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YamiPred: A Novel evolutionary Method for Predicting Pre-miRNAs and Selecting Relevant Features

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015年第5期12卷 1183-1192页

作者： Kleftogiannis, Dimitrios Theofilatos, Konstantinos Likothanassis, Spiros Mavroudi, Seferina King Abdullah Univ Sci & Technol Comp Sci & Math Sci & Engn Div Thuwal Saudi Arabia Univ Patras Dept Comp Engn & Informat GR-26110 Patras Greece

MicroRNAs (miRNAs) are small non-coding RNAs, which play a significant role in gene regulation. Predicting miRNA genes is a challenging bioinformatics problem and existing experimental and computational methods fail to deal with it effectively. We developed YamiPred, an embedded classification method that combines the efficiency and robustness of support vector machines (SVM) with genetic algorithms (GA) for feature selection and parameters optimization. YamiPred was tested in a new and realistic human dataset and was compared with state-of-the-art computational intelligence approaches and the prevalent SVM-based tools for miRNA prediction. Experimental results indicate that YamiPred outperforms existing approaches in terms of accuracy and of geometric mean of sensitivity and specificity. The embedded feature selection component selects a compact feature subset that contributes to the performance optimization. Further experimentation with this minimal feature subset has achieved very high classification performance and revealed the minimum number of samples required for developing a robust predictor. YamiPred also confirmed the important role of commonly used features such as entropy and enthalpy, and uncovered the significance of newly introduced features, such as % A-U aggregate nucleotide frequency and positional entropy. The best model trained on human data has successfully predicted pre-miRNAs to other organisms including the category of viruses.

关键词： Classifier design and evaluation evolutionary computing and genetic algorithms feature evaluation and selection SVM GA pre-miRNA prediction

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An Adaptive Population Resizing Scheme for Differential Evolution in Numerical Optimization

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2015年第7期12卷 1336-1350页

作者： Choi, Tae Jong Ahn, Chang Wook Sungkyunkwan Univ Dept Comp Engn Suwon Gyeonggi Do South Korea

Applying the appropriate values to the control parameters of Differential Evolution (DE) is a challengeable problem. In order to enhance the optimization performance of DE, the exploration exploitation properties should be adjusted depending on the optimization progress. To make a balance between the exploration exploitation properties, the population size that is one of the control parameters can be controlled. In this paper, we proposed Adaptive Population Resizing (APR) scheme that calculates the deviation of the scattered individuals over search spaces in every certain evaluation counters and conducts adjusting the population size based on the information. Therefore, APR scheme can adjust the number of individuals by adding or subtracting some individuals depending on the optimization progress. We extended an adaptive differential evolution (SaDE) by attaching APR scheme and compared with several state-of-the-art DE algorithms in 14 classical benchmark problems. The experiment result showed that the proposed algorithm performed better than the compared DE algorithms in not only unimodal but also multimodal benchmark problems.

关键词： Artificial Intelligence evolutionary computing and genetic algorithms Global Optimization

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Adaptive Human Action Recognition With an Evolving Bag of Key Poses

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IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 2014年第2期6卷 139-152页

作者： Andre Chaaraoui, Alexandros Florez-Revuelta, Francisco Univ Alicante Dept Comp Technol E-03080 Alicante Spain Univ Kingston Fac Sci Engn & Comp Kingston Upon Thames KT1 2EE Surrey England

Vision-based human action recognition allows to detect and understand meaningful human motion. This makes it possible to perform advanced human-computer interaction, among other applications. In dynamic environments, adaptive methods are required to support changing scenario characteristics. Specifically, in human-robot interaction, smooth interaction between humans and robots can only be performed if these are able to evolve and adapt to the changing nature of the scenarios. In this paper, an adaptive vision-based human action recognition method is proposed. By means of an evolutionary optimization method, adaptive and incremental learning of human actions is supported. Through an evolving bag of key poses, which models the learned actions over time, the current learning memory is developed to recognize increasingly more actions or actors. The evolutionary method selects the optimal subset of training instances, features and parameter values for each learning phase, and handles the evolution of the model. The experimentation shows that our proposal achieves to adapt to new actions or actors successfully, by rearranging the learned model. Stable and accurate results have been obtained on four publicly available RGB and RGB-D datasets, unveiling the method's robustness and applicability.

关键词： evolutionary computing and genetic algorithms feature evaluation and selection human computer interaction vision and scene understanding

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Position-Relative Identities in the Internet of Things: An evolutionary GHT Approach

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IEEE INTERNET OF THINGS JOURNAL 2014年第5期1卷 497-507页

作者： Attwood, Andrew Lamb, David J. Abuelmaatti, Omar Manchester Metropolitan Univ Sch Comp Math & Digital Technol Manchester M1 5GD Lancs England Liverpool John Moores Univ Sch Comp & Math Sci Liverpool L3 3AF Merseyside England

The Internet of Things (IoT) will result in the deployment of many billions of wireless embedded systems creating interactive pervasive environments. It is envisaged that devices will cooperate to provide greater system knowledge than the sum of its parts. In an emergency situation, the flow of data across the IoT may be disrupted, giving rise to a requirement for machine-to-machine interaction within the remaining ubiquitous environment. Geographic hash tables (GHTs) provide an efficient mechanism to support fault-tolerant rendezvous communication between devices. However, current approaches either rely on devices being equipped with a GPS or being manually assigned an identity. This is unrealistic when the majority of these systems will be located inside buildings and will be too numerous to expect manual configuration. Additionally, when using GHT as a distributed data store, imbalance in the topology can lead to storage and routing overhead. This causes unfair work load, exhausting limited power supplies as well as causing poor data redundancy. To deal with these issues, we propose an approach that balances graph-based layout identity assignment, through the application of multifitness genetic algorithms. Our experiments show through simulation that our multifitness evolution technique improves on the initial graph-based layout, providing devices with improved balance and reachability metrics.

关键词： Data-centric storage evolutionary computing and genetic algorithms information dispersal load balancing wireless sensor networks

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An Automated Framework for Incorporating News into Stock Trading Strategies

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2014年第4期26卷 823-835页

作者： Nuij, Wijnand Milea, Viorel Hogenboom, Frederik Frasincar, Flavius Kaymak, Uzay Semlab NL-2408 ZE Alphen Aan Den Rijn Zuid Holland Netherlands Erasmus Univ Erasmus Sch Econ NL-3000 DR Rotterdam Netherlands Eindhoven Univ Technol Dept Ind Engn & Innovat Sci NL-5600 MB Eindhoven Netherlands

In this paper we present a framework for automatic exploitation of news in stock trading strategies. Events are extracted from news messages presented in free text without annotations. We test the introduced framework by deriving trading strategies based on technical indicators and impacts of the extracted events. The strategies take the form of rules that combine technical trading indicators with a news variable, and are revealed through the use of genetic programming. We find that the news variable is often included in the optimal trading rules, indicating the added value of news for predictive purposes and validating our proposed framework for automatically incorporating news in stock trading strategies.

关键词： Computer applications evolutionary computing and genetic algorithms learning natural language processing web text analysis

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Self-Reconfigurable Evolvable Hardware System for Adaptive Image Processing

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IEEE TRANSACTIONS ON COMPUTERS 2013年第8期62卷 1481-1493页

作者： Salvador, Ruben Otero, Andres Mora, Javier de la Torre, Eduardo Riesgo, Teresa Sekanina, Lukas Univ Politecn Madrid Elect & Microelect Design Res Grp GDEM Res Ctr Software Technol & Multimedia Syst Sustai Dept Sistemas Elect & ControlEUIT Telecomunicac Madrid 28031 Spain Univ Politecn Madrid Ctr Elect Ind ETSI Ind E-28006 Madrid Spain Brno Univ Technol Fac Informat Technol IT4Innovat Ctr Excellence Brno 61266 Czech Republic

This paper presents an evolvable hardware system, fully contained in an FPGA, which is capable of autonomously generating digital processing circuits, implemented on an array of processing elements (PEs). Candidate circuits are generated by an embedded evolutionary algorithm and implemented by means of dynamic partial reconfiguration, enabling evaluation in the final hardware. The PE array follows a systolic approach, and PEs do not contain extra logic such as path multiplexers or unused logic, so array performance is high. Hardware evaluation in the target device and the fast reconfiguration engine used yield smaller reconfiguration than evaluation times. This means that the complete evaluation cycle is faster than software-based approaches and previous evolvable digital systems. The selected application is digital image filtering and edge detection. The evolved filters yield better quality than classic linear and nonlinear filters using mean absolute error as standard comparison metric. Results do not only show better circuit adaptation to different noise types and intensities, but also a nondegrading filtering behavior. This means they may be run iteratively to enhance filtering quality. These properties are even kept for high noise levels (40 percent). The system as a whole is a step toward fully autonomous, adaptive systems.

关键词： Evolvable hardware FPGAs self-adaptive systems reconfigurable hardware adaptable architectures autonomous systems evolutionary computing and genetic algorithms

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A genetic Programming Approach to Record Deduplication

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2012年第3期24卷 399-412页

作者： de Carvalho, Moises G. Laender, Alberto H. F. Goncalves, Marcos Andre da Silva, Altigran S. Nokia INdT BR-69048660 Manaus Amazonas Brazil Univ Fed Minas Gerais Dept Ciencia Comp BR-31270901 Belo Horizonte MG Brazil Univ Fed Amazonas Inst Comp BR-69077000 Manaus Amazonas Brazil

Several systems that rely on consistent data to offer high-quality services, such as digital libraries and e-commerce brokers, may be affected by the existence of duplicates, quasi replicas, or near-duplicate entries in their repositories. Because of that, there have been significant investments from private and government organizations for developing methods for removing replicas from its data repositories. This is due to the fact that clean and replica-free repositories not only allow the retrieval of higher quality information but also lead to more concise data and to potential savings in computational time and resources to process this data. In this paper, we propose a genetic programming approach to record deduplication that combines several different pieces of evidence extracted from the data content to find a deduplication function that is able to identify whether two entries in a repository are replicas or not. As shown by our experiments, our approach outperforms an existing state-of-the-art method found in the literature. Moreover, the suggested functions are computationally less demanding since they use fewer evidence. In addition, our genetic programming approach is capable of automatically adapting these functions to a given fixed replica identification boundary, freeing the user from the burden of having to choose and tune this parameter.

关键词： Database administration evolutionary computing and genetic algorithms database integration

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Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012年第6期9卷 1676-1689页

作者： Ashlock, Wendy Datta, Suprakash York Univ Dept Comp Sci & Engn Toronto ON M3J 1P3 Canada

Side effect machines produce features for classifiers that distinguish different types of DNA sequences. They have the, as yet unexploited, potential to give insight into biological features of the sequences. We introduce several innovations to the production and use of side effect machine sequence features. We compare the results of using consensus sequences and genomic sequences for training classifiers and find that more accurate results can be obtained using genomic sequences. Surprisingly, we were even able to build a classifier that distinguished consensus sequences from genomic sequences with high accuracy, suggesting that consensus sequences are not always representative of their genomic counterparts. We apply our techniques to the problem of distinguishing two types of transposable elements, solo LTRs and SINEs. Identifying these sequences is important because they affect gene expression, genome structure, and genetic diversity, and they serve as genetic markers. They are of similar length, neither codes for protein, and both have many nearly identical copies throughout the genome. Being able to efficiently and automatically distinguish them will aid efforts to improve annotations of genomes. Our approach reveals structural characteristics of the sequences of potential interest to biologists.

关键词： Endogenous retroviruses LTR retrotransposons SINE elements feature evaluation and selection machine learning evolutionary computing and genetic algorithms side effect machines

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The GA and the GWAS: Using genetic algorithms to Search for Multilocus Associations

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012年第3期9卷 899-910页

作者： Mooney, Michael A. Wilmot, Beth McWeeney, Shannon K. Oregon Hlth & Sci Univ Dept Med Informat & Clin Epidemiol Portland OR 97239 USA

Enormous data collection efforts and improvements in technology have made large genome-wide association studies a promising approach for better understanding the genetics of common diseases. Still, the knowledge gained from these studies may be extended even further by testing the hypothesis that genetic susceptibility is due to the combined effect of multiple variants or interactions between variants. Here, we explore and evaluate the use of a genetic algorithm to discover groups of SNPs (of size 2, 3, or 4) that are jointly associated with bipolar disorder. The algorithm is guided by the structure of a gene interaction network, and is able to find groups of SNPs that are strongly associated with the disease, while performing far fewer statistical tests than other methods.

关键词： Biology and genetics evolutionary computing and genetic algorithms graphs and networks

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Fuzzy ARTMAP Prediction of Biological Activities for Potential HIV-1 Protease Inhibitors Using a Small Molecular Data Set

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第1期8卷 80-93页

作者： Andonie, Razvan Fabry-Asztalos, Levente Abdul-Wahid, Christopher Badi' Abdul-Wahid, Sarah Barker, Grant I. Magill, Lukas C. Cent Washington Univ Dept Comp Sci Ellensburg WA 98926 USA Transylvania Univ Brasov Elect & Comp Dept Brasov Romania Cent Washington Univ Dept Chem Ellensburg WA 98926 USA

Obtaining satisfactory results with neural networks depends on the availability of large data samples. The use of small training sets generally reduces performance. Most classical Quantitative Structure-Activity Relationship (QSAR) studies for a specific enzyme system have been performed on small data sets. We focus on the neuro-fuzzy prediction of biological activities of HIV-1 protease inhibitory compounds when inferring from small training sets. We propose two computational intelligence prediction techniques which are suitable for small training sets, at the expense of some computational overhead. Both techniques are based on the FAMR model. The FAMR [1] is a Fuzzy ARTMAP (FAM) incremental learning system used for classification and probability estimation. During the learning phase, each sample pair is assigned a relevance factor proportional to the importance of that pair. The two proposed algorithms in this paper are: 1) The GA-FAMR algorithm, which is new, consists of two stages: a) During the first stage, we use a genetic algorithm (GA) to optimize the relevances assigned to the training data. This improves the generalization capability of the FAMR. b) In the second stage, we use the optimized relevances to train the FAMR. 2) The Ordered FAMR is derived from a known algorithm. Instead of optimizing relevances, it optimizes the order of data presentation using the algorithm of Dagher et al. [2], [3]. In our experiments, we compare these two algorithms with an algorithm not based on the FAM, the FS-GA-FNN introduced in [4], [5]. We conclude that when inferring from small training sets, both techniques are efficient, in terms of generalization capability and execution time. The computational overhead introduced is compensated by better accuracy. Finally, the proposed techniques are used to predict the biological activities of newly designed potential HIV-1 protease inhibitors.

关键词： Fuzzy neural networks evolutionary computing and genetic algorithms computational chemistry data mining

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