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2011 ACM Conference on Bioinformatics, Computational biology and Biomedicine, ACM-BCB 2011

作者： Xue, Li C. Jordan, Rafael A. El-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Genetics Development and Cell Biology Iowa State University Ames 50011 United States Department of Computer Science Iowa State University Ames IA 50011 United States Department of Computer Science Pontificia Universidad Javeriana Cali Colombia Department of Systems and Computer Engineering AI-Azhar University Cairo Egypt

ISBN: (纸本)9781450307963

Computational protein-protein docking is a valuable tool for determining the conformation of complexes formed by interacting proteins. Selecting near-native conformations from the large number of possible models generated by docking software presents a significant challenge in practice. We introduce a novel method for ranking docked conformations based on the degree of overlap between the interface residues of a docked conformation formed by a pair of proteins with the set of predicted interface residues between them. Our approach relies on a method, called PS-HomPPI, for reliably predicting proteinprotein interface residues by taking into account information derived from both interacting proteins. PS-HomPPI infers the residues of a query protein that are likely to interact with a partner protein based on known interface residues of the homo-interologs of the query-partner protein pair, i.e., pairs of interacting proteins that are homologous to the query protein and partner protein. Our results on Docking Benchmark 3.0 show that the quality of the ranking of docked conformations using our method is consistently superior to that produced using ClusPro cluster-size-based and energy-based criteria for 61 out of the 64 docking complexes for which PS-HomPPI produces interface predictions. An implementation of our method for ranking docked models is freely available at: http://***/DockRank/. Copyright © 2011 ACM.

关键词： Proteins

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Improving protein-RNA interface prediction by combining sequence homology based method with a naive bayes classifier: Preliminary results 11

Improving protein-RNA interface prediction by combining sequ...

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2011 ACM Conference on Bioinformatics, Computational biology and Biomedicine, ACM-BCB 2011

作者： Xue, Li C. Walia, Rasna EL-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States Department of Computer Science Iowa State University Ames IA 50011 United States Department of Systems and Computer Engineering Al-Azhar University Cairo Egypt

ISBN: (纸本)9781450307963

Protein-RNA interactions play important roles in cellular processes like protein synthesis, RNA processing, and gene expression regulation. Reliable identification of the interfaces involved in RNA-protein interactions is essential for comprehending the mechanisms and the functional implications of these interactions and provides a valuable guide for rational drug discovery and design. Because the determination of 3D structures of protein-RNA complexes has various technical limitations and is typically costly, reliable in silico interface prediction methods that require only the sequence information are urgently needed. We present HomPRIP, a homologous sequence based method for predicting protein-RNA interfaces, based on our conservation analysis of protein-RNA interfaces. We test Hom-PRIP on a benchmark dataset of 199 proteins and compare it with the state-of-the-art protein-RNA interface prediction methods. Our results show that HomPRIP can reliably identify protein-RNA interface residues in 71% of test proteins with at least one putative sequence homolog passing the similarity thresholds of HomPRIP. Moreover, to facilitate predictions for proteins with no identified homologs, we develop HomPRIP-NB, a method combining the HomPRIP predictor and a Naive Bayes (NB) classifier trained using evolutionary information derived from alignments against the NCBI nr database. Our results suggest that HomPRIP-NB significantly outperforms the state-of-the-art machine learning methods for predicting protein-RNA interface residues. Copyright © 2011 ACM.

关键词： Proteins

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From market baskets to mole rats: Using data mining techniques to analyze RFID data describing mole rat behavior 11

From market baskets to mole rats: Using data mining techniqu...

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17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011

作者： McCloskey, Dan P. Kress, Michael E. Imberman, Susan P. Kushnir, Igor Briffa-Mirabella, Susan Dept. Psychology College of Staten Island 2800 Victory Blvd Staten Island NY 10314 United States CSI/IBR Center for Developmental Neuroscience College of Staten Island 2800 Victory Blvd Staten Island NY 10314 United States College of Staten Island Department of Technology Systems 2800 Victory Blvd Staten Island NY 10314 United States College of Staten Island Dept. Computer Science 2800 Victory Blvd Staten Island NY 10314 United States College of Staten Island Masters Program In Neuroscience 2800 Victory Blvd Staten Island NY 10314 United States Program in Neuroscience Department of Biology Graduate Center at CUNY 2800 Victory Blvd Staten Island NY 10314 United States Program Computer Science Graduate Center at CUNY College of Staten Island 2800 Victory Blvd Staten Island NY 10314 United States

ISBN: (纸本)9781450308137

The use of new technologies, such as RFID sensors, provides scientists with novel ways of doing experimental research. As scientists become more technologically savvy and use these techniques, the traditional approaches to data analysis fail given the huge amounts of data produced by these methods. In this paper we describe an experiment in which colonies of naked mole rats were tagged with RFID transponders. RFID sensors were strategically placed in the mole rat caging system. The goal of this experiment was to document and analyze the interactions between animals. The huge amount of data produced by the sensors was not analyzable using the traditional methods employed by behavioral neuroscience researchers. Computational methods used by data miners, such as cluster analysis, association rule mining, and graphical models, were able to scale to the data and produce knowledge and insight that was previously unknown. This paper describes in detail the experimental setup and the computational methods used. Copyright 2011 ACM.

关键词： Cluster analysis

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Abstracts from the 2011 Joint Annual Meeting of the Society for Northwestern Vertebrate biology and Washington Chapter of the Wildlife Society, Held at the Wesley Inn, Gig Harbor, Washington, March 23–25, 2011

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Northwestern Naturalist 2011年第2期92卷 136-166页

作者： Michael J. Adams Christopher A. Pearl Jessica Y. Adkins Daniel D. Roby Ladd Bayliss Peter J. Loschl Donald E. Lyons Yasuko Suzuki Hannah E. Anderson Sarah T. Hamman Keith B. Aubry Catherine M. Raley John J. Rohrer Eric C. Lofroth Scott H. Fitkin Michael L. Best Hartwell H. Welsh Jamie B. Bettaso Don T. Ashton Hartwell H. Welsh Scott McBain Lindsay M. Biga Andrew R. Blaustein Susan F. Brady R. Steven Wagner Daniel J. Selski James E. Johnson R. Bruce Bury Wynn W. Cudmore Hartwell H. Welsh Nathan D. Chelgren Tara Chestnut Chaucney W. Anderson Julie Kirshtein Megan T. Cook Tiffany S. Garcia Stephen Corn Erin Muths Amanda Kissel Rick Scherer Eric Delvin Jonathan D. Bakker Peter W. Dunwiddie Diana L. Dishman Deborah A. Duffield Harriet R. Huber Dyanna M. Lambourn Karen M. Dvornich Greg Falxa Gary M. Fellers Patrick M. Kleeman David Miller Brandon Fessler Jason T. Irwin Daniel D. Beck R. Steven Wagner Marc P. Hayes John A. Frew Christian E. Grue Chris R. Friesen Robert T. Mason Stevan J. Arnold Suzanne Estes Tiffany S. Garcia Katie M. Dugger Josh Johnson Mike Rochelle A. J. Kroll Stephanie S. Gervasi Caren S. Goldberg Dan Davis Erica Bree Rosenblum Bill Bosworth Lisette P. Waits Purnima Govindarajulu Gregory A. Green Steve Negri Jennifer R. Griffiths Daniel E. Schindler Christian E. Grue Karen M. Dvornich Adam G. Hansen David A. Beauchamp Casey M. Baldwin Marc P. Hayes Gretchen Padgett-Flohr Blake R. Hossack Winsor H. Lowe Paul Stephen Corn Aaron N. Johnston Kris Kendell Dave Prescott Scott Stevens Peter M. Kiffney Carol Volk Kerensa A. King Christian E. Grue James M. Grassley Andrew J. Kroll Matt Hane Josh Johnston Mike Rochelle Stuart Farber Niels Leuthold Michael J. Adams John P. Hayes Bo Li Michael T. Brett Kent B. Livezey Ilon E. Logan Teresa J. Lorenz Kimberly A. Sullivan Amanda V. Bakian Deanna Lynch Nancy Thomas Krysten Schuler Steven Jeffries Joshua Malt Kelly R. McAllister Joanne P. Schuett-Hames Aimee P. McIntyre Jay E. Jones Eric M. Lund Frithiof T. Waterstrat Jack N. Giovanini Stephen D. Duke Marc P. Hayes US Forest Service Pacific Northwest Research Station Olympia WA 98512 kaubry@fs.fed.us US Forest Service Okanogan-Wenatchee National Forest Methow Valley Ranger District Winthrop WA 98862 British Columbia Ministry of Environment Victoria BC V8W9M1 Washington Department of Fish and Wildlife Winthrop WA 98862 Humboldt State University 1 Harpst St. Arcata CA 95521 addsmike14@*** US Forest Service Pacific Southwest Research Station 1700 Bayside Dr. Arcata CA 95521 hwelsh@fs.fed.us US Fish and Wildlife Service Arcata Field Office 1655 Heindon Road Arcata CA 95521 jamie_bettaso@fws.gov Redwood Sciences Laboratory US Forest Service1700 Bayview Drive Arcata CA 99521 dashton@fs.fed.us hwelsh@fs.fed.us McBain &amp Trush 980 7th Street Arcata CA 95521 scott@*** Oregon State University Environmental Sciences Graduate Program Corvallis OR 97331 lindsay.biga@science.oregonstate.edu Oregon State University Department of Zoology Corvallis OR 97331 US Geological Survey Forest and Rangeland Ecosystem Science Center 3200 SW Jefferson Way Corvallis OR 97331 buryb@usgs.gov Life Science Department Chemeketa Community College Salem OR 97309 wynn.cudmore@chemeketa.edu US Forest Service Redwood Sciences Laboratory 1700 Bayview Drive Arcata CA 95521 hwelsh@fs.fed.us USGS Oregon Water Science Center 2130 SW 5th Avenue Portland OR 97207 chestnut@usgs.gov USGS Headquarters 12201 Sunrise Valley Drive Reston VA 20192 US Geological Survey Northern Rocky Mountain Science Center Aldo Leopold Wilderness Research Institute 790 E. Beckwith Avenue Missoula MT 59801 scorn@usgs.gov US Geological Survey Fort Collins Science Center 2150 Centre Avenue Building C Fort Collins CO 80526 Department of Biological Sciences Simon Fraser University Burnaby BC V5A 1S6 Colorado State University Department of Fishery and Wildlife Biology Fort Collins CO 80523 The Nature Conservancy 120 Union Ave SE Olympia WA 98501 edelvin@tnc.org School of Forest Resources University of Washington Seattle WA

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Optimized expected information gain for nonlinear dynamical systems 09

Optimized expected information gain for nonlinear dynamical ...

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26th International Conference On Machine Learning, ICML 2009

作者： Busetto, Alberto Giovanni Ong, Cheng Soon Buhmann, Joachim M. Department of Computer Science ETH Zurich Universitätstr. 6 8092 Zurich Switzerland Competence Center for Systems Physiology and Metabolic Diseases Schafmattst. 18 8093 Zurich Switzerland Life Science Zurich PhD Program on Systems Biology of Complex Diseases Switzerland

ISBN: (纸本)9781605585161

This paper addresses the problem of active model selection for nonlinear dynamical systems. We propose a novel learning approach that selects the most informative subset of time-dependent variables for the purpose of Bayesian model inference. The model selection criterion maximizes the expected Kullback-Leibler divergence between the prior and the posterior probabilities over the models. The proposed strategy generalizes the standard D-optimal design, which is obtained from a uniform prior with Gaussian noise. In addition, our approach allows us to determine an information halting criterion for model identification. We illustrate the benefits of our approach by differentiating between 18 published biochemical models of the TOR signaling pathway, a model selection problem in systems biology. By generating pivotal selection experiments, our strategy outperforms the standard A-optimal, D-optimal and E-optimal sequential design techniques.

关键词： Nonlinear dynamical systems

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Meeting Report from the Second "Minimum Information for Biological and Biomedical Investigations" (MIBBI) workshop

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Standards in genomic sciences 2010年第3期3卷 259-66页

作者： Carsten Kettner Dawn Field Susanna-Assunta Sansone Chris Taylor Jan Aerts Nigel Binns Andrew Blake Cedrik M Britten Ario de Marco Jennifer Fostel Pascale Gaudet Alejandra González-Beltrán Nigel Hardy Jan Hellemans Henning Hermjakob Nick Juty Jim Leebens-Mack Eamonn Maguire Steffen Neumann Sandra Orchard Helen Parkinson William Piel Shoba Ranganathan Philippe Rocca-Serra Annapaola Santarsiero David Shotton Peter Sterk Andreas Untergasser Patricia L Whetzel Beilstein-Institut Frankfurt am Main Germany Centre for Ecology & Hydrology UK Oxford e-Research Centre University of Oxford UK The European Bioinformatics Institute (EBI) UK Faculty of Engineering - ESAT/SCD Leuven University Leuven-Heverlee Belgium Division of Pathway Medicine University of Edinburgh Medical School Edinburgh UK MRC Harwell UK Medical Department University Medical Center Johannes Gutenberg University-Mainz Mainz Germany Consortium for Genomic Technology Milano Italy University of Nova Gorica Nova Gorica Slovenia NIEHS NIH US National Toxicology Program USA Swiss Institute of Bioinformatics Geneva Switzerland Computational and Systems Medicine and Department of Computer Science University College London London UK Department of Computer Science Aberystwyth University Aberystwyth UK Center for Medical Genetics Ghent University Ghent Belgium Department of Plant Biology University of Georgia Athens USA Department of Stress- and Developmental Biology Institute for Plant Biochemistry Halle Germany Peabody Museum of Natural History Yale University New Haven USA Macquarie University Sydney Australia National University of Singapore Singapore Cancer Pharmacology The Mario Negri Institute for Pharmacological Research Milan Italy Image Bioinformatics Research Group Department of Zoology University of Oxford Oxford UK Wellcome Trust Sanger Institute UK Zentrum für Molekulare Biologie der Universität Heidelberg Heidelberg Germany The National Center for Biomedical Ontology / Stanford Center for Biomedical Informatics Research Stanford University Stanford USA

This report summarizes the proceedings of the second workshop of the 'Minimum Information for Biological and Biomedical Investigations' (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany through the sponsorship of the Beilstein-Institute. MIBBI is an umbrella organization uniting communities developing Minimum Information (MI) checklists to standardize the description of data sets, the workflows by which they were generated and the scientific context for the work. This workshop brought together representatives of more than twenty communities to present the status of their MI checklists and plans for future development. Shared challenges and solutions were identified and the role of MIBBI in MI checklist development was discussed. The meeting featured some thirty presentations, wide-ranging discussions and breakout groups. The top outcomes of the two-day workshop as defined by the participants were: 1) the chance to share best practices and to identify areas of synergy; 2) defining a series of tasks for updating the MIBBI Portal; 3) reemphasizing the need to maintain independent MI checklists for various communities while leveraging common terms and workflow elements contained in multiple checklists; and 4) revision of the concept of the MIBBI Foundry to focus on the creation of a core set of MIBBI modules intended for reuse by individual MI checklist projects while maintaining the integrity of each MI project. Further information about MIBBI and its range of activities can be found at http://***/.

关键词： Minimum Information European Bioinformatics Institute Biomedical Ontology Stable Link Breakout Group

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Workshop on imaging science development for cancer prevention and preemption

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Cancer Biomarkers 2007年第1期3卷 1-33页

作者： Kelloff, Gary J. Sullivan, Daniel C. Baker, Houston Clarke, Lawrence P. Nordstrom, Robert Tatum, James L. Dorfman, Gary S. Jacobs, Paula Berg, Christine D. Pomper, Martin G. Birrer, Michael J. Tempero, Margaret Higley, Howard R. Petty, Brenda Gumbs Sigman, Caroline C. Maley, Carlo Sharma, Prateek Wax, Adam Ginsberg, Gregory G. Dannenberg, Andrew J. Hawk, Ernest T. Messing, Edward M. Grossman, H. Barton Harisinghani, Mukesh Bigio, Irving J. Griebel, Donna Henson, Donald E. Fabian, Carol J. Ferrara, Katherine Fantini, Sergio Schnall, Mitchell D. Zujewski, Jo Anne Hayes, Wendy Klein, Eric A. DeMarzo, Angelo Ocak, Iclal Ketterling, Jeffrey A. Tempany, Clare Shtern, Faina Parnes, Howard L. Gomez, Jorge Srivastava, Sudhir Szabo, Eva Lam, Stephen Seibel, Eric J. Massion, Pierre McLennan, Geoffrey Cleary, Kevin Suh, Robert Burt, Randall W. Pfeiffer, Ruth M. Hoffman, John M. Roy, Hemant K. Wang, Thomas Limburg, Paul J. El-Deiry, Wafik S. Papadimitrakopoulou, Vali Hittelman, Walter N. MacAulay, Calum Veltri, Robert W. Solomonzz, Diane Jeronimo, Jose Richards-Kortum, Rebecca Johnson, Karen A. Viner, Jaye L. Stratton, Steven P. Rajadhyaksha, Milind Dhawan, Atam NIH/NCI/DCTD Cancer Imaging Program Bethesda MD United States NIH/NCI/DCTD Cancer Imaging Program NCI-Frederick Frederick MD United States NIH/NCI/DCP Early Detection Research Group Bethesda MD United States The Johns Hopkins University School of Medicine Departments of Radiology Pharmacology and Oncology Division of Neuroradiology Baltimore MD United States NIH/NCI/CCR Cell and Cancer Biology Branch Bethesda MD United States University of California San Francisco Department of Medicine Division of Medical Oncology San Francisco CA United States CCS Associates Inc. Mountain View CA United States Wistar Institute Molecular and Cellular Oncogenesis Program Philadelphia PA United States University of Kansas School of Medicine VA Hospital Kansas City MO United States Duke University Department of Biomedical Engineering Medical Physics Program Durham NC United States University of Pennsylvania School of Medicine Department of Gastroenterology Philadelphia PA United States New York Presbyterian Hospital Weill Medical College of Cornell University New York NY United States NIH/NCI/OD Office of Centers for Training and Resources Bethesda MD United States University of Rochester Medical Center Department of Urology Rochester NY United States University of Texas MD Anderson Cancer Center Department of Urology Houston TX United States Massachusetts General Hospital Abdominal Imaging/Interventional Radiology Boston MA United States Boston University Biomedical Engineering Electrical and Computer Engineering and Physics Boston MA United States NIH/NCI/DCP Gastrointestinal and Other Cancers Research Group Bethesda MD United States The George Washington University Cancer Institute Office of Cancer Prevention and Control Washington DC United States University of Kansas Cancer Center Clinical Oncology Kansas City KS United States University of California Davis Department of Biomedical Engineering Davis CA United States Tufts University De

The article provides information on a workshop on the development of imaging systems for cancer prevention and preemption convened by the U.S. National Cancer Institute (NCI) Cancer Imaging program held in Gaithersburg, Maryland. Doctor Katherine Ferrara of the University of California discussed the function of ultrasound in breast tumor imaging. Ways to close the gap between development and application of imaging approaches were tackled in a roundtable discussion on cervix and skin cancer.

关键词： FORUMS (Discussion & debate) IMAGING systems in medicine CANCER prevention

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Real-time organ motion tracking and fast image registration system for MRI-guided surgery

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systems and computers in Japan 2006年第1期37卷 83-92页

作者： Tokuda, Junichi Hirano, Masaya Tsukamoto, Tetsuji Dohi, Takeyoshi Hata, Nobuhiko Graduate School of Information Science and Technology University of Tokyo Tokyo 113-8656 Japan GE Yokogawa Medical Systems Ltd. Hino 191-8503 Japan International Society for Computer-Aided Surgery Japan Society for Computer-Aided Surgery Japan Society for Magnetic Resonance in Medicine GE Yokogawa Medical Systems Ltd. Imaging Application Tech. Center MR Group R and D Laboratory Technology Center GE Yokogawa Medical Systems Ltd. Society for Magnetic Resonance in Medicine Graduate School of Information Science and Technology Society for Life Support Technology Japan Society for Organ Preservation Biology and Medicine Society for Precision Engineering Society for Medical Image Computing and Computer Assisted Intervention International Union for Physics and Engineering Sciences in Medicine JSMEBE Japan Society for Life Assistance Technology Brigham and Women's Hospital Harvard Medical School MR Therapy Program

The advantage of magnetic resonance imaging (MRI)-guided surgery, in which MR images taken during surgery are used to guide the surgery, has been recognized recently. However, there is a problem, due to long imaging time of MRI, that it is difficult to capture the respiratory organ motion. It is often a constraint that the patient must hold his or her breath, especially in the treatment of the abdominal region. Consequently, this paper proposes a method of eliminating this constraint in MRI-guided surgery by real-time monitoring of the movement of the organ involved in the surgery by means of the signal derived from MRI. Specifically, the navigator echo is extracted in imaging during surgery. The movement of the organ involved in surgery is detected on the basis of the change in the calculated projection profile, and the results are presented to the surgeon. In this study, the procedure is built into a 1.5-T clinical MRI system. The feasibility of the proposed system was examined under clinical conditions by evaluating its accuracy and delay in a phantom experiment, and also in an in vivo experiment on the liver of a volunteer. The results are reported in this paper. © 2005 Wiley Periodicals, Inc.

关键词： Motion estimation

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A Learning Identification Algorithm And Its Application To An Environmental System

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IEEE Transactions on systems, Man and Cybernetics 1975年第2期SMC-5卷 226-240页

作者： Duffy, John J. Franklin, Mark A. Control Systems Science and Engineering Program Center for the Biology of Natural Systems Washington University St. Louis Mo. 63130 United States Departments of Electrical Engineering Computer Science Center for the Biology of Natural Systems Washington University St. Louis Mo. 63130 United States

An empirical heuristic learning identification algorithm of Ivakhnenko was modified and used to model an environmental system producing high nitrate levels in agricultural drain water in the Corn Belt. The method amounts to fitting a polynomial to a multi-input single-output response surface. The modifications result in a reduced number of terms in final model equations, a decrease in computational difficulties, and other improvements in the algorithm. This method appears to be advantageous with systems characterized by complexity with many variables and parameters, ill-defined mathematical structures, and limited data. In other words, this algorithm is useful for empirically generating hypotheses about systems of which relatively little is known. © 1975 IEEE. All rights reserved.

关键词： Polynomials Surface fitting Predictive models Nonlinear systems Response surface methodology Equations Algorithm design and analysis Heuristic algorithms Linear systems Belts

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Computational Methods for Single-Cell RNA Sequencing

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Biomedical Data science 1000年第1期3卷 339-364页

作者： Brian Hie Joshua Peters Sarah K. Nyquist Alex K. Shalek Bonnie Berger Bryan D. Bryson 1Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA email: bab@mit.edu 3Ragon Institute of MGH MIT and Harvard Cambridge Massachusetts 02139 USA 2Department of Biological Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA email: bryand@mit.edu 4Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA 5Department of Chemistry Institute for Medical Engineering & Science (IMES) and Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA 6Department of Mathematics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.

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