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PO-329 Rejecting neutral tumour evolution across human cancer types

ESMO Open 2018年 3卷 A357-A357页

作者： Tarabichi, M. Martincorena, I. Gerstung, M. Markowetz, F. Spellman, P. Morris, Q. Lingjærde, O.C. Wedge, D. Loo, P. Van Pcawg, W.G. The Francis Crick Institute Cancer Genomics London UK The Wellcome Trust Sanger Institute Inigo Martincorena lab Cambridge UK European Bioinformatics Institute EMBL – EBI- Wellcome Genome Campus Moritz Gerstung lab Cambridge UK CRUK Cancer Institute Markowetz lab Cambridge UK Oregon Health and Science University Spellman lab Portland USA Donnelly Centre for Cellular and Biomolecular Research- University of Toronto Morris lab Toronto Canada Centre for Computational Inference in Evolutionary Life Science- University of Oslo Department of statistics Oslo Norway Oxford Big Data Institute Wedge lab Oxford UK PCAWG PCAWG – working group 11 London UK

ABSTRACTIntroductionClusters of subclonal mutations attributed to subclonal expansions have been observed across most cancer types. To date it is not clear what fraction of these expansions can be ascribed to selective sweeps or genetic drifts. The widely held theory states that tumour expansion arises from a series of selective sweeps. However, Williams et al. (Nat. Genet. 48:238–224, 2016) recently claimed to have identified neutral tumour evolution in one third of 904 samples from The Cancer Genome Atlas (TCGA). Material and methodsFirst, using the same equations we simulated data of tumour undergoing selective sweeps. Second, we simulated data using branching processes, i.e. more realistic models. Third, we analysed the mutations from ~1600 exomes and ~1500 whole genomes of real tumours from TCGA and the International Cancer Genome Consortium, respectively. We grouped them into neutral and non-neutral tumours according to the rationale described by Williams et al. We then applied dN/dS, an orthogonal widely used approach to detect selection in non-synonymous mutations of coding regions. Finally, we ran dN/dS on subclonal mutations within individual cancer types. Results and discussionsApplication of the method described by Williams et al. led to over-calling of neutrality and led to random predictions (area under the curve~=50%) in simulations based on the deterministic or stochastic models, respectively. dN/dS analyses revealed significant and widespread positive selection in clonal and subclonal mutations within both neutral and non-neutral groups. Despite power limits, the subclonal mutations of many individual cancer types displayed dN/dS>1, evidence for positive selection. ConclusionAltogether, our results point to the lack of evidence for neutral tumour evolution and uncovered strong positive selection within subclonal mutations across cancers.

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A novel reversible watermarking scheme for relational databases protection based on histogram shifting

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Journal of Information Hiding and Multimedia Signal Processing 2016年第2期7卷 266-276页

作者： Ma, Yu-Jia Zhu, Yue-Sheng Liu, Xi-Yao Communication and Information Security Lab Institute of Big Data Technologies Shenzhen Graduate School Peking University Shenzhen China School of Information Science and Engineering Central South University Changsha China

The watermarking technique can be used to protect the ownership of relational databases by hiding some ownership information into the relational databases. In this paper, a novel robust reversible watermarking scheme for relational database protection is proposed to increase the embedding capacity by developing an improved histogram shifting with binary tree structure and an attribute selection scheme. The iteratively embedded watermarks are utilized by a majority voting mechanism to enhance the robustness of the proposed scheme. In addition, the attribute selection and cell selection are employed to reduce the distortion caused by the watermark embedding. The experimental results show that our proposed scheme can provide higher embedding capacity and stronger robustness with low distortion compared with other watermarking schemes for relational databases. © 2016.

关键词： Iterative methods

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Promoting Business Analytics and Quantitative Management of Technology: Preface for ITQM 2016

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Procedia Computer science 2016年 91卷 1-8页

作者： Lee, Heeseok Shi, Yong Lee, Jongwon Cordova, Felisa Dzitac, Ioan Kou, Gang Li, Jaiping College of Business Korea Advanced Institute Science and Technology Dongdaemum-gu Seoul130-722 Korea Republic of Research Center on Fictitiotus Economy and Data Science Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States School of Business Administration Hoseo University Asan-si Chungcheongnam-do336-795 Korea Republic of Faculty of Engineering and Business University Finis Terrae Pedro de Valdivia 1509. Providencia Santiago Chile Universitatea Agora Agora University Piata Tineretului 8 Oradea410526 Romania School of Business Administration Southwestern University of Finance and Economics Chengdu Sichuan61130 China Institute of Policy and Management Chinese Academy of Sciences Beijing100190 China

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QUasi-Affine TRansformation Evolutionary(QUATRE) Algorithm:the framework analysis for global optimization and application in hand gesture segmentation

QUasi-Affine TRansformation Evolutionary(QUATRE) Algorithm:t...

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Zhenyu Meng Jeng-Shyang Pan Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology

In this paper, a simple but powerful global optimization framework is discussed. The framework uses a affine-transformation-like form, so we name it QUasi-Affine TRansformation Evolutionary(QUATRE) algorithm. A general notation of QUATRE "QUATRE/B"("QUATRE/x/y") is given in earlier literature, which will appear in CEC2016 conference proceedings with the paper title "QUasi-Affine TRansformation Evolutionary(QUATRE) Algorithm: A Parameter-reduced Differential Evolution Algorithm for Optimization Problem".We'll give a thoroughly discussion of these variants herein the paper. An application for hand gesture segmentation is also given in the paper, and the performance shows that the QUATRE framework outperforms other swarm based optimization algorithms and it also tackles the pixel threshold tuning problem in hand gesture segmentation of a hand gesture remote control system very well.

关键词： Differential evolution global optimization hand gesture segmentation QUATRE

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Answering why-not questions on metric probabilistic range queries

Answering why-not questions on metric probabilistic range qu...

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International Conference on data Engineering

作者： Lu Chen Yunjun Gao Kai Wang Christian S. Jensen Gang Chen College of Computer Science Zhejiang University Hangzhou China The Key Lab of Big Data Intelligent Computing of Zhejiang Province Zhejiang University Hangzhou China Department of Computer Science Aalborg University Denmark

ISBN: (纸本)9781509020218

Metric probabilistic range queries (MPRQ) have received substantial attention due to their utility in multimedia and text retrieval, decision making, etc. Existing MPRQ studies generally aim to improve query efficiency and resource usage. In contrast, we define and offer solutions to why-not questions on MPRQ. Given an original metric probabilistic range query and a why-not set W of uncertain objects that are absent from the query result, a why-not question on MPRQ explains why the uncertain objects in W do not appear in the query result, and provides refinements of the original query and/or W with the minimal penalty, so that the uncertain objects in W appear in the result of the refined query. Specifically, we propose a framework that consists of three efficient solutions, one that modifies the original query, one that modifies the why-not set, and one that modifies both the original query and the why-not set. Extensive experiments using both real and synthetic data sets offer insights into the properties of the proposed algorithms, and show that they are effective and efficient.

关键词： Probabilistic logic Extraterrestrial measurements Uncertainty Computers databases Multimedia communication

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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3D-LSR: A New Model for Skeletal Representation and Segmentation

3D-LSR: A New Model for Skeletal Representation and Segmenta...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Di Zhang Xiaojuan Ban Di Wu Hao Wang Sch. of Comput. & Commun. Eng. University of Science and Technology Beijing Beijing Beijing CN Big Data Lab. Norwegian University of Science and Technology Ålesund Norway

ISBN: (纸本)9781509058815

Skeleton-based human action recognition with depth sensors have a renewed interest based on the development of popular depth sensors. This paper presents a new skeletal representation, Limb-based Skeleton Representation (LSR) that explicitly models 10 limbs as spherical coordinate in 3D space, and extract angular velocity feature based on the spherical coordinate representation. Using the Spherical Coordinate and Angular Velocity (SCAV) feature, human actions can be represented as spatial-temporal vector sequences. To address the classification challenge of vector sequences that can vary in duration and sometimes temporal ordering, we propose a Dynamic Segmentation Algorithm (DSA) and encode all the segments by orientations and spherical coordinates. We apply LSR and DSA on a standard UTD-MHAD human action dataset, in comparison with 3 other methods. The result shows the classification accuracy have been improved.

关键词： Skeleton Three-dimensional displays Angular velocity Heuristic algorithms Two dimensional displays Hidden Markov models Sensors

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Communication Cost Efficient Virtualized Network Function Placement for big data Processing

Communication Cost Efficient Virtualized Network Function Pl...

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IEEE Conference on Computer Communications Workshops

作者： Lin Gu Sheng Tao Deze Zeng Hai Jin Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science China University of Geosciences Wuhan Hubei China

ISBN: (纸本)9781467399562

The data explosion in the emerging big data era imposes a big burden on the network infrastructure. This vision has urged the evolution of computer networks. By softwarizing traditional dedicated hardware based functions to virtualized network function (VNF) that can run on standard commodity servers, network function virtualization (NFV) technology promises increased networking efficiency, flexibility and scalability. From the perspective of network service providers, with the consideration of big data traffic volume, one primary concern is on the communication cost, which is highly influenced by the VNF placement strategy. In this paper, we are motivated to investigate the issue on communication cost efficient VNF placement problem for big data processing, with joint consideration of network flow balancing and the predetermined network service semantics. We formulate this problem into a mixed-integer linear programming (MILP) form and then propose a low-complexity relaxation-based heuristic algorithm accordingly. The high efficiency of our proposal is validated by extensive simulation studies.

关键词： Noise measurement big data Distributed databases Algorithm design and analysis Servers Heuristic algorithms Semantics

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Self-Grouping Multi-network Clustering

Self-Grouping Multi-network Clustering

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IEEE International Conference on data Mining (ICDM)

作者： Jingchao Ni Wei Cheng Wei Fan Xiang Zhang Department of Electrical Engineering and Computer Science Case Western Reserve University NEC Laboratories America Baidu Research Big Data Lab College of Information Sciences and Technology Pennsylvania State University

ISBN: (纸本)9781509054749

Joint clustering of multiple networks has been shown to be more accurate than performing clustering on individual networks separately. Many multi-view and multi-domain network clustering methods have been developed for joint multi-network clustering. These methods typically assume there is a common clustering structure shared by all networks, and different networks can provide complementary information on this underlying clustering structure. However, this assumption is too strict to hold in many emerging real-life applications, where multiple networks have diverse data distributions. More popularly, the networks in consideration belong to different underlying groups. Only networks in the same underlying group share similar clustering structures. Better clustering performance can be achieved by considering such groups differently. As a result, an ideal method should be able to automatically detect network groups so that networks in the same group share a common clustering structure. To address this problem, we propose a novel method, ComClus, to simultaneously group and cluster multiple networks. ComClus treats node clusters as features of networks and uses them to differentiate different network groups. Network grouping and clustering are coupled and mutually enhanced during the learning process. Extensive experimental evaluation on a variety of synthetic and real datasets demonstrates the effectiveness of our method.

关键词： Clustering methods Tensile stress Measurement Symmetric matrices Conferences data mining Electrical engineering

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Exploring outliers in crowdsourced ranking for QoE

arXiv

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arXiv 2017年

作者： Xu, Qianqian Yan, Ming Huang, Chendi Xiong, Jiechao Huang, Qingming Yao, Yuan Institute of Information Engineering Cas Beijing100093 Department of Computational Mathematics Michigan State University East LansingMI48824 United States BICMR-LMAM-LMEQF-LMP School of Mathematical Sciences Peking University Beijing100871 Tencent Ai Lab Shenzhen518057 University of Chinese Academy of Sciences Beijing100049 China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Cas Beijing100190 Key Lab of Big Data Mining and Knowledge Management Cas Beijing100190 Department of Mathematics Hong Kong University of Science and Technology 100871 Hong Kong

Outlier detection is a crucial part of robust evaluation for crowd-sourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years. In this paper, we propose some simple and fast algorithms for outlier detection and robust QoE evaluation based on the nonconvex optimization principle. Several iterative procedures are designed with or without knowing the number of outliers in samples. Theoretical analysis is given to show that such procedures can reach statistically good estimates under mild conditions. Finally, experimental results with simulated and real-world crowdsourcing datasets show that the proposed algorithms could produce similar performance to Huber-LASSO approach in robust ranking, yet with nearly 8 or 90 times speed-up, without or with a prior knowledge on the sparsity size of outliers, respectively. Therefore the proposed methodology provides us a set of helpful tools for robust QoE evaluation with crowdsourcing data. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

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