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Multi Level Clustering of Phylogenetic Profiles

Multi Level Clustering of Phylogenetic Profiles

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Fotis E. Psomopoulos Pericles A. Mitkas Intelligent Systems and Software Engineering Laboratory Informatics and Telematics Institute Thessaloniki Greece Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

The prediction of gene function from genome sequences is one of the main issues in Bioinformatics. Most computational approaches are based on the similarity between sequences to infer gene function. However, the availability of several fully sequenced genomes has enabled alternative approaches, such as phylogenetic profiles. Phylogenetic profiles are vectors which indicate the presence or absence of a gene in other genomes. The main concept of phylogenetic profiles is that proteins participating in a common structural complex or metabolic pathway are likely to evolve in a correlated fashion. In this paper, a multi level clustering algorithm of phylogenetic profiles is presented, which aims to detect inter- and intra-genome gene clusters.

关键词： Phylogeny Bioinformatics Genomics Clustering algorithms Databases Proteins Biomedical engineering Intelligent systems Computational intelligence software engineering

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Towards a Metrics Suite for Measuring Composite Service Granularity Level Appropriateness

Towards a Metrics Suite for Measuring Composite Service Gran...

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IEEE World Congress on Services (SERVICES)

作者： A. Khoshkbarforoushha R. Tabein P. Jamshidi F. Shams Automated Software Engineering Research Group Electrical and Computer Engineering Faculty Shahid Beheshti University Tehran Iran Department of Computer and Systems Sciences Royal Institute of Technology Stockholm Sweden

One of the prominent principles of designing services is the matter of how abstract services should be i.e. granularity. Since service-oriented analysis and design methods lack on providing a quantitative model for service granularity level evaluation, identification of optimally granular services is the key challenge in service-oriented solution development. This article through a systematic process proposes a model namely Weighted Granularity Level Appropriateness (WGLA) which leverages and consolidates four metrics to constitute quantitative basis for granularity appropriateness analysis. These metrics are, indeed, the four quantified attributes of service granularity including business value, reusability, context-independency, and complexity. Our preliminary controlled experiment confirms the correctness of the quantitative model. In fact, by adopting WGLA metric, service granularity appropriateness analysis could be conduct quantitatively that leads to realize an optimized service-oriented solution in terms of its granularity.

关键词： Business Measurement Complexity theory Analytical models Service oriented architecture software

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Reports on the AAAI 2009 fall symposia

Reports on the AAAI 2009 fall symposia

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作者： Azevedo, Roger Bench-Capon, Trevor Biswas, Gautam Carmichael, Ted Green, Nancy Hadzikadic, Mirsad Koyejo, Oluwasanmi Kurup, Unmesh Parsons, Simon Pirrone, Roberto Prakken, Henry Samsonovich, Alexei Scott, Donia Souvenir, Richard Department of Psychology Institute for Intelligent Systems University of Memphis United States Department of Computer Science University of Liverpool United Kingdom Center for Intelligent Systems Vanderbilt University United States Department of Computer Science University of North Carolina Charlotte United States Department of Computer Science University of North Carolina Greensboro United States Department of Software and Information Systems University of North Carolina Charlotte United States Complex Systems Institute United States Department of Electrical and Computer Engineering University of Texas Austin United States Department of Cognitive Science Rensselaer Polytechnic Institute Troy NY United States Department of Computer and Information Science Brooklyn College City University New York United States University of Palermo Italy Computer Science Department Utrecht University Netherlands Department of Law and IT Law Faculty University of Groningen Netherlands Krasnow Institute for Advanced Study George Mason University United States Computational Linguistics Department Informatics at the University of Sussex United Kingdom

The Association for the Advancement of Artificial Intelligence was pleased to present the 2009 Fall Symposium Series, held Thursday through Saturday, November 5-7, at the Westin Arlington Gateway in Arlington, Virginia. The Symposium Series was preceded on Wednesday, November 4, by a one-day AI funding seminar. The titles of the seven symposia were as follows: (1) Biologically Inspired Cognitive Architectures, (2) Cognitive andMetacognitive Educational systems, (3) Complex Adaptive systems and the Threshold Effect: Views from the Natural and Social Sciences, (4) Manifold Learning and Its Applications, (5) Multirepresentational Architectures for Human-Level Intelligence, (6) The Uses of Computational Argumentation, and (7) Virtual Healthcare Interaction. Copyright © 2010, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Cognitive systems

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Selecting Informative Genes from Microarray Data by Using a Cyclic GA-Based Method

Selecting Informative Genes from Microarray Data by Using a ...

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International Conference on Intelligent systems, Modelling and Simulation (ISMS)

作者： Mohd Saberi Mohamad Sigeru Omatu Safaai Deris Michifumi Yoshioka Department of Software Engineering Universiti Teknologi Malaysia Skudai Johore Malaysia Department of Computer Science and Intelligent Systems Osaka Prefecture University Sakai Osaka Japan Department of Software Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia

ISBN: (纸本)9781424459841;9780769539737

Microarray data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contributes to a cancer disease. This selection process is difficult due to the availability of a small number of samples compared to the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this paper proposes a cyclic method based on genetic algorithms (GA) to select a near-optimal (small) subset of informative genes that is relevant for cancer classification. The performance of the proposed method was evaluated by three benchmark microarray data sets and obtained encouraging results as compared with other experimented methods and previous related works.

关键词： Cancer Filters Genetic algorithms Support vector machines Support vector machine classification Diseases Gene expression Intelligent systems Computational modeling computer simulation

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Computing students learning computing informally 10

Computing students learning computing informally

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Proceedings of the 10th Koli Calling International Conference on Computing Education Research

作者： McCartney, Robert Eckerdal, Anna Moström, Jan Erik Sanders, Kate Thomas, Lynda Zander, Carol Department of Computer Science and Engineering University of Connecticut Storrs CT United States Department of Information Technology Uppsala University Uppsala Sweden Department of Computing Science Umeå University 901 87 Umeå Sweden Math/CS Department Rhode Island College Providence RI United States Department of Computer Science Aberystwyth University Aberystwyth United Kingdom Computing and Software Systems University of Washington Bothell Bothell WA United States

ISBN: (纸本)9781450305204

In this paper we present background and early results from an investigation of how computing students learn computer science topics through informal means, that is, outside of organized classes. We provide some overall perspective by discussing the variety of research areas that fall under the general \informal learning" name. From there we propose specific research questions that concern what the students learn, what resources they bring to bear, what strategies they employ, and how they evaluate their progress. Preliminary results indicate that students primarily learn specific technologies, and that both their motivation and evaluation are closely tied to projects (at school, work, or home). Copyright © 2010 ACM.

关键词： Students

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Gene Expression Classification with a Novel Coevolutionary Based Learning Classifier System on Public Clouds

Gene Expression Classification with a Novel Coevolutionary B...

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IEEE International Conference on E-Science Workshops

作者： Christian Vecchiola Mani Abedini Michael Kirley Xingchen Chu Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory University of Melbourne Australia Department of Computer Science and Software Engineering University of Melbourne Australia

Microarray technology allows for the simultaneous monitoring of thousands of genes expressions per sample. Unfortunately, the classification of these samples into distinct classes is often difficult as the number of genes (features) greatly exceeds the number of samples. Consequently, there is a need to investigate new, robust machine learning techniques capable of accurately classifying microarray data. In this paper, we present a coevolutionary learning classifier system based on feature set partitioning to classify gene expression data. A distributed implementation, which leverages Cloud computing technologies, is used to address the inherent computational costs of our model. The development and execution of this application was done using the Aneka middleware on the public Cloud (Amazon EC2) infrastructure. Experiments conducted using gene expression profiles demonstrates that the proposed implementation outperforms other well-known classifiers in terms of accuracy. Preliminary analysis into the impact of different Cloud setups on the performance of the classifier are also reported.

关键词： Cloud computing Gene expression Accuracy Machine learning Containers computer architecture

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Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients

Comparing performances of logistic regression, decision tree...

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computer Information systems and Industrial Management Applications (CISIM)

作者： Anchana Khemphila Veera Boonjing Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

In this study, performances of classification techniques were compared in order to predict the presence of the patients getting a heart disease. A retrospective analysis was performed in 303 subjects. We compared the performance of logistic regression(LR), decision trees(DTs), and Artificial neural networks (ANNs). The variables were medical profiles are age, Sex, Chest Pain Type, Blood Pressure, Cholesterol, Fasting Blood Sugar, Resting ECG, Maximum Heart Rate, Induced Angina, Ole Peak, Slope, Number Colored Vessels, Thal and Concept Class. We have created the model using logistic regression classifiers, artificial neural networks and decision trees that they are often used for classification problems. Performances of classification techniques were compared using lift chart and error rates. In the result, artificial neural networks have the greatest area between the model curve and the baseline curve. The error rates are 0.22, 0.198, 0.21, respectively for logistic regression, artificial neural networks and decision trees. The neural networks exhibited sensitivity of 81.1%, specificity of 78.7% and accuracy of 80.2%, while the decision tree provided the prediction performance with a sensitivity, specificity and accuracy of 81.7%, 76.0% and 79.3%. And the logistic regression provided the prediction performance with a sensitivity, specificity and accuracy of 81.2%,73.1% and 77.7% Artificial neural networks have the least of error rate and has the highest accuracy, therefore Artificial neural networks is the best technique to classify in this data set.

关键词： Artificial neural networks Heart Logistics Regression tree analysis Data models Data mining

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XML Path Matching for Different Hierarchy Order of Elements in XML Documents

XML Path Matching for Different Hierarchy Order of Elements ...

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ACIS International Conference on software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Sutheetutt Vacharaskunee Sarun Intakosum Department of Mathematics and Computer Science Faculty of Science Software Systems Engineering Laboratory King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9781424474226;9780769540887

XML documents allow document owners to create their documents in their own formats (element names and structure). The same information might be described in several ways. To find the similarity between XML documents which use different formats, one method is path similarity detection. The recently approach is PathSim. The previous approach can detect the similarity rate between two XML paths in case those two XML paths are in the same hierarchy order of elements that are semantic with each other. If XML documents use different elements hierarchy order, that approach has a low similarity rate. To improve the previous approach, PathMatch is introduced. PathMatch uses the edit distance algorithm to find the semantic similarity rate between element names and a cost matrix model to find the similarity rate between two XML paths. On the result, PathMatch has a higher similarity rate than the previous approach in case those two XML paths contain a different hierarchy order of elements that are semantic with each other. Moreover, in case two XML paths contain the same hierarchy order, PathMatch has the same similarity rate as the previous one.

关键词： XML Costs Change detection algorithms software engineering Artificial intelligence Distributed computing software systems systems engineering and theory Laboratories Mathematics

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A Three-Stage Method to Select Informative Genes from Gene Expression Data in Classifying Cancer Classes

A Three-Stage Method to Select Informative Genes from Gene E...

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International Conference on Intelligent systems, Modelling and Simulation (ISMS)

作者： Mohd Saberi Mohamad Sigeru Omatu Safaai Deris Michifumi Yoshioka Department of Software Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia Department of Computer Science and Intelligent Systems Osaka Prefecture University Sakai Osaka Japan

ISBN: (纸本)9781424459841;9780769539737

The process of gene selection for the cancer classification faces with a major problem due to the properties of the data such as the small number of samples compared to the huge number of genes, irrelevant genes, and noisy data. Hence, this paper aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve the aim, a three-stage method has been proposed. It has three stages: (1) pre-selecting genes using a filter method; (2) optimizing the gene subset using a multi-objective hybrid method; (3) analyzing the frequency of appearance of each gene. By performing experiments on three public gene expression data sets, classification accuracies and the number of selected genes of the proposed method are better than those of other experimented methods and previous works. A list of informative genes in the final gene subsets is also presented for biological usage.

关键词： Gene expression Cancer Filters Genetic algorithms Support vector machines Support vector machine classification Frequency Intelligent systems Computational modeling computer simulation

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XER: A Recommendation for XML Element

XER: A Recommendation for XML Element

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International Conference on Information Technology: New Generations (ITNG)

作者： Sutheetutt Vacharaskunee Sarun Intakosum Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

The strong point of the XML is that it allows document owners to describe their documents in their own format (structure and element names). There might be the same information has been describe in various ways. When user wants to search some information from XML documents, user might not retrieve all related results because of the difference of elements (tag names). To retrieve all related results, user needs to have queries for all possible elements. XML Element Recommendation (XER) is an idea to make XML documents easier for searching. It receives XML document as an input and compares that input with XML documents from database. It compares word similarity and semantic of each element. For semantic similarity of each element, it checks those elements to find which element (word) should be used by using word ranking (most common words). The output is a recommendation for each element of input XML document.

关键词： XML Mobile communication Mobile ad hoc networks Routing protocols Ad hoc networks Earthquakes Network topology Motion analysis Vehicle dynamics Costs

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