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International Conference on Computational Intelligence, Modelling and Simulation (CSSIM)

作者： Muhammad Nur Latief Ford Lumban Gaol Bambang Heru Iswanto Graduate Program in Informatics Engineering Bina Nusantara University Jakarta Indonesia Bina Nusantara University Jakarta Indonesia Physics Department Universitas Negeri Jakarta Jakarta Indonesia

In this research will show a method for sound-recognition with artificial neural network back propagation concept. The artificial neural network use sigmoid activation function to all layer. Steps to the extraction, first, divide into ten component, second do the Fast Fourier Transform, third continue with Power Spectral Density, fourth count the average. The end result show that the pattern will recognize by the Artificial Neural Network. Extraction performed, as well as Fourier transform, and also calculate the power density, and then process them with artificial neural networks, allows the system to do the identification of voice data Gamelan Bonang using In this research will show a method for sound-recognition with artificial neural network back propagation concept.

关键词： Artificial neural networks Training Neurons Backpropagation Frequency domain analysis Fourier transforms Transfer functions

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An efficient ICA approach based on neural network framework for biomedical applications

An efficient ICA approach based on neural network framework ...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Lin, Yue-Der Hsu, Chih-Yu Chen, Hung-Yun Tseng, Kuo-Kun Department of Automatic Control Engineering Master Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Wufeng Taichung County Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Computer Science and Engineering HungKuang University Taichung Taiwan

ISBN: (纸本)9781424469178

The paper developed a block-wise approach for ICA algorithms which can improve the computational efficiency of ICA without the degradation of performance for the separation of biomedical signals. Source signals including electrocardiogram (ECG), electromyogram (EMG) and 60-Hz sinusoid are linearly mixed for experimental tests. The mean-square errors (MSE) between the original sources and the separated signals are calculated for the evaluation of separation performance. These results demonstrated that the proposed block-wise approach can achieve the desired separation performance of signals in a more efficient way. © 2010 IEEE.

关键词： Independent component analysis

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Maize in a changing climate-identification of physiological and genetic mechanisms associated with tolerance to heat and drought stress

Maize in a changing climate-identification of physiological ...

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第三届植物分子育种国际会议

作者： Cairns JE Araus JL Sanchez C Sonders K Wang J Li H Atlin GN Yan J Global Maize Program International Maize and Wheat Improvement Centre (CIMMYT) Socioeconomics Program International Maize and Wheat Improvement Centre (CIMMYT) Crop Research Informatics Laboratory International Maize and Wheat Improvement Centre Institute of Crop ScienceChinese Academy of Agricultural Sciences

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A fuzzy inference system to determine the number of clones in the clonal selection algorithm

A fuzzy inference system to determine the number of clones i...

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World Congress on Nature & Biologically Inspired Computing (NaBIC)

作者： Luiz Antonio Carraro Leandro Nunes de Castro Angelita Maria de Ré Computer and Informatics Faculty & Graduate Program in Electrical Engineering Mackenzie University São Paulo Brazil Computer Science Department State University of West-Center Parana Guarapuava Brazil

Artificial Immune Systems (AISs) are composed of techniques inspired by immunology. The clonal selection principle ensures the organism adaptation to fight invading antigens by an immune response activated by the binding of antigens and antibodies. As an immune response can be elicited even when the binding between an antigen and an antibody is not perfect, an approximate binding might suffice, and a Fuzzy Logic mechanism might be the most appropriate mechanism to control such process. This paper presents a novel hybrid model based on concepts of Immune and Fuzzy Systems with applications to pattern recognition problems. The preliminary results obtained here suggest the proposed model is a promising pattern recognition tool.

关键词： Variable speed drives Animals

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A Decision Support System for Medium-Sized Emergencies

A Decision Support System for Medium-Sized Emergencies

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Computer Science Society (SCCC) International Conference Chilean FLAGGED

作者： Andre Engelbrecht Adriana S. Vivacqua Marcos R.S. Borges Alvaro Monares Sergio F. Ochoa Jose A. Pino Post-graduate Program on Informatics (PPGI) Federal University of Rio de Janeiro Rio de Janeiro Rio de Janeiro Brazil Department of Computer Science Universidad de Chile Santiago Chile

The decisions made by the operation commander in emergency situations should be made quickly to save lives. To avoid late or bad decisions, the commander must construct a situational awareness. The irregular arrival flow of information, uncertainty, information overload and lack of persistence are the main factors that hinder this task. To minimize these effects we propose an architecture composed of mobile devices and a decision support system to be used in the command post. The main point in the system design is the cognitive overload. Therefore, heuristics about the usage of the information by experienced commanders were elicited and implemented.

关键词： Decision support systems Decision making Vehicles Mobile handsets Humans Ontologies Availability

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Inferring nonstationary gene networks from temporal gene expression data

Inferring nonstationary gene networks from temporal gene exp...

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IEEE Workshop on Signal Processing Systems (SIPS)

作者： Hsun-Hsien Chang Jonathan J. Smith Marco F. Ramoni Children's Hospital Informatics Program Harvard MIT Division Health Sciences and Technology Harvard Medical School Boston MA USA Department of Mathematics Massachusetts Institute of Technology Cambridge MA USA

Reverse-engineering transcriptional networks from longitudinal expression profiles is a crucial step towards the study of gene regulatory mechanisms. Genes dynamically orchestrate to each other, the stationarity assumption made by existing methods of transcriptional interaction inference is no longer adequate. As such, we need a new approach to handle the nonstationary behavior in gene expression. On the other hand, microarrays for human studies are equipped with a large number of probe sets, leading the inference of dynamic networks to a computationally intensive task. Hence, there is a need to design the inference algorithm in a tractable manner. This paper develops a Bayesian network approach to inferring the nonstationary transcriptional interactions. The applications of our approach to a clinical study of mechanical periodontal therapy demonstrates a significant improvement over stationary models. Our nonstationary network model also explains the anti-inflammatory effect of mechanical periodontal therapy.

关键词： Medical treatment Gene expression Bioinformatics Biological system modeling Markov processes Mathematical model Bayesian methods

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Quality control procedures for genome-wide association studies

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Current Protocols in Human Genetics 2011年第SUPPL.68期Chapter 1卷 Unit1.19页

作者： Turner, Stephen Armstrong, Loren L. Bradford, Yuki Carlsony, Christopher S. Crawford, Dana C. Crenshaw, Andrew T. de Andrade, Mariza Doheny, Kimberly F. Haines, Jonathan L. Hayes, Geoffrey Jarvik, Gail Jiang, Lan Kullo, Iftikhar J. Li, Rongling Ling, Hua Manolio, Teri A. Matsumoto, Martha McCarty, Catherine A. McDavid, Andrew N. Mirel, Daniel B. Paschall, Justin E. Pugh, Elizabeth W. Rasmussen, Luke V. Wilke, Russell A. Zuvich, Rebecca L. Ritchie, Marylyn D. Center for Human Genetics Research Department of Molecular Physiology and Biophysics Vanderbilt University Nashville TN United States Division of Endocrinology Metabolism and Molecular Medicine Feinberg School of Medicine Northwestern University Chicago IL United States Cancer Prevention Public Health Sciences Fred Hutchinson Cancer Research Center Seattle WA United States Genetic Analysis Platform and Program in Medical and Population Broad Institute Cambridge MA United States Division of Biostatistics and Informatics Department of Health Sciences Research Mayo Clinic College of Medicine Rochester MN United States Center for Inherited Disease Research Johns Hopkins University Baltimore MD United States Department of Genome Sciences University of Washington Seattle WA United States Division of Cardiovascular Diseases Department of Medicine Mayo Clinic Rochester MN United States Office of Population Genomics National Human Genome Research Institute National Institutes of Health Bethesda MD United States Biomedical Informatics Research Center Marshfield Clinic Research Foundation Marshfield WI United States National Center for Biotechnology Information National Library of Medicine National Institutes of Health Bethesda MD United States Division of Clinical Pharmacology Department of Medicine Vanderbilt University Nashville TN United States

Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of complex disease. Regardless of context, the practical utility of this information will ultimately depend upon the quality of the original data. Quality control (QC) procedures for GWAS are computationally intensive, operationally challenging, and constantly evolving. Here we enumerate some of the challenges in QC of GWAS data and describe the approaches that the electronic MEdical Records and Genomics (eMERGE) network is using for quality assurance in GWAS data, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of GWAS data, including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We propose best practices and discuss areas of ongoing and future research. © 2011 by John Wiley & Sons, Inc.

关键词： Biobanks Electronic medical records eMERGE Genome-wide association studies GWAS quality control QC

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Active machine learning for transmembrane helix prediction

Active machine learning for transmembrane helix prediction

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8th Asia-Pacific Bioinformatics Conference, APBC 2010

作者： Osmanbeyoglu, Hatice U. Wehner, Jessica A. Carbonell, Jaime G. Ganapathiraju, Madhavi K. Department of Biomedical Informatics University of Pittsburgh School of Medicine Pittsburgh PA United States Department of Mathematics University of North Carolina Chapel Hill NC United States Language Technologies Institute Carnegie Mellon University Pittsburgh PA United States Intelligent Systems Program University of Pittsburgh School of Art and Sciences Pittsburgh PA United States

Background: About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution structure can aid in predicting the structure of many previously unresolved proteins are therefore of potentially high value. Active machine learning is a supervised machine learning approach which is suitable for this domain where there are a large number of sequences but only very few have known corresponding structures. In essence, active learning seeks to identify proteins whose structure, if revealed experimentally, is maximally predictive of others. Results: An active learning approach is presented for selection of a minimal set of proteins whose structures can aid in the determination of transmembrane helices for the remaining proteins. TMpro, an algorithm for high accuracy TM helix prediction we previously developed, is coupled with active *** show that with a well-designed selection procedure, high accuracy can be achieved with only few proteins. TMpro, trained with a single protein achieved an F-score of 94% on benchmark evaluation and 91% on MPtopo dataset, which correspond to the state-of-the-art accuracies on TM helix prediction that are achieved usually by training with over 100 training proteins. Conclusion: Active learning is suitable for bioinformatics applications, where manually characterized data are not a comprehensive representation of all possible data, and in fact can be a very sparse subset thereof. It aids in selection of data instances which when characterized experimentally can improve the accuracy of computational characterization of remaining raw data. The results presented here also demonstrate that the feature extraction method of TMpro

关键词： Proteins

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Principles of Bioimage informatics: Focus on Machine Learning of Cell Patterns

Principles of Bioimage Informatics: Focus on Machine Learnin...

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Workshop of the BioLINK Special Interest Group on Linking Literature, Information and Knowledge for Biology

作者： Coelho, Luis Pedro Glory-Afshar, Estelle Kangas, Joshua Quinn, Shannon Shariff, Aabid Murphy, Robert F. Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology United States Lane Center for Computational Biology Carnegie Mellon University United States Center for Bioimage Informatics Carnegie Mellon University United States Department of Biological Sciences Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Department of Biomedical Engineering Carnegie Mellon University United States

ISBN: (纸本)9783642131301

The field of bioimage informatics concerns the development and use of methods for computational analysis of biological images. Traditionally, analysis of such images has been done manually. Manual annotation is, however, slow, expensive, and often highly variable from one expert to another. Furthermore, with modern automated microscopes, hundreds to thousands of images can be collected per hour, making manual analysis infeasible. This field borrows from the pattern recognition and computer vision literature (which contain many techniques for image processing and recognition), but has its own unique challenges and tradeoff's. Fluorescence microscopy images represent perhaps the largest class of biological images for which automation is needed. For this modality, typical problems include cell segmentation, classification of phenotypical response, or decisions regarding differentiated responses (treatment vs. control setting). This overview focuses on the problem of subcellular location determination as a running example, but the techniques discussed are often applicable to other problems.

关键词： Pattern recognition

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Analysis of cancer omics data in a semantic web framework

Analysis of cancer omics data in a semantic web framework

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Workshop on Semantic Web Applications and Tools for Life Sciences 2010, SWAT4LS 2010

作者： Holford, Matthew E. McCusker, James P. Cheung, Kei-Hoi Krauthammer, Michael Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT United States Department of Pathology Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States Center for Medical Informatics Yale University New Haven CT United States Department of Genetics Yale University New Haven CT United States

Our work concerns the elucidation of the cancer (epi)genome, transcriptome and proteome to better understand the complex interplay between a cancer cell's molecular state and its response to anti-cancer therapy. To study the problem, we have previously focused on data warehousing technologies and statistical data integration. In this paper, we present recent work on extending our analytical capabilities using Semantic Web technology. A key new component presented here is a SPARQL endpoint to our existing data warehouse. This endpoint allows the merging of observed quantitative data with existing data from semantic knowledge sources such as Gene Ontology (GO). We show how such variegated quantitative and functional data can be integrated and accessed in a universal manner using Semantic Web tools. We also demonstrate how Description Logic (DL) reasoning can be used to infer previously unstated conclusions from existing knowledge bases. As proof of concept, we illustrate the ability of our setup to answer complex queries on resistance of cancer cells to Decitabine, a demethylating agent.

关键词： Semantic Web

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