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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Alterovitz, Gil Muso, Taro Malalur, Paresh Ramoni, Marco F. Division of Health Sciences and Technology Harvard Medical School Massachusetts Institute of Technology Boston MA United States Children's Hospital Informatics Program Boston MA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Harvard Partners Center for Genetics and Genomics Harvard Medical School Boston MA United States

ISBN: (纸本)1424415098

The accumulation of genomic and proteomic data of many organisms presents an opportunity to analyze entire phylogenetic trees in a systematic, quantified manner. The universal tree of life, constructed by genomic data, provides an evolutionary context for proteomic data of individual organisms. Using proteomic information mapped to biological functions, we survey the tree of life for evolutionary trends, where trends are low p-value linear regressions of relative protein diversity against an evolutionary timeline. These trends provide quantified perspectives across evolution that would be otherwise difficult to discern from the large amount of available bioinformatic data. We analyzed 22,762 functions across 167 proteomes for approximately 3.8 million information content calculations. Of particular interest is the rise of protein-protein interactions along the path of mammalian evolution. Of general interest are the numerous uncovered trends that open the door to future queries of focused scope. ©2007 IEEE.

关键词： Information theory

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Decision fusion of circulating markers for breast cancer detection in premenopausal women

Decision fusion of circulating markers for breast cancer det...

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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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A new implementation method of the reverse engineering algorithm to elucidate gene functional dependencies

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2007年第10期127卷 1712-1718+32页

作者： Inaoka, Hidenori Fukuoka, Yutaka Ueno, Akinori Kohane, Isaac S. Institute of Biomaterials and Biomedical Engineering Tokyo Medical and Dental University 2-3-10 Kanda-Surugadai Chiyoda-ku Tokyo 101-0062 Japan School of Biomedical Science Tokyo Medical and Dental University 1-5-45 Yushima Bunkyo-ku Tokyo 113-8510 Japan Graduate School of Science and Engineering Tokyo Denki University Ishizaka Hatoyama-Cho Hiki-Gun Saitama 350-0394 Japan Informatics Program Childrens's Hospital Harvard Medical School Boston MA United States

Various clustering methods are applied to characterizing gene expression data from DNA microarrays. However, to elucidate functional dependencies of genes analysis of their associations is important. The REVerse engineering ALgorithm (REVEAL) was developed for analyzing the functional dependencies. Although the algorithm has been tested using binary models of genetic networks, it remains unclear how the method or similar technology will operate with systems of continuous variables. In this study, first, the REVEAL was examined using noisy, continuous data and the results suggested that its application to such data required considerable refinement of the algorithm. Then a new implementation method of REVEAL was proposed. This implementation method was tested through numerical experiments. The results of the simulations demonstrated the potential of the proposed method for extracting gene associations.

关键词： Reverse engineering

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Linking protein mass with function via organismal massome networks

Linking protein mass with function via organismal massome ne...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Alterovitz, Gil Lyashenko, Eugenia Xiang, Michael Ramoni, Marco F. Division of Health Sciences and Technology Harvard Medical School Massachusetts Institute of Technology Boston MA United States Children's Hospital Informatics Program at Health Sciences and Technology Harvard Medical School Boston MA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Harvard Partners Center for Genetics and Genomics Harvard Medical School Boston MA United States Department of Biomedical Informatics Columbia University New York NY United States

ISBN: (纸本)1424415098

With the human genome sequenced, attention has been shifting to proteins and their function. Several technologies including mass spectrometry and gel electrophoresis have traditionally been used to study proteins. These technologies rely on proteins' masses to characterize and/or identify them. Once identified, the discovered proteins' are often analyzed for their functional relevance. In this paper, we present an alternative approach to studying protein function directly, based on protein mass. By analyzing proteins with similar mass ranges (bins), we discovered that certain biological functions are associated with specific mass bins more frequently than would be expected by chance. Biological functional classes found in this manner could be seen across the three examined organisms: human, worm and fly. We found no sequence-based homology across significant mass-function proteins in the three different organisms. This investigation leads to useful property that the mass-based biological functional classes are preserved across the organisms. Thus, this paper describes a potential constraint for evolution of similar function based on mass constraints. Finally, this work yields a roadmap for experimental design for functional exploration of proteomes in mass-based technologies such as gel electrophoresis and mass spectrometry. ©2007 IEEE.

关键词： Proteins

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An information theoretic framework for ontology-based bioinformatics

An information theoretic framework for ontology-based bioinf...

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2007 Information Theory and Applications Workshop, ITA

作者： Alterovitz, Gil Xiang, Michael Ramoni, Marco F. Division of Health Sciences and Technology Harvard Medical School Massachusetts Institute of Technology Boston MA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Children's Hospital Informatics Program Boston MA United States Harvard Partners Center for Genetics and Genomics Harvard Medical School Boston MA United States Department of Biology Massachusetts Institute of Technology Cambridge MA United States

ISBN: (纸本)9780615153148

With myriad information being generated from high-throughput experiments such as microarrays and sequencing technologies, an ever-increasing amount of data is being recorded and analyzed with the help of hierarchical ontologies, such as the Gene Ontology (GO). We have developed a novel framework-based on the well established foundations of information theory - that allows for the evaluation of new types of hypotheses. The framework, encapsulated in Open Biomedical Ontology-Based Exploration and Search (OBOES), has already been applied in the investigation of different kinds of questions. The resulting framework enables the new field of information theoretic ontology-based analysis. We have applied this framework to create methods to re-engineer ontologies, explore fundamental questions on the evolution of biological complexity, determine optimal ontology terms for bioinformatics analysis, and quantify the usefulness of biofluids as proxies for tissues/diseases. In each case, we found that our methods provide novel, significant findings. An open source Java implementation of OBOES is available at: http://***. net.

关键词： Information theory

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Contributors to the Lab on a Chip cell and tissue engineering in microsystems special issue

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Lab on a Chip 2007年第6期7卷 667-671页

作者： Bhatia, Sangeeta N. Chen, Christoper Bashir, Rashid Beebe, David J. Desai, Tejal Gottwald, Eric Khademhosseini, Ali Langer, Robert S. Piel, Matthieu Takayama, Shuichi Thorsen, Todd Tien, Joe Voldman, Joel Vunjak-Novakovic, Gordana Wu, Mingming Yu, Hanry Massachusetts Institute of Technology Harvard-MIT Division of Health Sciences and Technology Department of Electrical Engineering and Computer Science United States Laboratory for Multiscale Regenerative Technologies United States Department of Innovation University of Pennsylvania Department of Bioengineering United States Cell Biology and Physiology Program Cell Growth and Cancer Program Tissue Microfabrication Laboratory Department of Electrical and Computer Engineering and Biomedical Engineering Purdue University United States Department of Electronics University of Glasgow United Kingdom Department of Physiology and Bioengineering University of California San Francisco CA United States California Institute for Quantitative Biomedical Research UCSF/UC Berkeley Graduate Group in Bioengineering United States Laboratory of Therapeutic Micro and Nanotechnology Institute for Biological Interfaces Germany 3D-Cell Culture Techniques Group Institute for Biological Interfaces Germany Department of Medicine and Health Sciences and Technology Massachusetts Institute of Technology (MIT) United States Institut Curie UMR 144 IC/CNRS France Biomedical Engineering Department University of Michigan United States Department of Biomedical Engineering Macromolecular Science and Engineering Program United States Sibley School of Mechanical and Aerospace Engineering School of Chemical and Molecular Engineering Cornell University United States National University of Singapore Singapore Institute of Bioengineering and Nanotechnology A STAR Singapore

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A Conceptual Persistent Healthcare Quality Improvement Process for Software Development Management

A Conceptual Persistent Healthcare Quality Improvement Proce...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jen-Chiun Lin Mei-Ju Su Po-Hsun Cheng Yung-Chien. Weng Sao-Jie Chen Jin-Shin Lai Feipei Lai Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Electronic Engineering and Department of Electrical Engineering National Taiwan University Taipei Taiwan National Taiwan University Hospital National Taiwan University College of Medicine Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Physical Medicine and Rehabilitation and Department of Medical Informatics National Taiwan University Hospital National Taiwan University College of Medicine Taiwan

This paper illustrates a sustained conceptual service quality improvement process for the management of software development within a healthcare enterprise. Our proposed process is revised from Niland's healthcare quality information system (HQIS). This process includes functions to survey the satisfaction of system functions, describe the operation bylaws on-line, and provide on-demand training. To achieve these goals, we integrate five information systems in National Taiwan University Hospital, including healthcare information systems, health quality information system, requirement management system, executive information system, and digital learning system, to form a full Deming cycle. A preliminary user satisfaction survey showed that our outpatient information system scored an average of 71.31 in 2006.

关键词： Medical services Software development management Management information systems Quality management programming Bylaws Management training Hospitals Information management Learning systems

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The generation challenge programme platform: Semantic standards and workbench for crop science

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International Journal of Plant Genomics 2008年第1期2008卷 369601页

作者： Bruskiewich, Richard Senger, Martin Davenport, Guy Ruiz, Manuel Rouard, Mathieu Hazekamp, Tom Takeya, Masaru Doi, Koji Satoh, Kouji Costa, Marcos Simon, Reinhard Balaji, Jayashree Akintunde, Akinnola Mauleon, Ramil Wanchana, Samart Shah, Trushar Anacleto, Mylah Portugal, Arllet Ulat, Victor Jun Thongjuea, Supat Braak, Kyle Ritter, Sebastian Dereeper, Alexis Skofic, Milko Rojas, Edwin Martins, Natalia Pappas, Georgios Alamban, Ryan Almodiel, Roque Barboza, Lord Hendrix Detras, Jeffrey Manansala, Kevin Mendoza, Michael Jonathan Morales, Jeffrey Peralta, Barry Valerio, Rowena Zhang, Yi Gregorio, Sergio Hermocilla, Joseph Echavez, Michael Yap, Jan Michael Farmer, Andrew Schiltz, Gary Lee, Jennifer Casstevens, Terry Jaiswal, Pankaj Meintjes, Ayton Wilkinson, Mark Good, Benjamin Wagner, James Morris, Jane Marshall, David Collins, Anthony Kikuchi, Shoshi Metz, Thomas McLaren, Graham Van Hintum, Theo Crop Research Informatics Laboratory International Rice Research Institute (IRRI) DAPO Box 7777 Metro Manila Philippines Crop Research Informatics Laboratory International Maize and Wheat Improvement Center (CIMMYT) DF 06600 Mexico Apdo. Postal 6-641 Mexico Centre International de Recherche Agronomique Pour le Développement (CIRAD) 34398 Montpellier Cedex 5 Avenue Agropolis France Bioversity International 00057 Maccarese (Fiumicino) Rome Via dei Tre Denari 472/a Italy National Institute for Agrobiological Sciences (NIAS) Tsukuba Ibaraki 305-8602 Kannondai 2-1-2 Japan Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA) Brasilia DF 70770-900 Parque Estaçao Biologia Final W5 Norte Brazil Centro Internacional de la Papa (CIP) Lima 12 Avenida La Molina 1895 La Molina Peru International Crops Research Institute for the Semi-Arid Tropics Patancheru Andhra Pradesh 502324 India International Center for Agricultural Research in the Dry Areas Aleppo P.O. Box 5466 Syrian Arab Republic National Center for Genetic Engineering and Biotechnology 113 Thailand Science Park Klong Luang Pathumthani 12120 Phahonyothin Road Klong 1 Thailand Institute of Computer Science College of Arts and Sciences University of the Philippines Los Banos Laguna 4031 Philippines Department of Computer Science University of the Philippines Melchor Hall Diliman Quezon City 1101 Philippines National Center for Genome Resources Santa Fe NM 87505 2935 Rodeo Park Drive East United States Scottish Crop Research Institute Invergowrie Dundee DD2 5DA United Kingdom Department of Plant Breeding Cornell University Ithaca NY 14853 United States African Centre for Gene Technologies Lynnwood Ridge 0040 P.O. Box 75011 South Africa Department of Medical Genetics Faculty of Medicine University of British Columbia Vancouver BC V6T 1Z3 Canada School of Computing Science Simon Fraser Universtiy Burnaby BC V5A 1S6 8888 University Drive Canada Bioinformatics Graduate Program Genome Sciences Centre BC

The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. A key consortium research activity is the development of a GCP crop bioinformatics platform to support GCP research. This platform includes the following: (i) shared, public platform-independent domain models, ontology, and data formats to enable interoperability of data and analysis flows within the platform;(ii) web service and registry technologies to identify, share, and integrate information across diverse, globally dispersed data sources, as well as to access high-performance computational (HPC) facilities for computationally intensive, high-throughput analyses of project data;(iii) platform-specific middleware reference implementations of the domain model integrating a suite of public (largely open-access/-source) databases and software tools into a workbench to facilitate biodiversity analysis, comparative analysis of crop genomic data, and plant breeding decision making.

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U-care for the elderly: Implemenation of a comprehensive living and health care network

U-care for the elderly: Implemenation of a comprehensive liv...

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HEALTHCOM 2007: Ubiquitous Health in Aging Societies - 2007 9th International Conference on e-Health Networking, Application and Services

作者： Chen, Heng-Shuen Su, Mei-Ju Tsai, Tsung-Hsueh Teng, Shih-Shung Zhang, Han-Wei Jin-Shin, Lai Lai, Fei-Pei Chen, Ching-Yu Department of Medical Informatics National Taiwan University College of Medicine University Hospital Taipei Taiwan Graduate Institute of Electronics Engineering College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Caridinal Tien Hospital Young-Ho Branch Taipei Taiwan Genmart Tech. Co. Taipei Taiwan Department of Physical Medicine and Rehabilitation National Taiwan University College of Medicine University Hospital Taipei Taiwan Dept. of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Family Medicine National Taiwan University College of Medicine University Hospital Taipei Taiwan Medical College University Hospital National Taiwan University No. 1 Ren-ai Rd Taipei 100 Taiwan

ISBN: (纸本)142440942X

Taiwan started to be an 'aging society' from 1993, and the aging process will progress more rapidly due to markedly decreased child birth in recent year. Facing very high old age dependency ratio as 1 to 2 in year 2030 which means every two working age persons (age 15 - 64 years) have to support one elderly people. That would be an enormous burden for every family and society. In this study a U-Care system was developed and implemented in a satellite city region of Taipei metropolitan area in Taiwan, to provide a comprehensive living and health care service network for the elderly. The u-Health research team of National Taiwan University and Hospital collaborate with Cardinal Tien Hospital Young-Ho Branch, 17 primary care doctors and two living care service provider partners to provide integrated and continuous services for elderly people in home, community and institutional setting. © 2007 IEEE.

关键词： Ubiquitous computing

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Enabling a strong U.S. engineering workforce for technological innovation: A national partnership in graduate professional education with industry to enhance U.S. competitiveness and economic development

Enabling a strong U.S. engineering workforce for technologic...

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114th Annual ASEE Conference and Exposition, 2007

作者： Keating, Donald Stanford, Thomas Bardo, John Dunlap, Duane DeLoatch, Eugene Tricamo, Stephen Deoew, Dennis Bertoline, Gary Schuver, Mark McHenry, Albert Lindquist, Timothy Tidwell, Joseph Palmer, Harvey Smith, Mark Morrison, Raymond Egbert, Norm Quick, David Olson, Roger Truesdale, Samuel Noori, Mohamad Sullivan, Edward Rencis, Joseph Purdy, Carla University of South Carolina United States Department of Mechanical Engineering University of South Carolina United States ASEE-Graduate Studies Division Department of Chemical Engineering University of South Carolina United States Western Carolina University United States Kimmel School Western Carolina University United States Morgan State University United States School of Engineering Morgan State University United States ASEE New Jersey Institute of Technology United States Department of Industrial and Manufacturing Engineering Purdue University United States College of Technology Purdue University United States Rolls-Royce-Purdue Master's Degree Program Purdue University United States Arizona State University United States Arizona State University Polytechnic United States Boeing Co. United States JACMET Arizona State University Polytechnic United States ASEE-College Industry Partnership Division Rochester Institute of Technology United States Kate Gleason College of Engineering Rochester Institute of Technology United States Lockheed Martin Aeronautics Co. United States Engineering Integration and Strategy Lockheed Martin Aeronautics Company United States ASEE-Corporate Rolls-Royce Corporation United States Rolls-Royce North American Technologies United States California State Polytechnic University United States College of Engineering California State Polytechnic University United States Graduate Programs and Research College of Engineering California State Polytechnic University United States University of Arkansas United States Department of Mechanical Engineering University of Arkansas Fayetteville United States ASEE-Mechanical Engineering Division ASME Mechanical Engineering Department University of Cincinnati United States Department of Electrical and Computer Engineering University of Cincinnati United States

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