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Architecture of the human regulatory network derived from ENCODE data

NATURE 2012年第7414期489卷 91-100页

作者： Gerstein, Mark B. Kundaje, Anshul Hariharan, Manoj Landt, Stephen G. Yan, Koon-Kiu Cheng, Chao Mu, Xinmeng Jasmine Khurana, Ekta Rozowsky, Joel Alexander, Roger Min, Renqiang Alves, Pedro Abyzov, Alexej Addleman, Nick Bhardwaj, Nitin Boyle, Alan P. Cayting, Philip Charos, Alexandra Chen, David Z. Cheng, Yong Clarke, Declan Eastman, Catharine Euskirchen, Ghia Frietze, Seth Fu, Yao Gertz, Jason Grubert, Fabian Harmanci, Arif Jain, Preti Kasowski, Maya Lacroute, Phil Leng, Jing Lian, Jin Monahan, Hannah O'Geen, Henriette Ouyang, Zhengqing Partridge, E. Christopher Patacsil, Dorrelyn Pauli, Florencia Raha, Debasish Ramirez, Lucia Reddy, Timothy E. Reed, Brian Shi, Minyi Slifer, Teri Wang, Jing Wu, Linfeng Yang, Xinqiong Yip, Kevin Y. Zilberman-Schapira, Gili Batzoglou, Serafim Sidow, Arend Farnham, Peggy J. Myers, Richard M. Weissman, Sherman M. Snyder, Michael Yale Univ Program Computat Biol & Bioinformat New Haven CT 06520 USA Yale Univ Dept Mol Biophys & Biochem New Haven CT 06520 USA Yale Univ Dept Commun Sci New Haven CT 06511 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA Stanford Univ Dept Genet Stanford CA 94305 USA NEC Labs Amer Dept Machine Learning Princeton NJ 08540 USA Yale Univ Dept Mol Cellular & Dev Biol New Haven CT 06520 USA Yale Univ Dept Chem New Haven CT 06520 USA Univ So Calif Dept Biochem & Mol Biol Norris Comprehens Canc Ctr Los Angeles CA 90089 USA HudsonAlpha Inst Biotechnol Huntsville AL 35806 USA Yale Univ Sch Med Dept Genet New Haven CT 06510 USA Univ Calif Davis Genome Ctr Davis CA 95616 USA Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China Stanford Univ Dept Pathol Stanford CA 94305 USA

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes;the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties;for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

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Feature selection via dependence maximization

The Journal of Machine Learning Research

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The Journal of machine learning Research 2012年第1期13卷

作者： Le Song Alex Smola Arthur Gretton Justin Bedo Karsten Borgwardt Computational Science and Engineering Georgia Institute of Technology Atlanta GA Yahoo! Research Santa Clara CA Gatsby Computational Neuroscience Unit London UK and Intelligent Systems Group Max Planck Institutes Tübingen Germany Statistical Machine Learning Program National ICT Australia Canberra ACT Australia and Australian National University Canberra ACT Australia Machine Learning and Computational Biology Research Group Max Planck Institutes Tübingen Germany

We introduce a framework for feature selection based on dependence maximization between the selected features and the labels of an estimation problem, using the Hilbert-Schmidt Independence Criterion. The key idea is that good features should be highly dependent on the labels. Our approach leads to a greedy procedure for feature selection. We show that a number of existing feature selectors are special cases of this framework. Experiments on both artificial and real-world data show that our feature selector works well in practice.

关键词： Hilbert space embedding of distribution Hilbert-Schmidt independence criterion feature selection independence measure kernel methods

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TVNViewer: An interactive visualization tool for exploring networks that change over time or space

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BIOINFORMATICS 2011年第13期27卷 1880-1881页

作者： Curtis, Ross E. Yuen, Amos Song, Le Goyal, Anuj Xing, Eric P. Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Joint CMU Pitt PhD Program Computat Biol Pittsburgh PA 15213 USA Carnegie Mellon Univ Lane Ctr Computat Biol Pittsburgh PA 15213 USA Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Machine Learning Pittsburgh PA 15213 USA

The relationship between genes and proteins is a dynamic relationship that changes across time and differs in different cells. The study of these differences can reveal various insights into biological processes and disease progression, especially with the aid of proper tools for network visualization. Toward this purpose, we have developed TVNViewer, a novel visualization tool, which is specifically designed to aid in the exploration and analysis of dynamic networks.

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An Optimal Transportation Approach for Nuclear Structure-Based Pathology

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2011年第3期30卷 621-631页

作者： Wang, Wei Ozolek, John A. Slepcev, Dejan Lee, Ann B. Chen, Cheng Rohde, Gustavo K. Carnegie Mellon Univ Dept Elect & Comp Engn Ctr Bioimage Informat Dept Biomed Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Computat Biol Program Pittsburgh PA 15213 USA Childrens Hosp Pittsburgh Dept Pathol Pittsburgh PA 15201 USA Carnegie Mellon Univ Dept Math Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Stat Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Machine Learning Pittsburgh PA 15213 USA

Nuclear morphology and structure as visualized from histopathology microscopy images can yield important diagnostic clues in some benign and malignant tissue lesions. Precise quantitative information about nuclear structure and morphology, however, is currently not available for many diagnostic challenges. This is due, in part, to the lack of methods to quantify these differences from image data. We describe a method to characterize and contrast the distribution of nuclear structure in different tissue classes (normal, benign, cancer, etc.). The approach is based on quantifying chromatin morphology in different groups of cells using the optimal transportation (Kantorovich-Wasserstein) metric in combination with the Fisher discriminant analysis and multidimensional scaling techniques. We show that the optimal transportation metric is able to measure relevant biological information as it enables automatic determination of the class (e.g., normal versus cancer) of a set of nuclei. We show that the classification accuracies obtained using this metric are, on average, as good or better than those obtained utilizing a set of previously described numerical features. We apply our methods to two diagnostic challenges for surgical pathology: one in the liver and one in the thyroid. Results automatically computed using this technique show potentially biologically relevant differences in nuclear structure in liver and thyroid cancers.

关键词： Cancer detection classification nuclear structure optimal transportation pathology

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud Computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Correlation transmission of spiking neurons is boosted by synchronous input

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BMC Neuroscience 2011年第1期12卷 1-2页

作者： Matthias Schultze-Kraft Markus Diesmann Moritz Helias Sonja Grün Machine Learning Group Berlin Institute of Technology Berlin Germany Laboratory for Computational Neurophysics RIKEN Brain Science Institute Wako City Japan Brain and Neural Systems Team RIKEN Computational Science Research Program Wako City Japan Institute of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience Research Center Jülich Germany Laboratory for Statistical Neuroscience RIKEN Brain Science Institute Wako City Japan

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GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment

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SCIENCE 2013年第6139期340卷 1467-1471页

作者： Rietveld, Cornelius A. Medland, Sarah E. Derringer, Jaime Yang, Jian Esko, Tonu Martin, Nicolas W. Westra, Harm-Jan Shakhbazov, Konstantin Abdellaoui, Abdel Agrawal, Arpana Albrecht, Eva Alizadeh, Behrooz Z. Amin, Najaf Bamard, John Baumeister, Sebastian E. Benke, Kelly S. Bielak, Lawrence F. Boatman, Jeffrey A. Boyle, Patricia A. Davies, Gail De Leeuw, Christiaan Eklund, Niina Evans, Daniel S. Ferhmann, Rudolf Fischer, Krista Gieger, Christian Gjessing, Hakon K. Haegg, Sara Harris, Jennifer R. Hayward, Caroline Holzapfel, Christina Ibrahim-Verbaas, Carla A. Ingelsson, Erik Jacobsson, Bo Joshi, Peter K. Jugessur, Astanand Kaakinen, Marika Kanoni, Stavroula Karjalainen, Juha Kolcic, Ivana Kristiansson, Kati Kutalik, Zoltan Lahti, Jari Lee, Sang H. Lin, Peng Lind, Penelope A. Liu, Yongmei Lohman, Kurt Loitfelder, Marisa McMahon, George Vidal, Pedro Marques Meirelles, Osorio Milani, Lili Myhre, Ronny Nuotio, Marja-Liisa Oldmeadow, Christopher J. Petrovic, Katja E. Peyrot, Wouter J. Polasek, Ozren Quaye, Lydia Reinmaa, Eva Rice, John P. Rizzi, Thais S. Schmidt, Helena Schmidt, Reinhold Smith, Albert V. Smith, Jennifer A. Tanaka, Toshiko Terracciano, Antonio van der Loos, Matthijs J. H. M. Vitart, Veronique Voelzke, Henry Wellmann, Juergen Yu, Lei Zhao, Wei Allik, Jueri Attia, John R. Bandinelli, Stefania Bastardot, Franois Beauchamp, Jonathan Bennett, David A. Berger, Klaus Bierut, Laura J. Boomsma, Dorret I. Bueltmann, Ute Campbell, Harry Chabris, Christopher F. Cherkas, Lynn Chung, Mina K. Cucca, Francesco de Andrade, Mariza De Jager, Philip L. De Neve, Jan-Emmanuel Deary, Ian J. Dedoussis, George V. Deloukas, Panos Dimitriou, Maria Eiriksdottir, Guony Elderson, Martin F. Eriksson, Johan G. Evans, David M. Faul, Jessica D. Ferrucci, Luigi Garcia, Melissa E. Groenberg, Henrik Guonason, Vilmundur Hall, Per Harris, Juliette M. Harris, Tamara B. Hastie, Nicholas D. Heath, Andrew C. Hernandez, Dena G. Hoffmann, Wolfgang Hofman, Adriaan Holle, Rolf Holliday, Elizabeth G. Hottenga, Jouke-Jan Iacono, William G. Illig, Thom Erasmus Univ Erasmus Sch Econ Dept Appl Econ NL-3000 DR Rotterdam Netherlands Erasmus MC Dept Epidemiol NL-3000 CA Rotterdam Netherlands Queensland Inst Med Res Brisbane Qld 4006 Australia Univ Colorado Inst Behav Genet Boulder CO 80309 USA Univ Queensland Princess Alexandra Hosp Diamantina Inst Brisbane Qld 4102 Australia Univ Tartu Estonian Genome Ctr EE-51010 Tartu Estonia Univ Queensland Sch Psychol Brisbane Qld 4072 Australia Univ Groningen Univ Med Ctr Groningen Dept Genet NL-9713 GZ Groningen Netherlands Univ Queensland Queensland Brain Inst Brisbane Qld 4072 Australia Vrije Univ Amsterdam Dept Biol Psychol NL-1081 BT Amsterdam Netherlands Washington Univ Dept Psychiat Sch Med St Louis MO 63110 USA German Res Ctr Environm Hlth Helmholtz Zentrum Munchen Inst Genet Epidemiol D-85764 Neuherberg Germany Univ Groningen Univ Med Ctr Groningen Dept Epidemiol NL-9700 RB Groningen Netherlands Erasmus MC Dept Epidemiol Genet Epidemiol Unit NL-3000 CA Rotterdam Netherlands Cleveland Clin Heart & Vasc & Lerner Res Inst Cleveland OH 44195 USA Univ Med Greifswald Inst Community Med D-17489 Greifswald Germany Univ Toronto Samuel Lunenfeld Res Inst Mt Sinai Hosp Toronto ON M5G 1X5 Canada Univ Michigan Sch Publ Hlth Dept Epidemiol Ann Arbor MI 48109 USA Univ Minnesota Div Biostat Minneapolis MN 55455 USA Rush Univ Med Ctr Rush Alzheimers Dis Ctr Chicago IL 60612 USA Univ Edinburgh Ctr Cognit Aging & Cognit Epidemiol Edinburgh EH8 9JZ Midlothian Scotland Vrije Univ Amsterdam Dept Funct Genom NL-1081 HV Amsterdam Netherlands Vrije Univ Amsterdam Med Ctr NL-1081 HV Amsterdam Netherlands Radboud Univ Nijmegen Machine Learning Grp Inst Comp & Informat Sci Fac Sci NL-6500 GL Nijmegen Netherlands Univ Helsinki Inst Mol Med Finland FIN-00014 Helsinki Finland Natl Inst Hlth & Welf Dept Chron Dis Prevent Publ Hlth Genom Unit Helsinki 00014 Finland Calif Pacific Med Ctr Res Inst San Francisco CA 94107 USA Norwegian Inst Publ Hlth Div Ep

A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R-2 approximate to 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for approximate to 2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics.

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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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Structured Literature Image Finder: Extracting Information from Text and Images in Biomedical Literature

Structured Literature Image Finder: Extracting Information f...

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

作者： Coelho, Luis Pedro Ahmed, Amr Arnold, Andrew Kangas, Joshua Sheikh, Abdul-Saboor Xing, Eric P. Cohen, William W. Murphy, Robert F. Carnegie Mellon Univ Lane Ctr Computat Biol Pittsburgh PA 15213 USA Univ Pittsburgh Ph D Program Computat Biol Carnegie Mellon Univ Pittsburgh PA 15213 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Machine Learning Dept Pittsburgh PA 15213 USA Language Technol Inst Pittsburgh PA 15213 USA Dept Biol Sci Pittsburgh PA 15213 USA Dept Biomed Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9783642131301

SLIF uses a combination of text-mining and image processing to extract information from figures in the biomedical literature. It also uses innovative extensions to traditional latent topic modeling to provide new ways to traverse the literature. SLIF provides a publicly available searchable database (http://***). SLIF originally focused on fluorescence microscopy images. We have now extended it to classify panels into more image types. We also improved the classification into subcellular classes by building a more representative training set. To get the most out of the human labeling effort, we used active learning to select images to label. We developed models that take into account the structure of the document (with panels inside figures inside papers) and the multi-modality of the information (free and annotated text, images, information from external databases). This has allowed us to provide new ways to navigate a large collection of documents.

关键词： Classification (of information)

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Decoding and Cortical Source Localization for Intended Movement Direction With MEG

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JOURNAL OF NEUROPHYSIOLOGY 2010年第5期104卷 2451-2461页

作者： Wang, Wei Sudre, Gustavo P. Xu, Yang Kass, Robert E. Collinger, Jennifer L. Degenhart, Alan D. Bagic, Anto I. Weber, Douglas J. Univ Pittsburgh Dept Phys Med & Rehabil Pittsburgh PA USA Univ Pittsburgh Dept Bioengn Pittsburgh PA USA Univ Pittsburgh Dept Neurol Pittsburgh PA 15260 USA Ctr Neural Basis Cognit Pittsburgh PA USA Qual Life Technol Engn Res Ctr Pittsburgh PA USA Carnegie Mellon Univ Program Neural Computat Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Stat Pittsburgh PA 15213 USA Dept Vet Affairs Pittsburgh PA USA Ctr Adv Brain Magnet Source Imaging Pittsburgh PA USA

Wang W, Sudre GP, Xu Y, Kass RE, Collinger JL, Degenhart AD, Bagic AI, Weber DJ. Decoding and cortical source localization for intended movement direction with MEG. J Neurophysiol 104: 2451-2461, 2010. First published August 25, 2010;doi:10.1152/jn.00239.2010. Magnetoencephalography (MEG) enables a noninvasive interface with the brain that is potentially capable of providing movement-related information similar to that obtained using more invasive neural recording techniques. Previous studies have shown that movement direction can be decoded from multichannel MEG signals recorded in humans performing wrist movements. We studied whether this information can be extracted without overt movement of the subject, because the targeted users of brain-controlled interface (BCI) technology are those with severe motor disabilities. The objectives of this study were twofold: 1) to decode intended movement direction from MEG signals recorded during the planning period before movement onset and during imagined movement and 2) to localize cortical sources modulated by intended movement direction. Ten able-bodied subjects performed both overt and imagined wrist movement while their cortical activities were recorded using a whole head MEG system. The intended movement direction was decoded using linear discriminant analysis and a Bayesian classifier. Minimum current estimation (MCE) in combination with a bootstrapping procedure enabled source-space statistical analysis, which showed that the contralateral motor cortical area was significantly modulated by intended movement direction, and this modulation was the strongest similar to 100 ms before the onset of overt movement. These results suggest that it is possible to study cortical representation of specific movement information using MEG, and such studies may aid in presurgical localization of optimal sites for implanting electrodes for BCI systems.

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