检索结果-内蒙古大学图书馆

12th international Joint conference on Biomedical Engineering Systems and Technologies / 10th international conference on bioinformatics models, methods and algorithms (bioinformatics)

作者： Shibuya, Yoshihiro Comin, Matteo Univ Padua Dept Informat Engn Via Gradenigo 6B Padua Italy Univ Paris Est Marne la Vallee Lab Informat Gaspard Monge LIGM Batiment Copernic 5Bd Descartes Champs Sur Marne France

ISBN: (纸本)9789897583537

Many bioinformatics tools heavily rely on k-mer dictionaries to describe the composition of sequences and allow for faster reference-free algorithms or look-ups. Unfortunately, naive k-mer dictionaries are very memory inefficient, requiring very large amount of storage space to save each k-mer. this problem is generally worsened by the necessity of an index for fast queries. In this work we discuss how to build an indexed linear reference containing a set of input k-mers, and its application to the compression of quality score in FASTQ files. Most of the entropy of sequencing data lies in the quality scores, and thus they are difficult to compress. Here, we present an application to improve the compressibility of quality values while preserving the information for SNPs calling. We show how a dictionary of significant k-mers, obtained from SNPs databases and multiple genomes, can be indexed in linear space and used to improve the compression of quality value.

关键词： k-mers Indexing Quality Score Read Compression

来源：评论

学校读者我要写书评

暂无评论

Learning More Expressive Joint Distributions in Multimodal Variational methods 6th

Learning More Expressive Joint Distributions in Multimodal V...

引用

6th international conference on Machine Learning, Optimization, and Data Science, LOD 2020

作者： Nedelkoski, Sasho Bogojeski, Mihail Kao, Odej Distributed Systems TU Berlin Berlin Germany Machine Learning TU Berlin Berlin Germany

ISBN: (纸本)9783030645823

Data often are formed of multiple modalities, which jointly describe the observed phenomena. Modeling the joint distribution of multimodal data requires larger expressive power to capture high-level concepts and provide better data representations. However, multimodal generative models based on variational inference are limited due to the lack of flexibility of the approximate posterior, which is obtained by searching within a known parametric family of distributions. We introduce a method that improves the representational capacity of multimodal variational methods using normalizing flows. It approximates the joint posterior with a simple parametric distribution and subsequently transforms into a more complex one. through several experiments, we demonstrate that the model improves on state-of-the-art multimodal methods based on variational inference on various computer vision tasks such as colorization, edge and mask detection, and weakly supervised learning. We also show that learning more powerful approximate joint distributions improves the quality of the generated samples. the code of our model is publicly available at https://***/SashoNedelkoski/BPFDMVM. © 2020, Springer Nature Switzerland AG.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Using LSTM Neural Networks as Resource Utilization Predictors: the Case of Training Deep Learning models on the Edge 17th

Using LSTM Neural Networks as Resource Utilization Predictor...

引用

17th international conference on Economics of Grids, Clouds, Systems, and Services, GECON 2020

作者： Violos, John Psomakelis, Evangelos Danopoulos, Dimitrios Tsanakas, Stylianos Varvarigou, theodora School of Electrical and Computer Engineering National Technical University of Athens Zografou15771 Greece Dept. of Informatics and Telematcs Harokopio University of Athens Tavros Greece

ISBN: (纸本)9783030630577

Cloud and Fog technologies are steadily gaining momentum and popularity in the research and industry circles. Both communities are wondering about the resource usage. the present work aims to predict the resource usage of a machine learning application in an edge environment, utilizing Raspberry Pies. It investigates various experimental setups and machine learning methods that are acting as benchmarks, allowing us to compare the accuracy of each setup. We propose a prediction model that leverages the time series characteristics of resource utilization employing an LSTM Recurrent Neural Network (LSTM-RNN). To conclude to a close to optimal LSTM-RNN architecture we use a genetic algorithm. For the experimental evaluation we used a real dataset constructed by training a well known model in Raspberry Pies3. the results encourage us for the applicability of our method. © 2020, Springer Nature Switzerland AG.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Property-Based Testing for Parameter Learning of Probabilistic Graphical models 4th

Property-Based Testing for Parameter Learning of Probabilist...

引用

4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 international Cross-Domain conference for Machine Learning and Knowledge Extraction, CD-MAKE 2020

作者： Saranti, Anna Taraghi, Behnam Ebner, Martin Holzinger, Andreas Medical University Graz Auenbruggerplatz 2 Graz8036 Austria xAI Lab Alberta Machine Intelligence Institute EdmontonT6G 2H1 Canada Department Educational Technology Graz University of Technology Münzgrabenstrasse 36/I Graz8010 Austria

ISBN: (纸本)9783030573201

Code quality is a requirement for successful and sustainable software development. the emergence of Artificial Intelligence and data driven Machine Learning in current applications makes customized solutions for both data as well as code quality a requirement. the diversity and the stochastic nature of Machine Learning algorithms require different test methods, each of which is suitable for a particular method. Conventional unit tests in test-automation environments provide the common, well-studied approach to tackle code quality issues, but Machine Learning applications pose new challenges and have different requirements, mostly as far the numerical computations are concerned. In this research work, a concrete use of property-based testing for quality assurance in the parameter learning algorithm of a probabilistic graphical model is described. the necessity and effectiveness of this method in comparison to unit tests is analyzed with concrete code examples for enhanced retraceability and interpretability, thus highly relevant for what is called explainable AI. © 2020, IFIP international Federation for Information Processing.

关键词： Quality assurance

来源：评论

学校读者我要写书评

暂无评论

High dimensional model representation of log likelihood ratio: binary classification with SNP data

引用

BMC MEDICAL GENOMICS 2020年第9期13卷 1-22页

作者： Pour, Ali Foroughi Pietrzak, Maciej Sucheston-Campbell, Lara E. Karaesmen, Ezgi Dalton, Lori A. Rempala, Grzegorz A. Ohio State Univ Dept Elect & Comp Engn 2015 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Math 231 West 18th Ave Columbus OH 43210 USA Math Biosci Inst 1735 Neil Ave Columbus OH 43210 USA Ohio State Univ Coll Publ Hlth 1841 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat 1585 Neil Ave Columbus OH 43210 USA Ohio State Univ Coll Pharm 500 West 12th Ave Columbus OH 43210 USA

Background Developing binary classification rules based on SNP observations has been a major challenge for many modern bioinformatics applications, e.g., predicting risk of future disease events in complex conditions such as cancer. Small-sample, high-dimensional nature of SNP data, weak effect of each SNP on the outcome, and highly non-linear SNP interactions are several key factors complicating the analysis. Additionally, SNPs take a finite number of values which may be best understood as ordinal or categorical variables, but are treated as continuous ones by many algorithms. methods We use the theory of high dimensional model representation (HDMR) to build appropriate low dimensional glass-box models, allowing us to account for the effects of feature interactions. We compute the second order HDMR expansion of the log-likelihood ratio to account for the effects of single SNPs and their pairwise interactions. We propose a regression based approach, calledlinear approximation for block second order HDMR expansion of categorical observations(LABS-HDMR-CO), to approximate the HDMR coefficients. We show how HDMR can be used to detect pairwise SNP interactions, and propose thefixed pattern test(FPT) to identify statistically significant pairwise interactions. Results We apply LABS-HDMR-CO and FPT to synthetically generated HAPGEN2 data as well as to two GWAS cancer datasets. In these examples LABS-HDMR-CO enjoys superior accuracy compared with several algorithms used for SNP classification, while also taking pairwise interactions into account. FPT declares very few significant interactions in the small sample GWAS datasets when bounding false discovery rate (FDR) by 5%, due to the large number of tests performed. On the other hand, LABS-HDMR-CO utilizes a large number of SNP pairs to improve its prediction accuracy. In the larger HAPGEN2 dataset FTP declares a larger portion of SNP pairs used by LABS-HDMR-CO as significant. Conclusion LABS-HDMR-CO and FPT are interestin

关键词： Single nucleotide polymorphism Binary classification High dimensional model representation Pairwise SNP interactions Log likelihood ratio

来源：评论

学校读者我要写书评

暂无评论

Sequential monte carlo inference based on activities for overlapping community models 1

引用

6th international Workshop on Mining Ubiquitous and Social Environments, MUSE 2015, held in conjunction with the 6th European conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2015, 6th international Workshop on Modeling Social Media, MSM 2015, held in conjunction with the 24th international World Wide Web conference, WWW 2015 and 7th international Workshop on Modeling Social Media, MSM 2016, held in conjunction with the 25th international World Wide Web conference, WWW 2016

作者： Sakamoto, Shohei Eguchi, Koji Kobe University Kobe657-8501 Japan Hiroshima University Higashi-Hiroshima739-8527 Japan

ISBN: (数字)9783030344078

ISBN: (纸本)9783030339067

Various kinds of data such as social media can be represented as a network or graph. Latent variable models using Bayesian statistical inference are powerful tools to represent such networks. One such latent variable network model is a Mixed Membership Stochastic Blockmodel (MMSB), which can discover overlapping communities in a network and has high predictive power. Previous inference methods estimate the latent variables and unknown parameters of the MMSB on the basis of the whole observed network. therefore, dynamic changes in network structure over time are hard to track. thus, we first present an incremental Gibbs sampler based on node activities that focuses only on observations within a fixed term length for online sequential estimation of the MMSB. We further present a particle filter based on node activities with various term lengths. For instance, in an e-mail communication network, each particle only considers e-mail accounts that sent or received a message within a specific term length, where the length may be different from those of other particles. We show through experiments with two link prediction datasets that our proposed methods achieve both high prediction performance and computational efficiency. © Springer Nature Switzerland AG 2019.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

On-line Searching in IUPAC Nucleotide Sequences 10

On-line Searching in IUPAC Nucleotide Sequences

引用

12th international Joint conference on Biomedical Engineering Systems and Technologies / 10th international conference on bioinformatics models, methods and algorithms (bioinformatics)

作者： Prochazka, Petr Holub, Jan Czech Tech Univ Fac Informat Technol Dept Theoret Comp Sci Thakurova 2700-9 Prague 6 Czech Republic

ISBN: (纸本)9789897583537

We propose a novel pattern matching algorithm for consensus nucleotide sequences over IUPAC alphabet, called BADPM (Byte-Aligned Degenerate Pattern Matching). the consensus nucleotide sequences represent a consensus obtained by sequencing a population of the same species and they are considered as so-called degenerate strings. BADPM works at the level of single bytes and it achieves sublinear search time on average. the algorithm is based on tabulating all possible factors of the searched pattern. It needs O (m + m alpha(2) logm)space data structure and O (m alpha(2)) time for preprocessing where m is a length of the pattern and a represents a maximum number of variants implied from a 4-gram over IUPAC alphabet. the worst-case locate time is bounded by O (nm(2)alpha(4)) for BADPM where n is the length of the input text. However, the experiments performed on real genomic data proved the sublinear search time. BADPM can easily cooperate with the block q-gram inverted index and so achieve still better locate time. We implemented two other pattern matching algorithms for IUPAC nucleotide sequences as a baseline: Boyer-Moore-Horspool (BMH) and Parallel Naive Search (PNS). Especially PNS proves its efficiency insensitive to the length of the searched pattern m. BADPM proved its strong superiority for searching middle and long patterns.

关键词： Consensus Nucleotide Sequences Genomic Sequences Degenerate Pattern Matching q-gram Inverted Index

来源：评论

学校读者我要写书评

暂无评论

6th international Workshop on Mining Ubiquitous and Social Environments, MUSE 2015, held in conjunction with the 6th European conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2015, 6th international Workshop on Modeling Social Media, MSM 2015, held in conjunction with the 24th international World Wide Web conference, WWW 2015 and 7th international Workshop on Modeling Social Media, MSM 2016, held in conjunction with the 25th international World Wide Web conference, WWW 2016

6th International Workshop on Mining Ubiquitous and Social E...

引用

ISBN: (纸本)9783030339067

the proceedings contain 7 papers. the special focus in this conference is on Mining Ubiquitous and Social Environments. the topics include: Analyzing big data streams with apache SAMOA;Multimodal behavioral mobility pattern mining and analysis using topic modeling on GPS data;sequential monte carlo inference based on activities for overlapping community models;results of a survey about the perceived task similarities in micro task crowdsourcing systems;Provenance of explicit and implicit interactions on social media with W3C PROV-DM.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Visual SLAM Location methods Based on Complex Scenes: A Review 6th

Visual SLAM Location Methods Based on Complex Scenes: A Revi...

引用

6th international conference on Artificial Intelligence and Security, ICAIS 2020

作者： Zhang, Hanxiao Peng, Jiansheng School of Electrical and Information Engineering Guangxi University of Science and Technology LiuzhouGuangxi545000 China School of Physics and Mechanical and Electronic Engineering Hechi University YizhouGuangxi546300 China

ISBN: (纸本)9783030578800

In recent years, positioning for simple static scenes has been unable to meet the requirements of people’s production and life. People want to achieve accurate positioning in practical scenarios such as airports, exhibition halls and stations. therefore, the research on visual SLAM positioning in complex dynamic scenes is increasing day by day. this article reviews the research results of SLAM positioning methods and visual SLAM positioning methods for complex scenes in recent years. Firstly, the development process of laser SLAM, visual SLAM, semantic SLAM and multi-sensor fusion is introduced, but the focus is on visual SLAM. Secondly, the paper summarizes the methods of moving object detection and visual SLAM localization in complex dynamic scenes. then the paper describes the development of deep learning and multi-sensor fusion in visual SLAM positioning based on complex dynamic scenes. Finally, the shortcomings of visual SLAM positioning methods based on complex scenes are summarized and the research prospects are prospected. © 2020, Springer Nature Switzerland AG.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Improving the Identification of Co-occurring Driver Pathways by Integrating Multi-Omics Data

Improving the Identification of Co-occurring Driver Pathways...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Qirong Cai Jingli Wu Rui Du Zhen Zhang Ke Pan College of Computer Science Beijing Institute Of Technology Zhuhai China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

It is widely accepted that driver pathways offer important information for precision and personalized medicine of cancer treatment, hence the problems of driver pathways identification have become a research hotspot in bioinformatics. In this paper, an improved collaborative mutation driver pathways model ICMDP is proposed by integrating the somatic mutation, copy number variation and gene expression data. the model has two characteristics:(1) each individual pathway has moderate mutual exclusion and high coverage; (2) collaborative driver pathways exhibit significant common mutations in cancer samples, and the genes in collaborative driver pathways are related. Meanwhile, a parthenogenetic algorithm PA-ICMDP is proposed for solving the ICMDP model. Experiments were performed to compare algorithms PA-ICMDP, CoMDP and GAMTOC by using real biological data sets, i.e., the samples of glioblastoma and ovarian. the experimental results indicate that the PA-ICMDP algorithm can not only identify important collaborative driver pathways with higher co-occurrence mutation rates, but also detect more important driver genes such as MET, MDM2, GAB2, TERT, TBX3 and so on. In addition, the EICMDP model and the PA-EICMDP algorithm are put forward by extending the ICMDP model and the PAICMDP algorithm respectively. they can effectively identify other important pathways that collaborate with known driver pathways. the experimental results indicate that the methods presented in this paper may become suitable tools for mining driver genes and driver pathways related to cancer development.

关键词： Proteins Biological system modeling High performance computing Collaboration Tools Data models Gene expression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：