2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第4页

Visualized analysis of incomplete TCM meridian conductance data 中医经络不完整数据的可视化分析

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822728

Jiamin Yuan, Jiachang Chen, Li Huang, Fuping Xu, Mary Yang, Shixing Yan, Guozheng Li, Zhimin Yang

引用次数: 3

Enlarging drug dictionary with semi-supervised learning for Drug Entity Recognition 基于半监督学习的药品实体识别扩充药品字典

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822818

Donghuo Zeng, Chengjie Sun, Lei Lin, Bingquan Liu

引用次数: 4

Analysis of transcriptome assembly pipelines for wheat 小麦转录组组装管道的分析

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822507

Natasha Pavlovikj, Kevin Begcy, S. Behera, Malachy T. Campbell, H. Walia, J. Deogun

引用次数: 0

Symmetrical rigid body parameterization for biomolecular structures 生物分子结构的对称刚体参数化

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822505

J. S. Kim, G. Chirikjian

引用次数: 1

A comprehensive (biological and computational) investigation on the role of microRNA::mRNA regulations performed in chronic obstructive pulmonary disease and lung cancer 对microRNA::mRNA调控在慢性阻塞性肺病和肺癌中的作用进行了全面的(生物学和计算)研究

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822669

Jingshan Huang, D. Dou, Jun She, A. Limper, Yanan Yang, Ping Yang

引用次数: 4

Top-k utility-based gene regulation sequential pattern discovery Top-k基于效用的基因调控序列模式发现

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822529

Morteza Zihayat, Heydar Davoudi, Aijun An

{"title":"Top-k utility-based gene regulation sequential pattern discovery","authors":"Morteza Zihayat, Heydar Davoudi, Aijun An","doi":"10.1109/BIBM.2016.7822529","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822529","url":null,"abstract":"Sequential pattern mining has been used in bioinformatics to discover frequent gene regulation sequential patterns based on time course microarray datasets. While mining frequent sequences are important in biological studies for disease treatment, to date, most of the approaches do not consider the importance of the genes with respect to a disease being studied when identifying gene regulation sequential patterns. In addition, they focus on the more general up/down effects of genes in a microarray dataset and do not take into account the various degrees of expression during the mining process. As a result, the current techniques return too many sequences which may not be informative enough for biologists to explore relationships between the disease and underlying causes encoded in gene regulation sequences. In this paper, we propose a utility model by considering both the importance of genes with respect to a disease and their degrees of expression levels under a biological investigation. Then, we design a new method, called TU-SEQ, for identifying top-k high utility gene regulation sequential patterns from a time-course microarray dataset. The evaluation results show that our approach can effectively and efficiently discover key patterns representing meaningful gene regulation sequential patterns in a time course microarray dataset.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"20 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":"130966671","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: 13

qPMS10: A randomized algorithm for efficiently solving quorum Planted Motif Search problem qPMS10:一种高效解决quorum种植Motif搜索问题的随机化算法

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822598

Peng Xiao, Soumitra Pal, S. Rajasekaran

{"title":"qPMS10: A randomized algorithm for efficiently solving quorum Planted Motif Search problem","authors":"Peng Xiao, Soumitra Pal, S. Rajasekaran","doi":"10.1109/BIBM.2016.7822598","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822598","url":null,"abstract":"Discovering patterns in biological sequences is very important to extract useful information from them. Motifs are crucial patterns that have numerous applications including the identification of transcription factors and their binding sites, composite regulatory patterns, similiarity between families of proteins, etc. Several models of motifs have been proposed in the literature. The (l, d)-motif model is one of these that has been studied widely. The (l, d)-motif search problem is also known as Planted Motif Search (PMS). The general problem of PMS has been proven to be NP-hard. In this paper, we present an elegant as well as efficient randomized algorithm, named qPMS10, to solve PMS. Currently, the best known algorithm for solving PMS is qPMS9 and it can solve challenging (l, d)-motif instances up to (28, 12) and (30, 13). qPMS9 is a deterministic algorithm. We provide a performance comparison of qPMS10 with qPMS9 on standard benchmark datasets. Both theoretical and empirical analysis demonstrate that our randomized algorithm outperforms the exsiting algorithms for solving PMS. Besides, the random sampling techniques we employ in our algorithm can also be extended to solve other motif search problems including Simple Motif Search (SMS) and Edit-distance based Motif Search (EMS). Furthermore, our algorithm can be parallelized efficiently and has the potential of yielding great speedups on multi-core machines.","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":"132815394","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

A computationally inferred regulatory heart aging model including post-transcriptional regulations 计算推断的包括转录后调控的调节心脏衰老模型

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822517

G. Politano, F. Logrand, M. Brancaccio, S. Carlo

引用次数: 1

Just-in-time expression of influential genes in the cellular networks of the malaria parasite Plasmodium falciparum during the red blood cycle 在红细胞循环过程中，疟疾寄生虫恶性疟原虫细胞网络中影响基因的及时表达

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822526

Xinran Yu, H. Zhang, T. Lilburn, Hong Cai, Jianying Gu, T. Korkmaz, Yufeng Wang

引用次数: 0

L21-iPaD: An efficient method for drug-pathway association pairs inference 一种有效的药物通路关联对推断方法

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822597

Dong-Qin Wang, C. Zheng, Ying-Lian Gao, Jin-Xing Liu, Sha-Sha Wu, J. Shang

{"title":"L21-iPaD: An efficient method for drug-pathway association pairs inference","authors":"Dong-Qin Wang, C. Zheng, Ying-Lian Gao, Jin-Xing Liu, Sha-Sha Wu, J. Shang","doi":"10.1109/BIBM.2016.7822597","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822597","url":null,"abstract":"Pathway-based drug discovery overcomes the disadvantages of the “one drug-one target” method, which aims to find the effective drugs to act on single targets. The current method “iPaD” identities the drug-pathway association pairs by taking the lasso-type penalty on the drug-pathway association matrix. In order to enhance the robustness of the methods and be more effective to find the novel drug-pathway association pairs, we introduce a new method named “L2,1-iPaD”. Compared with the iPaD method, we impose the L2,1-norm constraint on the drug-pathway association coefficient matrix. By applying our method to a real widely datasets (CCLE dataset), we demonstrate that our method is superior to the iPaD method. And our method can obtain the smaller P-values than the iPaD method by performing permutation test to assess the significance of the identified drug-pathway association pairs. More importantly, compared with the iPaD method, our method can identify larger numbers of validated drug-pathway association pairs. The experimental results on the real dataset demonstrate the effectiveness of our method.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"259 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":"134474035","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