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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Jesneck, Jonathan L. Mukherjee, Sayan Nolte, Loren W. Lokshin, Anna E. Marks, Jeffrey R. Lo, Joseph Department of Biomedical Engineering Department of Statistical Science Computational Biology and Bioinformatics Program Duke Advanced Imaging Labs. Institute for Genome Sciences and Policy Department of Computer Science Duke University Department of Electrical and Computer Engineering Division of Hematology/Oncology University of Pittsburgh School of Medicine University of Pittsburgh Cancer Institute Pittsburgh PA 15213 United States Department of Experimental Surgery Duke University Medical Center Medical Physics Graduate Program Duke University Durham NC 27708 United States

ISBN: (纸本)1424415098

Current mammographic screeningfor breast cancer is less effective for younger women. To complement mammography for premenopausal women, we investigated the feasibility screening test using 98 blood serum proteins. Because the data set was very noisy and contained only weak features, we used a classifier designed for noisy data: decision fusion. Decision fusion outperformed both a support vector machine (SVM) and linear regression with forward stepwise feature selection on all three two-class classification tasks: normal tissue vs. cancer, normal tissue vs. benign lesions, and benign lesions vs. cancer. Decision fusion detected cancer moderately well (AUC=0.84 on normal vs. cancer), demonstrating promise as a screening tool. Decision fusion also detected benign lesions similarly well (AUC=0.83 on normal vs. benign lesions) and was the only classifier to achieve any success in separating benign from malignant lesions (AUC=0.64 on benign vs. cancer). The classification results suggest that the assayed proteins are more indicative of a secondary effect, such as immune response, rather than specific for breast cancer. In conclusion, the decision fusion classifier demonstrated some promise in detecting breast lesions and outperformed other classifiers, especially for the very noisy classification problem of distinguishing benign from malignant lesions. ©2007 IEEE.

关键词： Tissue

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Virtual Robot for Interactive Gait Training Improving Regularity and Dynamic Stability of the Stride Patterns

Virtual Robot for Interactive Gait Training Improving Regula...

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2007 IEEE/ICME International Conference on Complex Medical Engineering-CME2007(CME2007 第二届国际复合医学工程学术大会)

作者： Takeshi Muto -Barbara Herzberger Ernst Poeppel Joachim Hermsdoerfer Yoshihiro Miyake AGU - Aoyama Gakuin University Department of Integrated Information Technology College of Science GRP - Generation Research Program Humanwissenschaftliches Zentrum Ludwig-Maximilians-Universit(a)t TITECH - Tokyo Institute of Technology Department of Computational intelligence and Systems Science

In this paper, we analyzed the gait support procedure of the lower limb's disturbance by our proposed gait support robot "Walk-Mate". "Walk-Mate" is a virtual locomotion robot, which supports the handicapped gait motion by presenting acoustic rhythmic stimuli controlled dynamically by its user's gait motion. We focused on the hemiplegia of lower limb and measured improvement of bilateral asymmetry with Walk-Mate's support. As the results, we found that the improvement procedure is realized by following characteristic two mechanisms;Short-term control with about 10 seconds of timescale for the improvement of the phase characteristics of the footstep rhythm and Long-term control with about a minute of timescale for the improvement of the complexity of gait motion pattern. Therefore the result suggests that Walk-Mate's rhythmic control mechanism is effective to improve the gait disturbance, from the viewpoints of the rhythm and complexity of gait, and that the improvements are realized by a two-level hierarchical control mechanism in interaction with the dynamic environment.

关键词： Robots Stability Legged locomotion Motion control Rhythm Force measurement Pattern analysis Fractals Information technology Educational institutions

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Modeling heterogeneous materials via two-point correlation functions: Basic principles

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Physical Review E 2007年第3期76卷 031110-031110页

作者： Y. Jiao F. H. Stillinger S. Torquato Department of Chemistry Princeton University Princeton New Jersey 08544 USA Princeton Institute for the Science and Technology of Materials Princeton University Princeton New Jersey 08544 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Princeton Center for Theoretical Physics Princeton University Princeton New Jersey 08544 USA

Heterogeneous materials abound in nature and man-made situations. Examples include porous media, biological materials, and composite materials. Diverse and interesting properties exhibited by these materials result from their complex microstructures, which also make it difficult to model the materials. Yeong and Torquato [Phys. Rev. E 57, 495 (1998)] introduced a stochastic optimization technique that enables one to generate realizations of heterogeneous materials from a prescribed set of correlation functions. In this first part of a series of two papers, we collect the known necessary conditions on the standard two-point correlation function S2(r) and formulate a conjecture. In particular, we argue that given a complete two-point correlation function space, S2(r) of any statistically homogeneous material can be expressed through a map on a selected set of bases of the function space. We provide examples of realizable two-point correlation functions and suggest a set of analytical basis functions. We also discuss an exact mathematical formulation of the (re)construction problem and prove that S2(r) cannot completely specify a two-phase heterogeneous material alone. Moreover, we devise an efficient and isotropy-preserving construction algorithm, namely, the lattice-point algorithm to generate realizations of materials from their two-point correlation functions based on the Yeong-Torquato technique. Subsequent analysis can be performed on the generated images to obtain desired macroscopic properties. These developments are integrated here into a general scheme that enables one to model and categorize heterogeneous materials via two-point correlation functions. We will mainly focus on basic principles in this paper. The algorithmic details and applications of the general scheme are given in the second part of this series of two papers.

关键词： RECONSTRUCTING RANDOM-MEDIA PROBABILITY FUNCTIONS NONLINEAR COMPOSITES POROUS-MEDIA BOUNDS MICROSTRUCTURES OPTIMIZATION SCATTERING

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Convergence properties of preconditioned hermitian and skew-hermitian splitting methods for non-hermitian positive semidefinite matrices

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Mathematics of Computation 2007年第257期76卷 287-298页

作者： Bai, Zhong-Zhi Golub, Gene H. Li, Chi-Kwong Department of Mathematics Fudan University Shanghai 200433 China State Key Laboratory of Scientific Acad. of Math. and Syst. Sci. Chinese Acad. of Sci. Beijing 100080 P.O. BOX 2719 China Scientific Computing and Computational Mathematics Program Department of Computer Science Stanford University Stanford CA 94305-9025 United States Department of Mathematics College of William and Mary Williamsburg VA 23187-8795 P.O. Box 8795 United States

For the non-Hermitian and positive semidefinite systems of linear equations, we derive necessary and sufficient conditiorrs for guaranteeing the unconditional convergence of the preconditioned Hermitian and skew-Hermitian splitting iteration methods. We then apply these results to block tridiagonal linear systems in order to obtain convergence conditions for the corresponding block variants of the preconditioned Hermitian and skew-Hermitian splitting iteration methods. © 2006 American Mathematical Society.

关键词： Convergence Hermitian and skew-Hermitian splitting Non-Hermitian matrix Positive semidefinite matrix Splitting iteration method

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AlzPharm: Integration of neurodegeneration data using RDF

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BMC Bioinformatics 2007年第SUPPL. 3期8卷 S4-S4页

作者： Lam, Hugo Y.K. Marenco, Luis Clark, Tim Gao, Yong Kinoshita, June Shepherd, Gordon Miller, Perry Wu, Elizabeth Wong, Gwendolyn T. Liu, Nian Crasto, Chiquito Morse, Thomas Stephens, Susie Cheung, Kei-Hoi Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT United States Center for Medical Informatics Yale University New Haven CT United States Department of Anesthesiology Yale University New Haven CT United States Department of Neurobiology Yale University New Haven CT United States Department of Molecular Cellular and Developmental Biology Yale University New Haven CT United States Department of Genetics Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States Initiative in Innovative Computing Harvard University Cambridge MA United States Massachusetts General Hospital Boston MA United States Alzheimer Research Forum Burlington MA United States Oracle Burlington MA United States

Background: Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. Results: We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. Conclusion: Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results su

关键词： Data integration

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Comparison of experimental and computed protein anisotropic temperature factors

Comparison of experimental and computed protein anisotropic ...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Lei Yang Guang Song Robert L. Jernigan Program of Bioinformatics and Computational Biology Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA USA Department of Computer Science Iowa State University Ames IA USA Department of Biochemistry Biophysics and Molecular Biology L. H. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA USA

Because of its appealing simplicity, the anisotropic network model (ANM) has been widely accepted and applied to study many molecular motion problems: such as the molecular mechanisms of GroEL-GroES function, allosteric changes in hemoglobin, ribosome motion, motor-protein motions, and conformational changes in general, etc. However, the validity of the ANM has not been closely examined. In this work, we use ANM to predict the anisotropic temperature factors of proteins. On the flip side, the rich, directional anisotropic temperature factor data available for hundreds of proteins in the protein data bank (PDB) are used as validation data to closely test the ANM model. The significance of this work is that it presents a timely, important evaluation of the model, shows the extent of its accuracy in reproducing experimental anisotropic temperature factors, and suggests ways to improve the model. An improved model will help us better understand the internal dynamics of proteins, which in turn can greatly expand the usefulness of the models, which has already been demonstrated in many applications.

关键词： Proteins Anisotropic magnetoresistance Temperature Fluctuations computational biology Biology computing Computer networks Bioinformatics Biochemistry Biophysics

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Epileptic seizure detection

Epileptic seizure detection

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作者： Schuyler, Ronald White, Andrew Staley, Kevin Cios, Krzysztof J. Computational Bioscience Ph.D. Program UCDHSC United States University of Colorado United States American Epilepsy Society Society for Neuroscience United States Department of Neurology Massachusetts General Hospital Harvard Medical School United States Staley Lab. United States University of Colorado at Denver Health Sciences Center United States University of Colorado Bioenergetics Institute United States Data Mining and Bioinformatics Laboratory Turkey Polish Academy of Arts and Sciences Poland Dept. of Computer Science and Engineering University of Colorado at Denver Health Sciences Center P.O. Box 173364 Denver CO 80217-3364 United States

No abstract available

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NIPS workshop on new problems and methods in computational biology

NIPS workshop on new problems and methods in computational b...

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作者： Chechik, Gal Leslie, Christina Noble, William Stafford Rätsch, Gunnar Morris, Quaid Tsuda, Koji Computer Science Department Stanford University 353 Serra Mall Stanford CA 94305 United States Computational Biology Program Memorial Sloan-Kettering Cancer Center 1275 York Ave New York NY 10065 United States Department of Genome Sciences University of Washington 1705 NE Pacific St Seattle WA 98109 United States Friedrich Miescher Laboratory of the Max Planck Society Spemannstr. 39 72076 Tübingen Germany Terrence Donnelley Centre for Cellular and Biomolecular Research University of Toronto 160 College Street Toronto ON M5S 3E1 Canada Max Planck Institute for Biological Cybernetics Spemannstr. 38 72076 Tübingen Germany

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Oscillator models and collective motion

IEEE Control Systems Magazine

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IEEE Control Systems Magazine 2007年第4期27卷 89-105页

作者： Paley, Derek A. Leonard, Naomi Ehrich Sepulchre, Rodolphe Grünbaum, Daniel Parrish, Julia K. Princeton University Department of Mechanical and Aerospace Engineering Princeton NJ 08544 United States Department of Mechanical and Aerospace Engineering Program in Applied and Computational Mathematics Princeton University Princeton NJ 08544 United States Department Electrical Engineering and Computer Science University of Liège Liège Belgium School of Oceanography Department of Biology University of Washington Seattle WA United States Biology Department School of Aquatic and Fishery Sciences University of Washington Seattle WA United States

A description of the so called "particles with coupled oscillator dynamics" (PCOD) is presented which is used to model, analyze and synthesize collective motion. An oscillator model with spatial dynamics is presented to help describe how to design steering control laws while it is being used to study biological collectives. Lastly, both engineering and biological analysis were described.

关键词： Oscillators (mechanical)

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37

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Journal of Surgical Research 2007年第2期137卷 166-166页

作者： Shin, J. Yuan, Z. Fordyce, K. Sreeramoju, P. Kent, T. Kim, J. Wang, V. Schneyer, D. Weber, T.K. Departments of Surgery & Molecular Genetics Albert Einstein College of Medicine New York NY Computational Decision Science Group IBM USA Burlington VT Department of Surgery University of Pittsburgh Medical Center Pittsburgh PA Honors Biology Research Program Horace Greeley High School Chappaqua NY

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