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Towards understanding dominant processes in complex dynamical systems: Case of precipitation extremes

Towards understanding dominant processes in complex dynamica...

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6th International Workshop on Knowledge Discovery from Sensor data, SensorKDD'12 - Held in Conjunction with the 18th ACM SIGKDD Conference on Knowledge Discovery and data Mining, KDD 2012

作者： Das, Debasish Ganguly, Auroop Banerjee, Arindam Obradovic, Zoran Department of Civil and Environmental Engineering Northeastern University Boston MA United States Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA United States Computer Science and Engineering University of Minnesota Minneapolis MN United States

ISBN: (纸本)9781450315548

Complex dynamical systems like precipitation extremes under climate variability or change are typically governed by multiple processes at multiple scales. The processes themselves may be manifested at multiple scales and would need to be captured through key indicator variables, which in turn may be better projected by physical models than the variables of interest. We posit that hybrid approaches based on physically-motivated approaches and data-driven methods, which in turn are conditioned on both observations and simulations from large-scale physics-based models, may offer novel and quantifiable insights. The data-driven approaches may need to extend and adapt methods developed and tested in statistics, data mining and machine learning to the concept of dominant processes. In this paper, we performed some exploratory data analysis to characterize the effect of dominant processes on precipitation extremes, annually and seasonally, and from global and century scale to regional and decadal scale, and found some interesting insights that pointed towards need of improved understanding. We identified the gaps in understanding the regional drivers where data-driven methods can make useful improvements and eventually lead to a predictive model for precipitation extremes. Although we do not propose any specific method for solving the problem, we realize that any successful data mining solution should include all or a subset of tools like dimensionality reduction, grouped variable selection, non-linear regression and graphical models. The concepts of dominant processes proposed here would likely generalize broadly to climate extremes while the solution frameworks themselves may generalize beyond climate. Copyright 2012 ACM.

关键词： data mining

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Exploring genetic variability in drug therapy by selecting a minimum subset of the most informative single nucleotide polymorphisms through approximation of a markov blanket in a kernel-induced space

Exploring genetic variability in drug therapy by selecting a...

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2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012

作者： Lou, Qiang Parkman, Henry P. Jacobs, Michael R. Krynetskiy, Evgeny Obradovic, Zoran Center for Data Analytics and Biomedical Informatics Temple University Philadelphia United States Gastroenterology Section School of Medicine Temple University Philadelphia United States School of Pharmacy Temple University Philadelphia United States Jayne Haines Center for Pharmacogenomics and Drug Safety Temple University Philadelphia United States

ISBN: (纸本)9781467311892

Genome-wide analysis of single nucleotide polymorphisms (SNP) can potentially be helpful in exploring the role of genetic variability in drug therapy. However, two major problems with such an analysis are the need for a large number of interrogated genomes, and the resulting high-dimensional data where the number of SNPs used as features is much larger than the number of subjects. The aim of this study is to identify informative SNPs associated with clinical efficacy and side effects of domperidone treatment for gastroparesis from DNA microarray experiments by applying our feature selection method, which approximates the Markov Blanket in a kernel-induced space. DNA samples extracted from the saliva of 46 patients treated with domperidone were analyzed using Affymetrix 6.0 SNP microarrays. Experimental evaluations on this SNP microarray dataset provide evidence that our feature selection method can remove useless SNP features more accurately than existing Markov Blanket based alternatives. © 2012 IEEE.

关键词： Feature Selection

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Towards a DGSS Prototype for Early Warning for Ski Injuries

Towards a DGSS Prototype for Early Warning for Ski Injuries

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International Conference on data Engineering Workshops

作者： Boris Delibaic Zoran Obradovic Center for Business Decision-Making University of Belgrade Belgrade Serbia Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA USA

This paper reports on our ongoing research in ski injury analysis. We propose a decision guidance support system for early warning for ski injuries. We report several injury patterns in form of IF-THEN rules which could be used to build a decision guidance support system for early warning for ski injuries. The rules are induced from data merged from two databases, namely the Mountaineer rescue service database and the Ski gate entrances database. Around 2000 injuries from year 2006 to year 2011 were analyzed. Based on movement of skiers (recorded through ski gates on each lift) several statistics for each accident (e.g. average speed of skier, average weight of slopes) have been calculated.

关键词： Injuries databases Accidents Snow Logic gates Medical diagnostic imaging

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Semi-Supervised Learning on Single-View datasets by Integration of Multiple Co-trained Classifiers

Semi-Supervised Learning on Single-View Datasets by Integrat...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jelena Slivka Ping Zhang Aleksandar Kovacevic Zora Konjovic Zoran Obradovic Computing and Control Department University of Novi Sad Serbia Center for Data Analytics and Biomedical Informatics Temple University Philadelnhia PA USA

We propose a novel semi-supervised learning algorithm, called IMCC, designed for co-training classifiers on single-view datasets. Our method runs the co-training algorithm for a predefined number of times, each time using a different random split of features. Thus, a set of diverse co-training classifiers is created. Each of these classifiers then labels each of the examples for which we want to determine the class label. In this way, each example for classification is assigned multiple labels. We then treat this as a problem of learning from inconsistent and unreliable annotators in a multi-annotator problem setting and estimate the single hidden true label for each example. In experimental results obtained on 25 benchmark datasets of various properties IMCC outperformed five considered alternative methods for co-training on single-view datasets, and resulted in a statistical tie with a Naive Bayes classifier trained using a much larger set of labeled examples.

关键词： Training Classification algorithms Accuracy Estimation Semisupervised learning Algorithm design and analysis Sensitivity and specificity

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Early classification of multivariate time series using a hybrid HMM/SVM model

Early classification of multivariate time series using a hyb...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mohamed F. Ghalwash Dušan Ramljak Zoran Obradović Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA Mathematics Department Faculty of Science Ain Shams University Cairo Egypt

Early classification of time series has been receiving a lot of attention as of late, particularly in the context of gene expression. In the biomédical realm, early classification can be of tremendous help, by identifying the onset of a disease before it has time to fully take hold, or determining that a treatment has done its job and can be discontinued. In this paper we present a state-of-the-art model, which we call the Early Classification Model (ECM), that allows for early, accurate, and patient-specific classification of multivariate time series. The model is comprised of an integration of the widely-used HMM and SVM models, which, while not a new technique per se, has not been used for early classification of multivariate time series classification until now. It attained very promising results on the datasets we tested it on: in our experiments based on a published dataset of response to drug therapy in Multiple Sclerosis patients, ECM used only an average of 40% of a time series and was able to outperform some of the baseline models, which needed the full time series for classification.

关键词： Hidden Markov models Time series analysis Gene expression Support vector machines Accuracy Training data models

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A method for design of data-tailored partitioning algorithms for optimizing the number of clusters in microarray analysis

A method for design of data-tailored partitioning algorithms...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Milan Vukicevic Boris Delibasic Milos Jovanovic Milija Suknovic Zoran Obradovic Center for Business Decision-Making Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Center for Data Analytics and Biomedical Informatics CIST Temple University Philadelphia USA

We propose a method for designing a partitioning clustering algorithm from reusable components that is suitable for finding the appropriate number of clusters (K) in microarray data. The proposed method is evaluated on 10 datasets (4 syntetic and 6 real-word microarrays) by considering 1008 reusable-component-based algorithms and four normalization methods. The best performing algorithm were reported on every dataset and also rules were identified for designing microarray-specific clustering algorithms. The obtained results indicate that in the majority of cases a data-tailored clustering algorithm design outperforms the results reported in the literature. In addition, data normalization can have an important influence on algorithm performance. The method proposed in this paper gives insights for design of divisive clustering algorithms that can reveal the optimal K in a microarray dataset.

关键词： Clustering algorithms Algorithm design and analysis Partitioning algorithms Indexes Gene expression Principal component analysis

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SPATIALLY PENALIZED REGRESSION FOR DEPENDENCE ANALYSIS OF RARE EVENTS: A STUDY IN PRECIPITATION EXTREMES

SPATIALLY PENALIZED REGRESSION FOR DEPENDENCE ANALYSIS OF RA...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Debasish Das Auroop Ganguly Snigdhansu Chatterjee Vipin Kumar Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Civil and Environmental Engineering Northeastern University School of Statistics University of Minnesota Computer Science and Engineering University of Minnesota

ISBN: (纸本)9781467311601

Discovery of dependence structure between precipitation extremes and other climate variables (covariates) within a smaller spatial and temporal neighborhood is an important step in better understanding the drivers of this complex phenomenon as well as short-term prediction of extremes occurrence. Apart from the inherent spatio-temporal variability of the dependence, it is further complicated by the availability of the covariates at different vertical levels. The above problem can be split into three different sub-problems. Firstly, a spatio-temporal neighborhood of influence has to be discovered, which can be different for different locations. Secondly, the dependence structure between the precipitation extremes and the covariates has to be discovered within this neighborhood and thirdly, it has to be investigated whether this dependence structure can be exploited for any predictive power. Climate scientists have already discovered some physics-based relations between some of the covariates (e.g. temperature, relative humidity, precipitable water etc.) and precipitation extremes. We are exploring data-dependent alternatives for these problems and any possibility of incorporating the physics-based relations into the resulting data model. In particular, we used elastic net-based sparse optimization technique which solves all three problems of neighborhood discovery, covariate dependence discovery and predictive modeling and at the same time maintains the interpretability of the resulting model. Preliminary results look promising and show potential for some interesting knowledge discovery. We are currently exploring non-linear correlations and the alternatives to combine the physics-based relationships into the data model.

关键词： One Two Three Four Five data models precipitation Chemical Precipitation precipitation (meteorology) extremum values Neighborhood DEPENDENCE data mining interpretability

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Online robust image alignment via iterative convex optimization

Online robust image alignment via iterative convex optimizat...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yi Wu Bin Shen Haibin Ling Center for Data Analytics & Biomedical Informatics Computer & Information Science Department Temple University Philadelphia PA USA Department of Computer Science Purdue University West Lafayette IN USA

In this paper we study the problem of online aligning a newly arrived image to previously well-aligned images. Inspired by recent advances in batch image alignment using low rank decomposition [16], we treat the newly arrived image, after alignment, as being linearly and sparsely reconstructed by the well-aligned ones. The task is accomplished by a sequence of convex optimization that minimizes the l\-norm. After that, online basis updating is pursued in two different ways: (1) a two-stage incremental alignment for joint registration of a large image dataset which is known a prior, and (2) a greedy online alignment of dynamically increasing image sequences, such as in the tracking scenario. In (1), we first sequentially collect basis images that are easily aligned by checking their reconstruction residuals, followed by the second stage where all images are re-aligned one-by-one using the collected basis set. In (2), during the tracking process, we dynamically enrich the image basis set by the new target if it significantly distinguishes itself from existing basis images. While inheriting the benefits of sparsity, our method enjoys the great time efficiency and therefore be capable of dealing with large image set and real time tasks such as visual tracking. The efficacy of the proposed online robust alignment algorithm is verified with extensive experiments on image set alignment and visual tracking, in reference with state-of-the-art methods.

关键词： Target tracking Visualization Face Robustness Joints Image reconstruction Convex functions

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Combining gene expression and function in a spatially localized approach

Combining gene expression and function in a spatially locali...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Evangelia I. Zacharaki Angeliki Skoura Desmond J. Smith Scott H. Faro Li An Vasileios Megalooikonomou Department of Computer Engineering and Informatics University of Patras Patras Greece Data Engineering Laboratory Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA Department of Molecular and Medical Pharmacology David Geffen School of Medicine UCLA Los Angeles USA Department of Radiology Temple University School of Medicine Philadelphia USA

The integration of gene expression datasets with gene function information provides valuable insights in unraveling the molecular mechanisms of the brain. In this paper, gene expression maps, acquired by the technique of voxelation, are analyzed using an atlas-based framework and the extracted spatial information is employed to organize genes in significant clusters. Gene function enrichment analysis of clusters enabled exploration of the relationships among brain regions, gene expression and gene function. In addition, the analysis revealed new function categories, for example biological process for the gene Pbx3, cellular component and biological process for the gene Ppplrlb. Our work confirms the hypothesis that genes of similar spatial expression patterns display similar functions indicating that our methodology could assist in the function identification of unannotated genes.

关键词： Gene expression Biological processes Educational institutions Ontologies Mice Prototypes Focusing

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Exploring genetic variability in drug therapy by selecting a...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Qiang Lou Henry P. Parkman Michael R. Jacobs Evgeny Krynetskiy Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA Gastroenterology Section School of Medicine Temple University Philadelphia USA School of Pharmacy Temple University Philadelphia USA School of Pharmacy Jayne Haines Center for Pharmacogenomics and Drug Safety Temple University Philadelphia USA

关键词： Markov processes Kernel Approximation methods Approximation algorithms Accuracy Drugs Genomics

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