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computational identification of De-centric genetic regulatory relationships from functional genomic data

Computational identification of De-centric genetic regulator...

引用

10th International symposium on bioinformatics Research and Applications, ISBRA 2014

作者： Yue, Zongliang Wan, Ping Xie, Zhan Chen, Jake Y. Institute of Biopharmaceutical Informatics and Technology Wenzhou Medical University Wenzhou Zhejiang Province China School of Informatics and Computing Indiana University Indianapolis IN 46202 United States Indiana Center for Systems Biology and Personalized Medicine Indiana University Indianapolis IN 46202 United States Bioinformatics Lab Capital Normal University Beijing China

ISBN: (纸本)9783319081700

We developed a new computational technique to identify de-centric genetic regulatory relationship candidates. Our technique takes advantages of functional genomics data for the same species under different perturbation conditions, therefore making it complementary to current computational techniques including database search, clustering of gene expression profiles, motif matching, structural modeling, and network effect simulation methods. It is fast and addressed the need of biologists to determine activation/inhibition relationship details often missing in synthetic lethality or chip-seq experiments. We used GEO microarray data set GSE25644 with 158 different mutant genes in S. cerevisiae. We screened out 83 targets with 610 activation pairs and 93 targets with 494 inhibition pairs. In the Yeast Fitness database, 33 targets (40%) with 126 activation pairs and 31 targets (33%) with 97 inhibition pairs were identified. To be identified further are 50 targets with 484 activation pairs and 62 targets with 397 inhibition pairs. The aggregation test confirmed that all discovered de-centric regulatory relationships are significant from random discovery at a p-value=0.002;therefore, this method is highly complementary to others that tend to discover hub-related regulatory relationships. We also developed criteria for rejecting genetic regulator candidates x as a candidate regulator and assessing the ranking of the regulator-target relationship identified. The top 10 high suspected regulators determined by our criteria were found to be significant, pending future experimental verifications. © 2014 Springer International Publishing Switzerland.

关键词： Yeast

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Randomized communication complexity of approximating kolmogorov complexity

Randomized communication complexity of approximating kolmogo...

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9th International Computer Science symposium in Russia, CSR 2014

作者： Vereshchagin, Nikolay Higher School of Economics Moscow State University Yandex Russia

ISBN: (纸本)9783319066851

The paper [Harry Buhrman, Michal Koucký, Nikolay Vereshchagin. Randomized Individual Communication Complexity. ieee Conference on computational Complexity 2008: 321-331] considered communication complexity of the following problem. Alice has a binary string x and Bob a binary string y, both of length n, and they want to compute or approximate Kolmogorov complexity C(x|y) of x conditional to y. It is easy to show that deterministic communication complexity of approximating C(x|y) with additive error α is at least n∈-∈2α∈-∈O(1). The above referenced paper asks what is randomized communication complexity of this problem and shows that for r-round randomized protocols its communication complexity is at least Ω((n/α)1/r ). In this paper, for some positive Ε, we show the lower bound 0.99n for (worst case) communication length of any randomized protocol that with probability at least 0.01 approximates C(x|y) with additive error Εn for all input pairs. © 2014 Springer International Publishing Switzerland.

关键词： computational complexity

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Multi-Genomic Algorithms

Multi-Genomic Algorithms

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ieee symposium on computational intelligence in Multi-Criteria Decision-Making, MCDM

作者： Mathias Ngo Raphaël Labayrade Université de Lyon Lyon France Departement Génie Civil Bâtiment Ecole Nationale des Travaux Publics de l'Etat Vaulx-en-Velin France

The first step of any optimization process consists in choosing the Decision Variables (DV) and its relationships that model the problem, system or object to optimize. Many problems cannot be represented by a unique, exhaustive model which would ensure a global best result: in those cases, the model (DV and relationships) choice matters on the quality of the results.

关键词： Genomics bioinformatics Optimization Sociology Statistics computational modeling Biological cells

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Statistical analysis and classification of EEG-based attention network task using optimized feature selection

Statistical analysis and classification of EEG-based attenti...

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ieee symposium on computational intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)

作者： Hua-Chin Lee Li-Wei Ko Hui-Ling Huang Jui-Yun Wu Ya-Ting Chuang Shinn-Ying Ho Institute of Bioinformatics and Systems Biology National Chiao Tung University (NCTU) Taiwan Center for Bioinformatics Research NCTU Taiwan Brain Research Center NCTU Taiwan

ISBN: (纸本)9781479945481

This research incorporates optimized feature selection using an inheritable bi-objective combinatorial genetic algorithm (IBCGA) and mathematic modeling for classification and analysis of electroencephalography (EEG) based attention network. It consists of two parts. 1) We first design the attention network experiments, record the EEG signals of subjects from NeuronScan instrument, and filter noise from the EEG data. We use alerting scores, orienting scores, and conflict scores to serve as the efficiency evaluation of the attention network. 2) Based on an intelligent evolutionary algorithm as the core technique, we analyze the large-scale EEG data, identify a set of important frequency-channel factors, and establish mathematical models for within-subject, across-subject and leave-one-subject-out evaluation using a global optimization approach. The results of using 10 subjects show that the average classification accuracy of independent test in the within-subject case is 86.51%, the accuracy of the across-subject case is 68.44%, and the accuracy of the leave-one-subject-out case is 54.33%.

关键词： Accuracy Electroencephalography Training Probability Brain modeling Mathematical model Optimization

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Development of SSVEP-based BCI using common frequency pattern to enhance system performance

Development of SSVEP-based BCI using common frequency patter...

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ieee symposium on computational intelligence in Brain Computer Interfaces (CIBCI)

作者： Li-Wei Ko Shih-Chuan Lin Wei-Gang Liang Oleksii Komarov Meng-Shue Song Brain Research Center in National Chiao Tung University Hsinchu Taiwan Institute of Bioinformatics and Systems Biology Department of Physics National Tsing Hau University Hsinchu Taiwan Institute of Molecular Medicine and Bioengineering

Brain Computer Interface(BCI) systems provide an additional way for people to interact with external environment without using peripheral nerves or muscles[1]. In a variety of BCI systems, a BCI system based on the steady-state visual evoked potentials (SSVEP) is one most common system known for application, because of its ease of use and good performance with little user training. In this study, the common frequency pattern method (CFP) is used to improve the accuracy of our EEG-based SSVEP BCI system. There are four basic classifiers (SVM, KNNC, PARZENDC, LDC) in this paper to estimate the accuracy of our SSVEP system. Without using CFP, the highest accuracy of the EEG-based SSVEP system was 80%. By using CFP, the accuracy could be upgraded to 95%.

关键词： Accuracy Electroencephalography Static VAr compensators Visualization Support vector machines Eigenvalues and eigenfunctions Covariance matrices

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Protein Named Entity Classification with Probabilistic Features Derived from GENIA Corpus and MEDLINE 7

Protein Named Entity Classification with Probabilistic Featu...

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Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International symposium on Advanced Intelligent Systems (ISIS)

作者： Sumathipala, Sagara Yamada, Koichi Unehara, Muneyuki Nagaoka Univ Technol Grad Sch Engn 1603-1 Kamitomioka Machi Nagaoka Niigata 9402188 Japan

ISBN: (纸本)9781479959556

Biomedical named entity recognition (BNER) is one of the most essential and initial tasks (discovering relations between biomedical entities, identifying molecular pathways, etc.) of biomedical information retrieval. Although named entity recognition performed well in ordinary text, it still remains challenging in molecular biology domain because of the complex nature of biomedical nomenclature, different kinds of spelling forms and many more reasons. Even though biomedical entities in biological text are found successfully, classifying them into relevant biomedical classes such as genes, proteins, diseases, drug names, etc. is still another challenge and an open question. This paper presents a new method to classify biomedical named entities into protein and non-protein classes. Our approach employs Random Forest, a machine learning algorithm, with a new combination of features. They are orthographic, keyword and morphological, as well as a probabilistic feature called Proteinhood and a Protein-Score feature based on the Medline abstracts cited on the Pubmed, which are the main contributions in the paper. A series of experiments is conducted to compare the proposed approach with other state of the art approaches. Our protein named entity classifier shows significant performance in the experiments on GENIA corpus achieving the highest values of precision 93.8%, recall 83.8% and F-measure 88.5% for protein named entity identification. In this study we showed the effect of new Proteinhood and Protein-Score features as well as adjusting parameters of Random Forest algorithm.

关键词： Named entity recognition Biomedical text mining computational molecular biology Biomedical named entity Protein named entity

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HiCOMB Introduction and Committees

HiCOMB Introduction and Committees

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ieee International symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Alba Cristina Magalhaes Alves de Melo Srinivas Aluru David A. Bader

Provides a listing of current committee members and society officers.

关键词： Educational institutions Conferences computational biology Laboratories High performance computing bioinformatics biology

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Massively parallelized support vector machines based on GPU-accelerated multiplicative updates

Massively parallelized support vector machines based on GPU-...

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ieee symposium on computational intelligence and Data Mining (CIDM)

作者： Connie Khor Li Kou Chao-Hui Huang Agency for Science Technology and Research Singapore SG Bioinformatics Institute Agency for Science Technology and Research Singapore

In this paper, we present multiple parallelized support vector machines (MPSVMs), which aims to deal with the situation when multiple SVMs are required to be performed concurrently. The proposed MPSVM is based on an optimization procedure for nonnegative quadratic programming (NQP), called multiplicative updates. By using graphical processing units (GPUs) to parallelize the numerical procedure of SVMs, the proposed MPSVM showed good performance for a certain range of data size and dimension. In the experiments, we compared the proposed MPSVM with other cutting-edge implementations of GPU-based SVMs and it showed competitive performance. Furthermore, the proposed MPSVM is designed to perform multiple SVMs in parallel. As a result, when multiple operations of SVM are required, MPSVM can be one of the best options in terms of time consumption.

关键词： Support vector machines Training Kernel Training data Optimization Accuracy Graphics processing units

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Algorithmic systems ecology: Experiments on multiple interaction types and patches

Algorithmic systems ecology: Experiments on multiple interac...

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10th International Conference on Software Engineering and Formal Methods, SEFM 2012, 1st International symposium on InSuEdu 2012, 1st International symposium on MoKMaSD 2012, 6th International Workshop on Foundations and Techniques for OpenCert 2012

作者： Kahramanoǧullari, Ozan Lynch, James F. Priami, Corrado Microsoft Research University of Trento Centre for Computational and Systems Biology Trento Italy Department of Computer Science Clarkson University Potsdam NY United States Department of Mathematics University of Trento Trento Italy

ISBN: (纸本)9783642543371

Dynamic behavior in ecosystems can emerge as a result of multiple interactions of different types as well as movements of the ecosystem species between different patches. The extinction behaviors in ecosystem models, which can result from the small species numbers, bring stochasticity to the foreground as they are often not observable in deterministic representations. To this end, we demonstrate an integrated approach to ecosystem modeling from an algorithmic systems biology point of view. We use a modeling interface, called LIME, which allows us to give biologically intuitive models of a plant-pollinator system's descriptions with varying interaction types and patches. Our models, written in a narrative style, are automatically translated into stochastic programming languages. The discrete stochastic nature of the models brings about the possibility to analyze the models with respect to their simulations as well as various graph representations. Our analysis provides an assessment of the functional dynamics of ecosystems with respect to the influence of various interaction patterns and patch links. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Ecosystems

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Intellectual Property Protection for bioinformatics and computational intelligence

Intellectual Property Protection for Bioinformatics and Comp...

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10th Annual ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Fernandez, Dennis Maninang, Antonia Kobayashi, Shumpei Fernandez & Associates LLP Atherton CA 94027 USA Stanford Hosp Stanford CA 94305 USA Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781467358750

bioinformatics and computational biology are two fast-growing fields that require the direct application of computational intelligence. The American patent system is currently going through the biggest reformation since the passage of Patent Act of 1952, and thus intellectual property rights (IPR) and strategies continue to be increasingly vital in these fields. In order to better understand the status quo of intellectual property (IP) specifically in the fields of biology that apply computational intelligence, basic IP definitions, recent IP developments, and advanced protection strategies are presented and discussed.

关键词： computational biology bioinformatics Intellectual Property America Invents Act Patent Portfolio

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