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6th international conference on Information Technology in Bio- and Medical Informatics, ITBAM 2015

作者： Lu, Jing Hales, Alan Rew, David Keech, Malcolm Fröhlingsdorf, Christian Mills-Mullett, Alex Wette, Christian Southampton Solent University SouthamptonSO14 0YN United Kingdom University Hospital Southampton SouthamptonSO16 6YD United Kingdom University of Bedfordshire Park SquareLutonLU1 3JU United Kingdom

ISBN: (纸本)9783319227405

this paper presents a case study of using data mining techniques in the analysis of diagnosis and treatment events related to Breast Cancer disease. Data from over 16,000 patients has been pre-processed and several data mining techniques have been implemented by using Weka (Waikato Environment for Knowledge Analysis). In particular, Generalized Sequential Patterns mining has been used to discover frequent patterns from disease event sequence profiles based on groups of living and deceased patients. Furthermore, five models have been evaluated in Classification with the objective to classify the patients based on selected attributes. this research showcases the data mining process and techniques to transform large amounts of patient data into useful information and potentially valuable patterns to help understand cancer outcomes. © Springer international Publishing Switzerland 2015.

关键词： Data mining

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6th international conference on Pattern Recognition and Machine Intelligence, PReMI 2015

6th International Conference on Pattern Recognition and Mach...

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6th international conference on Pattern Recognition and Machine Intelligence, PReMI 2015

the proceedings contain 55 papers. the special focus in this conference is on Pattern Recognition and Machine Intelligence. the topics include: Recent Advances in Recommender Systems and Future Directions;On the Number of Rules and Conditions in Mining Data with Attribute-Concept Values and "Do Not Care" Conditions;Simplifying Contextual Structures;Towards a Robust Scale Invariant Feature Correspondence;Hierarchical Agglomerative Method for Improving NPS;A New Linear Discriminant Analysis Method to Address the Over-Reducing Problem;Procedural Generation of Adjustable Terrain for Application in Computer Games Using 2D Maps;Fixed Point Learning Based 3D Conversion of 2D Videos;Fast and Accurate Foreground Background Separation for Video Surveillance;Enumeration of Shortest Isothetic Paths Inside a Digital Object;Modified Exemplar-Based Image Inpainting via Primal-Dual Optimization;A Novel Approach for Image Super Resolution Using Kernel methods;Generation of Random Triangular Digital Curves Using Combinatorial Techniques;Tackling Curse of Dimensionality for Efficient Content Based Image Retrieval;Face Profile View Retrieval Using Time of Flight Camera Image Analysis;Context-Based Semantic Tagging of Multimedia Data;Improved Simulation of Holography Based on Stereoscopy and Face Tracking;Head Pose Tracking from RGBD Sensor Based on Direct Motion Estimation;A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine;Modified Orthogonal Neighborhood Preserving Projection for Face Recognition;An Optimal Greedy Approximate Nearest Neighbor Method in Statistical Pattern Recognition.

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Label propagation for question classification in cqa 6th

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6th international conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Chen, Jun Su, Lei Li, Yiyang Shu, Peng School of Information Engineering and Automation Kunming University of Science and Technology Kunming650051 China

ISBN: (纸本)9783319204710

Questions in Community question answering (CQA) consisting of some labeled questions and numerous unlabeled questions are so complex and irregular. therefore, question classification in CQA has become the research hotspot in recent years. In this paper, we propose to classify the questions in CQA through the label propagation algorithm (LPA) based on the concept of graph, where nodes represent the labeled and unlabeled sample questions and edges represent the distance between the sample questions, through the node label propagation to realize question classification. Experiments on corpuses from "Baidu Knows", the accuracy in question classification through the LPA is not only higher than that through the KNN algorithm and SVM algorithm that have applied the labeled samples, but also higher than that through the SVM-based Bootstrapping algorithm that has utilized the labeled and unlabeled samples. © Springer international Publishing Switzerland 2015.

关键词： Graphic methods

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Analysis of ampliseq RNA-sequencing enrichment panels 6th

Analysis of ampliseq RNA-sequencing enrichment panels

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6th international conference on Pattern Recognition and Machine Intelligence, PReMI 2015

作者： Wiewiorka, Marek S. Szabelska, Alicja Okoniewski, Michal J. Institute of Computer Science Warsaw University of Technology Warsaw Poland Department of Mathematical and Statistical Methods Poznan University of Life Sciences Poznan Poland Zurich Switzerland

this study presents a proof of concept of encoding genomic signatures in the AmpliSeq technology. the samples of patients with a disease and healthy ones have been processed using an AmpliSeq RNA sequencing kit of a custom design, that include 290 amplicons, sequenced using an IonTorrent machine. the read count data show the sufficient coverage in most of the chosen amplicons, which results in a good separability between the disease patients and healthy donors. In addition, several amplicons allow for checking useful genomics variants (SNPs), whenever the coverage level permits. the paper presents a machinelearning classifier evaluation of the answer to the question of difference between the patients and healthy donors, based upon the AmpliSeq panel data. the outcome confirms the potential utility of similar RNA amplicon kits in the research and clinical practice to encode gene expression signatures of diseases and their phenotypes. © Springer international Publishing Switzerland 2015.

关键词： Genome

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A novel approach for image super resolution using kernel methods 6th

A novel approach for image super resolution using kernel met...

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6th international conference on Pattern Recognition and Machine Intelligence, PReMI 2015

作者： Prasoon, Adhish Chaubey, Himanshu Gupta, Abhinav Garg, Rohit Chaudhury, Santanu Samsung Research and Development Institute Delhi Noida India Department of Electrical Engineering Indian Institute of Technology Delhi Delhi India

We present a learning based method for image super resolution problem. Our approach uses kernel methods to build an efficient representation and also to learn the regression model. For constructing an efficient set of features, we apply Kernel Principal Component Analysis (Kernel-PCA) with a Gaussian kernel on a patch based data-base constructed from 69 training images up-scaled using bi-cubic interpolation. these features were given as input to a non-linear Support Vector Regression (SVR) model, with Gaussian kernel, to predict the pixels of the high resolution image. the model selection for SVR was performed using grid search. We tested our algorithm on an unseen data-set of 13 images. Our method out-performed a state-of the-art method and achieved an average of 0.92 dB higher Peak signal-to-noise ratio (PSNR). the average improvement in PSNR over bi-cubic interpolation was found to be 3.38 dB. © Springer international Publishing Switzerland 2015.

关键词： Principal component analysis

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Plant photosynthesis phenomics data quality control

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bioinformatics 2015年第11期31卷 1796-1804页

作者： Xu, Lei Cruz, Jeffrey A. Savage, Linda J. Kramer, David M. Chen, Jin Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA Michigan State Univ Dept Energy Plant Res Lab E Lansing MI 48824 USA Michigan State Univ Dept Biochem & Mol Biol E Lansing MI 48824 USA

Motivation: Plant phenomics, the collection of large-scale plant phenotype data, is growing exponentially. the resources have become essential component of modern plant science. Such complex datasets are critical for understanding the mechanisms governing energy intake and storage in plants, and this is essential for improving crop productivity. However, a major issue facing these efforts is the determination of the quality of phenotypic data. Automated methods are needed to identify and characterize alterations caused by system errors, all of which are difficult to remove in the data collection step and distinguish them from more interesting cases of altered biological responses. Results: As a step towards solving this problem, we have developed a coarse-to-refined model called dynamic filter to identify abnormalities in plant photosynthesis phenotype data by comparing light responses of photosynthesis using a simplified kinetic model of photosynthesis. Dynamic filter employs an expectation-maximization process to adjust the kinetic model in coarse and refined regions to identify both abnormalities and biological outliers. the experimental results show that our algorithm can effectively identify most of the abnormalities in both real and synthetic datasets.

关键词： algorithms models, Biological Phenotype Photosynthesis Plants/metabolism Quality Control

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A Robust Data Scaling Algorithm for Gene Expression Classification

A Robust Data Scaling Algorithm for Gene Expression Classifi...

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IEEE international conference on bioinformatics and Bioengineering

作者： Xi Hang Cao Zoran Obradovic Center for Data Analytics and Biomedical Informatics Department of Computer and Information Sciences College of Science and Technology Temple University

ISBN: (纸本)9781467379847

Gene expression data are widely used in classification tasks for medical diagnosis. Data scaling is recommended and helpful for learning the classification models. In this study, we propose a data scaling algorithm to transform the data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative density function of the data. the proposed algorithm is robust to outliers, and experimental results show that models learned using data scaled by the proposed algorithm generally outperform the ones using min-max mapping and z-score which are currently the most commonly used data scaling algorithms.

关键词： Logistics Data models Approximation algorithms Robustness Density functional theory Approximation methods Random variables

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Efficient Stochastic methods: Profiled Attacks Beyond 8 Bits 13

Efficient Stochastic Methods: Profiled Attacks Beyond 8 Bits

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13th international conference on Smart Card Research and Advanced Applications, CARDIS 2014

作者： Choudary, Marios O. Kuhn, Markus G. Computer Laboratory University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9783319167626

Template attacks and stochastic models are among the most powerful side-channel attacks. However, they can be computationally expensive when processing a large number of samples. Various compression techniques have been used very successfully to reduce the data dimensionality prior to applying template attacks, most notably Principal Component Analysis (PCA) and Fisher’s Linear Discriminant Analysis (LDA). these make the attacks more efficient computationally and help the profiling phase to converge faster. We show how these ideas can also be applied to implement stochastic models more efficiently, and we also show that they can be applied and evaluated even for more than eight unknown data bits at once. © Springer international Publishing Switzerland 2015.

关键词： Principal component analysis

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Optimizing production and distribution problem for agricultural products with type-2 fuzzy variables 6th

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6th international conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Bai, Xuejie Xu, Lijun College of Science Agricultural University of Hebei Baoding Hebei071001 China College of Management Hebei University Baoding Hebei071002 China Bureau of Animal Husbandry and Fisheries of Baoding Baoding Hebei071005 China

ISBN: (纸本)9783319204710

this paper focuses on generating the optimal solutions of the production and distribution for agricultural products under fuzzy environment, where the crop’s yield is characterized by type-2 fuzzy variable with known type-2 possibility distributions. In order to formulate the problem within the framework of the credibility optimization, we employ the possibility value-at-risk (VaR) reduction method to the type-2 fuzzy yield, and then reformulate the multi-fold fuzzy production and distribution problem as the chance constrained programming model. On the basis of the critical value formula for possibility value-at-risk reduced fuzzy variable, original fuzzy production and distribution model is converted into its equivalent parametric mixed integer programming form, which can be solved by general-purpose software. Numerical experiment is implemented to highlight the application of the fuzzy production and distribution model as well as the effectiveness of the solution method. © Springer international Publishing Switzerland 2015.

关键词： Numerical methods

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the Generating Function Approach for Peptide Identification in Spectral Networks

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18th Annual international conference on Research in Computational Molecular Biology (RECOMB)

作者： Guthals, Adrian Boucher, Christina Bandeira, Nuno Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA Univ Calif San Diego Skaggs Sch Pharm & Pharmaceut Sci La Jolla CA 92093 USA Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA

ISBN: (纸本)9783319052687

Tandem mass (MS/MS) spectrometry has become the method of choice for protein identification and has launched a quest for the identification of every translated protein and peptide. However, computational developments have lagged behind the pace of modern data acquisition protocols and have become a major bottleneck in proteomics analysis of complex samples. As it stands today, attempts to identify MS/MS spectra against large databases (e.g., the human microbiome or 6-frame translation of the human genome) face a search space that is 10-100 times larger than the human proteome, where it becomes increasingly challenging to separate between true and false peptide matches. As a result, the sensitivity of current state-of-the-art database search methods drops by nearly 38% to such low identification rates that almost 90% of all MS/MS spectra are left as unidentified. We address this problem by extending the generating function approach to rigorously compute the joint spectral probability of multiple spectra being matched to peptides with overlapping sequences, thus enabling the confident assignment of higher significance to overlapping peptide-spectrum matches (PSMs). We find that these joint spectral probabilities can be several orders of magnitude more significant than individual PSMs, even in the ideal case when perfect separation between signal and noise peaks could be achieved per individual MS/MS spectrum. After benchmarking this approach on a typical lysate MS/MS dataset, we show that the proposed intersecting spectral probabilities for spectra from overlapping peptides improve peptide identification by 30-62%.

关键词： databases statistical models probability algorithms computational molecular biology

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