Proceedings of the 5th International Conference on Bioinformatics and Computational Biology最新文献

Circadian rhythm of brain activity estimated using visual synchronization tasks: relations between brain activity and living activity 使用视觉同步任务估计大脑活动的昼夜节律:大脑活动和生活活动之间的关系

Proceedings of the 5th International Conference on Bioinformatics and Computational Biology Pub Date : 2017-01-06 DOI: 10.1145/3035012.3035028

K. Aoki

引用次数: 1

Comparison of standardization approaches applied to metabolomics data 代谢组学数据标准化方法的比较

Proceedings of the 5th International Conference on Bioinformatics and Computational Biology Pub Date : 2017-01-06 DOI: 10.1145/3035012.3035023

Qingxia Yang, Bo Li, Feng Zhu

引用次数: 1

A new focusing excitation method based on magnetic induction tomography 基于磁感应层析成像的聚焦激励新方法

Proceedings of the 5th International Conference on Bioinformatics and Computational Biology Pub Date : 2017-01-06 DOI: 10.1145/3035012.3035025

Yi Lv, Haijun Luo

引用次数: 0

Classification of multi-class microarray datasets using a minimizing class-overlapping based ECOC algorithm 基于最小化类重叠的ECOC算法的多类微阵列数据集分类

Proceedings of the 5th International Conference on Bioinformatics and Computational Biology Pub Date : 2017-01-06 DOI: 10.1145/3035012.3035018

Haiyue Yu, Kunhong Liu

{"title":"Classification of multi-class microarray datasets using a minimizing class-overlapping based ECOC algorithm","authors":"Haiyue Yu, Kunhong Liu","doi":"10.1145/3035012.3035018","DOIUrl":"https://doi.org/10.1145/3035012.3035018","url":null,"abstract":"The classification of multi-class microarray datasets is much more difficult compared with the binary datasets because the former usually consist of unbalanced data with a smaller sample size in each class. Our paper focuses on the multi-class problem, and proposes a new method based on a class-overlapping measure, named as Minimum Class-Overlapping Error-Correcting Output Codes (MCO-ECOC). In this algorithm, important variables are selected through different filter methods firstly. Then, the class overlapping is measured in training sets, the algorithm searches all class splitting schemes, and select the one minimizing the class-overlapping measure. Each column of the coding matrix represents such a splitting scheme. And then all the coding matrixs are combined by eliminating the redundant columns to make the final ensemble system compact. Neural networks are used as binary classifiers. MCO-ECOC algorithm is applied to classify the different multi-class microarray datasets, and the output of each base learner are fused to produce the final decision based on the Hamming distance. The experimental results show that the performance of MCO-ECOC is significantly higher than those obtained by DECOC and Forest ECOC.","PeriodicalId":130142,"journal":{"name":"Proceedings of the 5th International Conference on Bioinformatics and Computational Biology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129459163","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: 8

A fast microbial detection algorithm based on high-throughput sequencing data 基于高通量测序数据的微生物快速检测算法

Proceedings of the 5th International Conference on Bioinformatics and Computational Biology Pub Date : 2017-01-06 DOI: 10.1145/3035012.3035014

Jiangyu Li, Xiaolei Wang, Dongsheng Zhao, Yiqing Mao, Qian Cheng

{"title":"A fast microbial detection algorithm based on high-throughput sequencing data","authors":"Jiangyu Li, Xiaolei Wang, Dongsheng Zhao, Yiqing Mao, Qian Cheng","doi":"10.1145/3035012.3035014","DOIUrl":"https://doi.org/10.1145/3035012.3035014","url":null,"abstract":"Objective Design a rapid microbial detection algorithm that analyzes the sequencing data while sequencing, which can improve the speed of pathogenic microbial detection. Method A 'analysis while sequencing' method is used to analyze the sequencing data, the method uses the detection algorithm to analyze the sequencing data generated by the sequencer periodically. The reference microbial genomes are grouped. For each group the algorithm extracts sequencing reads mapped to the microbial genomes and then filters the human genome data, then the algorithm assembles the reads left and aligns the assembled contigs to the microbial genomes. Result For the simulated data, the new algorithm achieves speedup of 10 compared with RINS when the microorganisms in the sample have ref-genomes, both results are consistent and the new algorithm gets longer contigs. The new algorithm achieves an average speedup of 9 with no ref-genomes, and it obtains more complete sequence. For the real data, the new algorithm achieves an average speedup of 3 compared with RINS, and both detection results are the same. When verifying the Sequencing-by-side method, a reliable result can be obtained with a certain scale of sequencing data. Conclusion The 'analysis while sequencing' methods can improve pathogen detection speed, and have good application prospects.","PeriodicalId":130142,"journal":{"name":"Proceedings of the 5th International Conference on Bioinformatics and Computational Biology","volume":"244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123100426","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

Extrapolation-accelerated aggregation multigrid with block relaxations for GeneRank problems GeneRank问题的块松弛外推加速聚合多网格

Proceedings of the 5th International Conference on Bioinformatics and Computational Biology Pub Date : 2017-01-06 DOI: 10.1145/3035012.3035024

Zhao-Li Shen, Tingzhu Huang, Chun Wen, Hong-Fan Zhang, B. Carpentieri

引用次数: 0

An image encryption algorithm based on compressed sensing and dual-tree complex wavelet transform 基于压缩感知和双树复小波变换的图像加密算法

Proceedings of the 5th International Conference on Bioinformatics and Computational Biology Pub Date : 2017-01-06 DOI: 10.1145/3035012.3035013

Jiwen Dong, Ziru Zhao, Hengjian Li

{"title":"An image encryption algorithm based on compressed sensing and dual-tree complex wavelet transform","authors":"Jiwen Dong, Ziru Zhao, Hengjian Li","doi":"10.1145/3035012.3035013","DOIUrl":"https://doi.org/10.1145/3035012.3035013","url":null,"abstract":"Combined image encryption with the process of compressed sensing (CS) which based on dual-tree complex wavelet transform, we came up with the image encryption algorithm which based on the theory of CS and double random phase encoding (DRPE). Firstly, adopting DT-CWT represent the image sparsely. It can enhance the image edge and texture, it also can attain directional feature information. Secondly, utilizing chaotic system to product chaos sequence as the measurement matrix on the process of CS. At the same time, the values of initial chaotic parameters are the keys. Finally, we selected DRPE on measurements to finish the process of secondary encryption. The values of seeds which are used to generate two random phase masks are the keys of secondary encryption. In the process of decryption, providing the correct values of keys can generate the chaotic measurement matrix and random phase masks to be used for decryption. Orthogonal matching pursuit (OMP) algorithm is employed to reconstruct the original image approximately. Compared with several existing algorithms, this encryption scheme can reconstruct a higher quality image. It has higher robustness and higher sensitivity to the initial keys.","PeriodicalId":130142,"journal":{"name":"Proceedings of the 5th International Conference on Bioinformatics and Computational Biology","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128699986","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

Three-dimensional geometric statistical model based on locating feature points of maximal curvature flow 基于最大曲率流特征点定位的三维几何统计模型

Proceedings of the 5th International Conference on Bioinformatics and Computational Biology Pub Date : 2017-01-06 DOI: 10.1145/3035012.3035016

Hui Yu, Wu Jun-Sheng, Y. Bin, Zhang Chen

引用次数: 1

A developed apriori algorithm based on frequent matrix 一种基于频繁矩阵的先验算法

Proceedings of the 5th International Conference on Bioinformatics and Computational Biology Pub Date : 2017-01-06 DOI: 10.1145/3035012.3035019

Kun Niu, Haizhen Jiao, Zhipeng Gao, Cheng Chen, Huiyang Zhang

引用次数: 7

Breast mass lesion classification in mammograms by transfer learning 基于迁移学习的乳房x光片肿块病变分类

Proceedings of the 5th International Conference on Bioinformatics and Computational Biology Pub Date : 2017-01-06 DOI: 10.1145/3035012.3035022

Fan Jiang, Hui Liu, Shaode Yu, Yaoqin Xie

引用次数: 68