo-heterogeneous architectures","authors":"Haidong Lan, Weiguo Liu, B. Schmidt, Bingqiang Wang","doi":"10.1109/BIBM.2015.7359735","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359735","url":null,"abstract":"In this paper we present new parallelization techniques for searching large-scale biological sequence databases with the Smith-Waterman algorithm on Xeon Phi-based neoheterogenous architectures. In order to make full use of the compute power of both the multi-core CPU and the many-core Xeon Phi hardware, we use a collaborative computing scheme as well as hybrid parallelism. At the CPU side, we employ SSE intrinsics and multi-threading to implement SIMD parallelism. At the Xeon Phi side, we use Knights Corner vector instructions to gain more data parallelism. We have presented two dynamic task distribution schemes (thread level and device level) in order to achieve better load balancing. Furthermore, a multi-threaded asynchronous scheme is used to overlap communication and computation between CPUs and Xeon Phis. Evaluations on real protein sequence databases show that our method achieves a peak overall performance up to 220 GCUPS on a neo-heterogeneous platform consisting of two Intel E5-2620 CPUs and two Intel Xeon Phi 7110P cards. It also exhibits good scalability in terms of database size and query length. Our implementation is available at: http://turbo0628.github.io/LSBDS/.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"37 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":"122450048","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: 12

Feature selection and classification of speech under long-term stress 长期应激下语音特征选择与分类

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359804

B. Hu, Zhenyu Liu, Lihua Yan, Tian-Zhong Wang, Fei Liu, Xiaoyu Li, Huanyu Kang

引用次数: 2

Usability testing of a novel automated external defibrillator user interface: A pilot study 一种新型自动体外除颤器用户界面的可用性测试:初步研究

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359895

R. Bond, P. O'Hare, R. D. Maio

引用次数: 3

Boosting compound-protein interaction prediction by deep learning 通过深度学习促进化合物-蛋白质相互作用预测

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359651

Kai Tian, Mingyu Shao, Shuigeng Zhou, J. Guan

{"title":"Boosting compound-protein interaction prediction by deep learning","authors":"Kai Tian, Mingyu Shao, Shuigeng Zhou, J. Guan","doi":"10.1109/BIBM.2015.7359651","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359651","url":null,"abstract":"The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in many applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"50 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":"117278296","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: 138

Learning to rank for biomedical information retrieval 学习为生物医学信息检索排序

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359729

Bo Xu, Hongfei Lin, Yuan Lin, Yunlong Ma, Liang Yang, Jian Wang, Zhihao Yang

{"title":"Learning to rank for biomedical information retrieval","authors":"Bo Xu, Hongfei Lin, Yuan Lin, Yunlong Ma, Liang Yang, Jian Wang, Zhihao Yang","doi":"10.1109/BIBM.2015.7359729","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359729","url":null,"abstract":"Research articles in biomedicine domain have increased exponentially, which makes it more and more difficult for biologists to manually capture all the information they need. Information retrieval technologies can help to obtain the users' needed information automatically. However, it is a great challenge to apply these technologies to biomedicine domain directly because of some domain specific characteristics, such as the abundance of terminologies. To enhance the effectiveness of the biomedical information retrieval, we propose a novel framework based on the state-of-the-art information retrieval methods, called learning to rank, which has been proved effective to rank documents based on their relevance degree. In the framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate our proposed framework is effective in improving the performance of biomedical information retrieval.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"22 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":"115268238","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