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Proquest Dissertations
Automated learning of protein involvement in pathogenesis using integrated queries Eithon Cadag A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2009 Program Authorized to Offer Degree: Department of Medical Education and Biomedical Informatics UMI Number: 3394276 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. UMI Dissertation Publishing UMI 3394276 Copyright 2010 by ProQuest LLC. All rights reserved. This edition of the work is protected against unauthorized copying under Title 17, United States Code. uest ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 University of Washington Graduate School This is to certify that I have examined this copy of a doctoral dissertation by Eithon Cadag and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the final examining committee have been made. Chair of the Supervisory Committee: Reading Committee: (SjLt KJ. £U*t~ Peter Tgffczy-Hornoch In presenting this dissertation in partial fulfillment of the requirements for the doctoral degree at the University of Washington, I agree that the Library shall make its copies freely available for inspection. I further agree that extensive copying of this dissertation is allowable only for scholarly purposes, consistent with "fair use" as prescribed in the U.S. -
Ismb/Eccb 2015
Research Collection Journal Article ISMB/ECCB 2015 Author(s): Moreau, Yves; Beerenwinkel, Niko Publication Date: 2015 Permanent Link: https://doi.org/10.3929/ethz-b-000102416 Originally published in: Bioinformatics 31(12), http://doi.org/10.1093/bioinformatics/btv303 Rights / License: Creative Commons Attribution-NonCommercial 4.0 International This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Bioinformatics, 31, 2015, i1–i2 doi: 10.1093/bioinformatics/btv303 ISMB/ECCB 2015 Editorial ISMB/ECCB 2015 This special issue of Bioinformatics serves as the proceedings of the 175 external reviewers recruited as sub-reviewers by program com- joint 23rd annual meeting of Intelligent Systems for Molecular mittee members. Table 1 provides a summary of the areas, area Biology (ISMB) and 14th European Conference on Computational chairs and a review summary by area. The conference used a two- Biology (ECCB), which took place in Dublin, Ireland, July 10–14, tier review system—a continuation and refinement of a process that 2015 (http://www.iscb.org/ismbeccb2015). ISMB/ECCB 2015, the begun with ISMB/ECCB 2013 in an effort to better ensure thorough official conference of the International Society for Computational and fair reviewing. Under the revised process, each of the 241 sub- Biology (ISCB, http://www.iscb.org/), was accompanied by nine missions was first reviewed by at least three expert referees, with a Special Interest Group meetings of 1 or 2 days each, and two satel- subset receiving between four and six reviews, as needed. lite meetings. -
Algorithms for Computational Biology 8Th International Conference, Alcob 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings
Lecture Notes in Bioinformatics 12715 Subseries of Lecture Notes in Computer Science Series Editors Sorin Istrail Brown University, Providence, RI, USA Pavel Pevzner University of California, San Diego, CA, USA Michael Waterman University of Southern California, Los Angeles, CA, USA Editorial Board Members Søren Brunak Technical University of Denmark, Kongens Lyngby, Denmark Mikhail S. Gelfand IITP, Research and Training Center on Bioinformatics, Moscow, Russia Thomas Lengauer Max Planck Institute for Informatics, Saarbrücken, Germany Satoru Miyano University of Tokyo, Tokyo, Japan Eugene Myers Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Marie-France Sagot Université Lyon 1, Villeurbanne, France David Sankoff University of Ottawa, Ottawa, Canada Ron Shamir Tel Aviv University, Ramat Aviv, Tel Aviv, Israel Terry Speed Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia Martin Vingron Max Planck Institute for Molecular Genetics, Berlin, Germany W. Eric Wong University of Texas at Dallas, Richardson, TX, USA More information about this subseries at http://www.springer.com/series/5381 Carlos Martín-Vide • Miguel A. Vega-Rodríguez • Travis Wheeler (Eds.) Algorithms for Computational Biology 8th International Conference, AlCoB 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings 123 Editors Carlos Martín-Vide Miguel A. Vega-Rodríguez Rovira i Virgili University University of Extremadura Tarragona, Spain Cáceres, Spain Travis Wheeler University of Montana Missoula, MT, USA ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Bioinformatics ISBN 978-3-030-74431-1 ISBN 978-3-030-74432-8 (eBook) https://doi.org/10.1007/978-3-030-74432-8 LNCS Sublibrary: SL8 – Bioinformatics © Springer Nature Switzerland AG 2021 This work is subject to copyright. -
Computational Pan-Genomics: Status, Promises and Challenges
bioRxiv preprint doi: https://doi.org/10.1101/043430; this version posted March 12, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Computational Pan-Genomics: Status, Promises and Challenges Tobias Marschall1,2, Manja Marz3,60,61,62, Thomas Abeel49, Louis Dijkstra6,7, Bas E. Dutilh8,9,10, Ali Ghaffaari1,2, Paul Kersey11, Wigard P. Kloosterman12, Veli M¨akinen13, Adam Novak15, Benedict Paten15, David Porubsky16, Eric Rivals17,63, Can Alkan18, Jasmijn Baaijens5, Paul I. W. De Bakker12, Valentina Boeva19,64,65,66, Francesca Chiaromonte20, Rayan Chikhi21, Francesca D. Ciccarelli22, Robin Cijvat23, Erwin Datema24,25,26, Cornelia M. Van Duijn27, Evan E. Eichler28, Corinna Ernst29, Eleazar Eskin30,31, Erik Garrison32, Mohammed El-Kebir5,33,34, Gunnar W. Klau5, Jan O. Korbel11,35, Eric-Wubbo Lameijer36, Benjamin Langmead37, Marcel Martin59, Paul Medvedev38,39,40, John C. Mu41, Pieter Neerincx36, Klaasjan Ouwens42,67, Pierre Peterlongo43, Nadia Pisanti44,45, Sven Rahmann29, Ben Raphael46,47, Knut Reinert48, Dick de Ridder50, Jeroen de Ridder49, Matthias Schlesner51, Ole Schulz-Trieglaff52, Ashley Sanders53, Siavash Sheikhizadeh50, Carl Shneider54, Sandra Smit50, Daniel Valenzuela13, Jiayin Wang70,71,72, Lodewyk Wessels56, Ying Zhang23,5, Victor Guryev16,12, Fabio Vandin57,34, Kai Ye68,69,72 and Alexander Sch¨onhuth5 1Center for Bioinformatics, Saarland University, Saarbr¨ucken, Germany; 2Max Planck Institute for Informatics, Saarbr¨ucken, -
Cancer Informatics: New Tools for a Data-Driven Age in Cancer Research Warren Kibbe1, Juli Klemm1, and John Quackenbush2
Cancer Focus on Computer Resources Research Cancer Informatics: New Tools for a Data-Driven Age in Cancer Research Warren Kibbe1, Juli Klemm1, and John Quackenbush2 Cancer is a remarkably adaptable and formidable foe. Cancer Precision Medicine Initiative highlighted the importance of data- exploits many biological mechanisms to confuse and subvert driven cancer research, translational research, and its application normal physiologic and cellular processes, to adapt to thera- to decision making in cancer treatment (https://www.cancer.gov/ pies, and to evade the immune system. Decades of research and research/key-initiatives/precision-medicine). And the National significant national and international investments in cancer Strategic Computing Initiative highlighted the importance of research have dramatically increased our knowledge of the computing as a national competitive asset and included a focus disease, leading to improvements in cancer diagnosis, treat- on applying computing in biomedical research. Articles in the ment, and management, resulting in improved outcomes for mainstream media, such as that by Siddhartha Mukherjee in many patients. the New Yorker in April of 2017 (http://www.newyorker.com/ In melanoma, the V600E mutation in the BRAF gene is now magazine/2017/04/03/ai-versus-md), have emphasized the targetable by a specific therapy. BRAF is a serine/threonine protein growing importance of computing, machine learning, and data kinase activating the MAP kinase (MAPK)/ERK signaling pathway, in biomedicine. and both BRAF and MEK inhibitors, such as vemurafenib and The NCI (Rockville, MD) recognized the need to invest in dabrafenib, have shown dramatic responses in patients carrying informatics. In 2011, it established a funding opportunity, the mutation. -
Ontology-Based Methods for Analyzing Life Science Data
Habilitation a` Diriger des Recherches pr´esent´ee par Olivier Dameron Ontology-based methods for analyzing life science data Soutenue publiquement le 11 janvier 2016 devant le jury compos´ede Anita Burgun Professeur, Universit´eRen´eDescartes Paris Examinatrice Marie-Dominique Devignes Charg´eede recherches CNRS, LORIA Nancy Examinatrice Michel Dumontier Associate professor, Stanford University USA Rapporteur Christine Froidevaux Professeur, Universit´eParis Sud Rapporteure Fabien Gandon Directeur de recherches, Inria Sophia-Antipolis Rapporteur Anne Siegel Directrice de recherches CNRS, IRISA Rennes Examinatrice Alexandre Termier Professeur, Universit´ede Rennes 1 Examinateur 2 Contents 1 Introduction 9 1.1 Context ......................................... 10 1.2 Challenges . 11 1.3 Summary of the contributions . 14 1.4 Organization of the manuscript . 18 2 Reasoning based on hierarchies 21 2.1 Principle......................................... 21 2.1.1 RDF for describing data . 21 2.1.2 RDFS for describing types . 24 2.1.3 RDFS entailments . 26 2.1.4 Typical uses of RDFS entailments in life science . 26 2.1.5 Synthesis . 30 2.2 Case study: integrating diseases and pathways . 31 2.2.1 Context . 31 2.2.2 Objective . 32 2.2.3 Linking pathways and diseases using GO, KO and SNOMED-CT . 32 2.2.4 Querying associated diseases and pathways . 33 2.3 Methodology: Web services composition . 39 2.3.1 Context . 39 2.3.2 Objective . 40 2.3.3 Semantic compatibility of services parameters . 40 2.3.4 Algorithm for pairing services parameters . 40 2.4 Application: ontology-based query expansion with GO2PUB . 43 2.4.1 Context . 43 2.4.2 Objective . -
AI and Bioinformatics
AI Magazine Volume 25 Number 1 (2004) (© AAAI) Articles Editorial Introduction AI and Bioinformatics Janice Glasgow, Igor Jurisica, and Burkhard Rost ■ This article is an editorial introduction to the re- modern-day biology is far more complex than search discipline of bioinformatics and to the articles suggested by the simplified sketch presented in this special issue. In particular, we address the issue here. In fact, researchers in life sciences live off of how techniques from AI can be applied to many of the introduction of new concepts; the discov- the open and complex problems of modern-day mol- ecular biology. ery of exceptions; and the addition of details that usually complicate, rather than simplify, his special issue of AI Magazine focuses the overall understanding of the field. on some areas of research in bioinfor- Possibly the most rapidly growing area of re- Tmatics that have benefited from applying cent activity in bioinformatics is the analysis AI techniques. Undoubtedly, bioinformatics is of microarray data. The article by Michael Mol- a truly interdisciplinary field: Although some la, Michael Waddell, David Page, and Jude researchers continuously affect wet labs in life Shavlik (“Using Machine Learning to Design science through collaborations or provision of and Interpret Gene-Expression Microarrays”) tools, others are rooted in the theory depart- introduces some background information and ments of exact sciences (physics, chemistry, or provides a comprehensive description of how engineering) or computer sciences. This wide techniques from machine learning can be used variety creates many different perspectives and to help understand this high-dimensional and terminologies. One result of this Babel of lan- prolific gene-expression data. -
Microblogging the ISMB: a New Approach to Conference Reporting
Message from ISCB Microblogging the ISMB: A New Approach to Conference Reporting Neil Saunders1*, Pedro Beltra˜o2, Lars Jensen3, Daniel Jurczak4, Roland Krause5, Michael Kuhn6, Shirley Wu7 1 School of Molecular and Microbial Sciences, University of Queensland, St. Lucia, Brisbane, Queensland, Australia, 2 Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California, United States of America, 3 Novo Nordisk Foundation Center for Protein Research, Panum Institute, Copenhagen, Denmark, 4 Department of Bioinformatics, University of Applied Sciences, Hagenberg, Freistadt, Austria, 5 Max-Planck-Institute for Molecular Genetics, Berlin, Germany, 6 European Molecular Biology Laboratory, Heidelberg, Germany, 7 Stanford Medical Informatics, Stanford University, Stanford, California, United States of America Cameron Neylon entitled FriendFeed for Claire Fraser-Liggett opened the meeting Scientists: What, Why, and How? (http:// with a review of metagenomics and an blog.openwetware.org/scienceintheopen/ introduction to the human microbiome 2008/06/12/friendfeed-for-scientists-what- project (http://friendfeed.com/search?q = why-and-how/) for an introduction. room%3Aismb-2008+microbiome+OR+ We—a group of science bloggers, most fraser). The subsequent Q&A session of whom met in person for the first time at covered many of the exciting challenges The International Conference on Intel- ISMB 2008—found FriendFeed a remark- for those working in this field. Clearly, ligent Systems for Molecular Biology -
Finding New Order in Biological Functions from the Network Structure of Gene Annotations
Finding New Order in Biological Functions from the Network Structure of Gene Annotations Kimberly Glass 1;2;3∗, and Michelle Girvan 3;4;5 1Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA 2Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA 3Physics Department, University of Maryland, College Park, MD, USA 4Institute for Physical Science and Technology, University of Maryland, College Park, MD, USA 5Santa Fe Institute, Santa Fe, NM, USA The Gene Ontology (GO) provides biologists with a controlled terminology that describes how genes are associated with functions and how functional terms are related to each other. These term-term relationships encode how scientists conceive the organization of biological functions, and they take the form of a directed acyclic graph (DAG). Here, we propose that the network structure of gene-term annotations made using GO can be employed to establish an alternate natural way to group the functional terms which is different from the hierarchical structure established in the GO DAG. Instead of relying on an externally defined organization for biological functions, our method connects biological functions together if they are performed by the same genes, as indicated in a compendium of gene annotation data from numerous different experiments. We show that grouping terms by this alternate scheme is distinct from term relationships defined in the ontological structure and provides a new framework with which to describe and predict the functions of experimentally identified sets of genes. Tools and supplemental material related to this work can be found online at http://www.networks.umd.edu. -
Biocreative II.5 Workshop 2009 Special Session on Digital Annotations
BioCreative II.5 Workshop 2009 Special Session on Digital Annotations The purified IRF-4 was also The main role of BRCA2 shown to be capable of binding appears to involve regulating the DNA in a PU.1-dependent manner function of RAD51 in the repair by by electrophoretic mobility shift homologous recombination . analysis. brca2 irf4 We found that cells ex- Moreover, expression of pressing Olig2, Nkx2.2, and NG2 Carma1 induces phosphorylation were enriched among virus- of Bcl10 and activation of the infected, GFP-positive (GFP+) transcription factor NF-kappaB. cells. carma1 BB I O olig2 The region of VHL medi- The Rab5 effector ating interaction with HIF-1 alpha Rabaptin-5 and its isoform C R E A T I V E overlapped with a putative Rabaptin-5delta differ in their macromolecular binding site within ability to interact with the rsmallab5 the crystal structure. GTPase Rab4. vhl Translocation RCC, bearing We show that ERBB2-dependenterbb2 atf1 TFE3 or TFEB gene fusions, are Both ATF-1 homodimers and tfe3 medulloblastoma cell invasion and ATF-1/CREB heterodimers bind to recently recognized entities for prometastatic gene expression can the CRE but not to the related which risk factors have not been be blocked using the ERBB tyrosine phorbol ester response element. identified. kinase inhibitor OSI-774. C r i t i c a l A s s e s s m e n t o f I n f o r m a t i o n E x t r a c t i o n i n B i o l o g y October 7th - 9th, 2009 www.BioCreative.org BioCreative II.5 Workshop 2009 special session | Digital Annotations Auditorium of the Spanish National -
Computational Biology and Bioinformatics
Vol. 30 ISMB 2014, pages i1–i2 BIOINFORMATICS EDITORIAL doi:10.1093/bioinformatics/btu304 Editorial This special issue of Bioinformatics serves as the proceedings of The conference used a two-tier review system, a continuation the 22nd annual meeting of Intelligent Systems for Molecular and refinement of a process begun with ISMB 2013 in an effort Biology (ISMB), which took place in Boston, MA, July 11–15, to better ensure thorough and fair reviewing. Under the revised 2014 (http://www.iscb.org/ismbeccb2014). The official confer- process, each of the 191 submissions was first reviewed by at least ence of the International Society for Computational Biology three expert referees, with a subset receiving between four and (http://www.iscb.org/), ISMB, was accompanied by 12 Special eight reviews, as needed. These formal reviews were frequently Interest Group meetings of one or two days each, two satellite supplemented by online discussion among reviewers and Area meetings, a High School Teachers Workshop and two half-day Chairs to resolve points of dispute and reach a consensus on tutorials. Since its inception, ISMB has grown to be the largest each paper. Among the 191 submissions, 29 were conditionally international conference in computational biology and bioinfor- accepted for publication directly from the first round review Downloaded from matics. It is expected to be the premiere forum in the field for based on an assessment of the reviewers that the paper was presenting new research results, disseminating methods and tech- clearly above par for the conference. A subset of 16 papers niques and facilitating discussions among leading researchers, were viewed as potentially in the top tier but raised significant practitioners and students in the field. -
Are You an Invited Speaker? a Bibliometric Analysis of Elite Groups for Scholarly Events in Bioinformatics
Are You an Invited Speaker? A Bibliometric Analysis of Elite Groups for Scholarly Events in Bioinformatics Senator Jeong, Sungin Lee, and Hong-Gee Kim Biomedical Knowledge Engineering Laboratory, Seoul National University, 28–22 YeonGeon Dong, Jongno Gu, Seoul 110–749, Korea. E-mail: {senator, sunginlee, hgkim}@snu.ac.kr Participating in scholarly events (e.g., conferences, work- evaluation, but it would be hard to claim that they have pro- shops, etc.) as an elite-group member such as an orga- vided comprehensive lists of evaluation measurements. This nizing committee chair or member, program committee article aims not to provide such lists but to add to the current chair or member, session chair, invited speaker, or award winner is beneficial to a researcher’s career develop- practices an alternative metric that complements existing per- ment.The objective of this study is to investigate whether formance measures to give a more comprehensive picture of elite-group membership for scholarly events is represen- scholars’ performance. tative of scholars’ prominence, and which elite group is By one definition (Jeong, 2008), a scholarly event is the most prestigious. We collected data about 15 global “a sequentially and spatially organized collection of schol- (excluding regional) bioinformatics scholarly events held in 2007. We sampled (via stratified random sampling) ars’ interactions with the intention of delivering and shar- participants from elite groups in each event. Then, bib- ing knowledge, exchanging research ideas, and performing liometric indicators (total citations and h index) of seven related activities.” As such, scholarly events are communica- elite groups and a non-elite group, consisting of authors tion channels from which our new evaluation tool can draw who submitted at least one paper to an event but were its supporting evidence.