Rich Probabilistic Models for Genomic Data
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UNIVERSITY of CALIFORNIA RIVERSIDE Unsupervised And
UNIVERSITY OF CALIFORNIA RIVERSIDE Unsupervised and Zero-Shot Learning for Open-Domain Natural Language Processing A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science by Muhammad Abu Bakar Siddique June 2021 Dissertation Committee: Dr. Evangelos Christidis, Chairperson Dr. Amr Magdy Ahmed Dr. Samet Oymak Dr. Evangelos Papalexakis Copyright by Muhammad Abu Bakar Siddique 2021 The Dissertation of Muhammad Abu Bakar Siddique is approved: Committee Chairperson University of California, Riverside To my family for their unconditional love and support. i ABSTRACT OF THE DISSERTATION Unsupervised and Zero-Shot Learning for Open-Domain Natural Language Processing by Muhammad Abu Bakar Siddique Doctor of Philosophy, Graduate Program in Computer Science University of California, Riverside, June 2021 Dr. Evangelos Christidis, Chairperson Natural Language Processing (NLP) has yielded results that were unimaginable only a few years ago on a wide range of real-world tasks, thanks to deep neural networks and the availability of large-scale labeled training datasets. However, existing supervised methods assume an unscalable requirement that labeled training data is available for all classes: the acquisition of such data is prohibitively laborious and expensive. Therefore, zero-shot (or unsupervised) models that can seamlessly adapt to new unseen classes are indispensable for NLP methods to work in real-world applications effectively; such models mitigate (or eliminate) the need for collecting and annotating data for each domain. This dissertation ad- dresses three critical NLP problems in contexts where training data is scarce (or unavailable): intent detection, slot filling, and paraphrasing. Having reliable solutions for the mentioned problems in the open-domain setting pushes the frontiers of NLP a step towards practical conversational AI systems. -
A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast
A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast By Jessica M. Vera B.S. University of Wisconsin-Madison, 2007 A thesis submitted to the Faculty of the Graduate School in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Molecular, Cellular, and Developmental Biology 2015 This thesis entitled: A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast written by Jessica M. Vera has been approved for the Department of Molecular, Cellular, and Developmental Biology Tom Blumenthal Robin Dowell Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline iii Vera, Jessica M. (Ph.D., Molecular, Cellular and Developmental Biology) A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast Thesis Directed by Robin Dowell Cryptic unstable transcripts (CUTs) are a largely unexplored class of nuclear exosome degraded, non-coding RNAs in budding yeast. It is highly debated whether CUT transcription has a functional role in the cell or whether CUTs represent noise in the yeast transcriptome. I sought to ascertain the extent of conserved CUT expression across a variety of Saccharomyces yeast strains to further understand and characterize the nature of CUT expression. To this end I designed a Hidden Markov Model (HMM) to analyze strand-specific RNA sequencing data from nuclear exosome rrp6Δ mutants to identify and compare CUTs in four different yeast strains: S288c, Σ1278b, JAY291 (S.cerevisiae) and N17 (S.paradoxus). -
EMBO Encounters Issue43.Pdf
WINTER 2019/2020 ISSUE 43 Nine group leaders selected Meet the first EMBO Global Investigators PAGE 6 Accelerating scientific publishing EMBO publishing costs Review Commons Making our journals’ platform announced finances public PAGE 3 PAGES 10 – 11 Welcome, Young Investigators! Contract replaces stipend Marking ten years 27 group leaders join the programme EMBO Postdoctoral Fellowships EMBO Molecular Medicine receive an update celebrates anniversary PAGES 4 – 5 PAGE 7 PAGE 13 www.embo.org TABLE OF CONTENTS EMBO NEWS EMBO news Review Commons: accelerating publishing Page 3 EMBO Molecular Medicine turns ten © Marietta Schupp, EMBL Photolab Marietta Schupp, © Page 13 Editorial MBO was founded by scientists for Introducing 27 new Young Investigators scientists. This philosophy remains at Pages 4-5 Ethe heart of our organization until today. EMBO Members are vital in the running of our Meet the first EMBO Global programmes and activities: they screen appli- Accelerating scientific publishing 17 journals on board Investigators cations, interview candidates, decide on fund- Review Commons will manage the transfer of ing, and provide strategic direction. On pages EMBO and ASAPbio announced pre-journal portable review platform the manuscript, reviews, and responses to affili- Page 6 8-9 four members describe why they chose to ate journals. A consortium of seventeen journals New members meet in Heidelberg dedicate their time to an EMBO Committee across six publishers (see box) have joined the Fellowships: from stipends to contracts Pages 14 – 15 and what they took away from the experience. n December 2019, EMBO, in partnership with decide to submit their work to a journal, it will project by committing to use the Review Commons Page 7 When EMBO was created, the focus lay ASAPbio, launched Review Commons, a multi- allow editors to make efficient editorial decisions referee reports for their independent editorial deci- specifically on fostering cross-border inter- Ipublisher partnership which aims to stream- based on existing referee comments. -
University of California Santa Cruz Sample
UNIVERSITY OF CALIFORNIA SANTA CRUZ SAMPLE-SPECIFIC CANCER PATHWAY ANALYSIS USING PARADIGM A dissertation submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in BIOMOLECULAR ENGINEERING AND BIOINFORMATICS by Stephen C. Benz June 2012 The Dissertation of Stephen C. Benz is approved: Professor David Haussler, Chair Professor Joshua Stuart Professor Nader Pourmand Dean Tyrus Miller Vice Provost and Dean of Graduate Studies Copyright c by Stephen C. Benz 2012 Table of Contents List of Figures v List of Tables xi Abstract xii Dedication xiv Acknowledgments xv 1 Introduction 1 1.1 Identifying Genomic Alterations . 2 1.2 Pathway Analysis . 5 2 Methods to Integrate Cancer Genomics Data 10 2.1 UCSC Cancer Genomics Browser . 11 2.2 BioIntegrator . 16 3 Pathway Analysis Using PARADIGM 20 3.1 Method . 21 3.2 Comparisons . 26 3.2.1 Distinguishing True Networks From Decoys . 27 3.2.2 Tumor versus Normal - Pathways associated with Ovarian Cancer 29 3.2.3 Differentially Regulated Pathways in ER+ve vs ER-ve breast can- cers . 36 3.2.4 Therapy response prediction using pathways (Platinum Free In- terval in Ovarian Cancer) . 38 3.3 Unsupervised Stratification of Cancer Patients by Pathway Activities . 42 4 SuperPathway - A Global Pathway Model for Cancer 51 4.1 SuperPathway in Ovarian Cancer . 55 4.2 SuperPathway in Breast Cancer . 61 iii 4.2.1 Chin-Naderi Cohort . 61 4.2.2 TCGA Breast Cancer . 63 4.3 Cross-Cancer SuperPathway . 67 5 Pathway Analysis of Drug Effects 74 5.1 SuperPathway on Breast Cell Lines . -
Introduction
Introduction IJCAI-01 Conference Committee IJCAI-01 Program Committee: Contents: CONFERENCE CHAIR: Elisabeth André, DFKI GmbH (Germany) Introduction 2 Hector J. Levesque, University of Toronto (Canada) Minoru Asada, Osaka University (Japan) Sponsors & Committees 2-3 Franz Baader, RWTH Aachen (Germany) PROGRAM CHAIR: IJCAI-01 Awards 4 Craig Boutilier, University of Toronto (Canada) Bernhard Nebel,Albert-Ludwigs-Universität, Freiburg Didier Dubois, IRIT-CNRS (France) Conference at a Glance 5 (Germany) Maria Fox, University of Durham (United Kingdom) Workshop Program 6-7 LOCAL ARRANGEMENTS CHAIR: Hector Geffner, Universidad Simón Bolívar Doctoral Consortium 8 James Hoard, The Boeing Company, Seattle (USA) (Venezuela) Tutorial Program 8 SECRETARY-TREASURER: Georg Gottlob,Vienna University of Technology (Austria) Conference Program Highlights 9 Ronald J. Brachman,AT&T Labs – Research (USA) Invited Speakers 10 Haym Hirsh, Rutgers University (USA) IAAI-01 Conference 11 Eduard Hovy, Information Sciences Institute (USA) Advisory Committee: Joxan Jaffar, National University of Singapore Technical Program 12-19 Bruce Buchanan, University of Pittsburgh (USA) (Singapore) Exhibit Program 20-23 Silvia Coradeschi, Örebro University (Sweden) Daphne Koller, Stanford University (USA) RoboCup 2001 24 Olivier Faugeras, INRIA (France) Fangzhen Lin, Hong Kong University of Science and Registration Information 25 Cheng Hu, Chinese Academy of Sciences (China) Technolog y (Hong Kong) General Information 25-27 Nicholas Jennings, University of London (England) Heikki Mannila, Nokia Research Center (Finland) Conference Maps 28-30 Henry Kautz, University of Washington (USA) Robert Milne, Intelligent Applications (United Kingdom) IJCAI-03 Conference 31 Robert Mercer, University of Western Ontario (Canada) Daniele Nardi, Università di Roma “La Sapienza” Special Meetings 31 Silvia Miksch,Vienna University of Technology (Italy) (Austria) Dana Nau, University of Maryland (USA) Devika Subramanian, Rice University (USA) Patrick Prosser, University of Glasgow (UK) Welcome to IJCAI-01 L. -
UCLA UCLA Electronic Theses and Dissertations
UCLA UCLA Electronic Theses and Dissertations Title Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods Permalink https://escholarship.org/uc/item/0tr9j01r Author Sun, Yidan Publication Date 2021 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA Los Angeles Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Statistics by Yidan Sun 2021 © Copyright by Yidan Sun 2021 ABSTRACT OF THE DISSERTATION Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods by Yidan Sun Doctor of Philosophy in Statistics University of California, Los Angeles, 2021 Professor Jingyi Li, Chair In a bipartite network, nodes are divided into two types, and edges are only allowed to connect nodes of different types. Bipartite network clustering problems aim to identify node groups with more edges between themselves and fewer edges to the rest of the network. The approaches for community detection in the bipartite network can roughly be classified into algorithmic and model-based methods. The algorithmic methods solve the problem either by greedy searches in a heuristic way or optimizing based on some criteria over all possible partitions. The model-based methods fit a generative model to the observed data and study the model in a statistically principled way. In this dissertation, we mainly focus on bipartite clustering under two scenarios: incorporation of node covariates and detection of mixed membership communities. -
Director's Update
Director’s Update Francis S. Collins, M.D., Ph.D. Director, National Institutes of Health Council of Councils Meeting September 6, 2019 Changes in Leadership . Retirements – Paul A. Sieving, M.D., Ph.D., Director of the National Eye Institute Paul Sieving (7/29/19) Linda Birnbaum – Linda S. Birnbaum, Ph.D., D.A.B.T., A.T.S., Director of the National Institute of Environmental Health Sciences (10/3/19) . New Hires – Noni Byrnes, Ph.D., Director, Center for Scientific Review (2/27/19) Noni Byrnes – Debara L. Tucci, M.D., M.S., M.B.A., Director, National Institute on Deafness and Other Communication Disorders (9/3/19) Debara Tucci . New Positions – Tara A. Schwetz, Ph.D., Associate Deputy Director, NIH (1/7/19) Tara Schwetz 2019 Inaugural Inductees Topics for Today . NIH HEAL (Helping to End Addiction Long-termSM) Initiative – HEALing Communities Study . Artificial Intelligence: ACD WG Update . Human Genome Editing – Exciting Promise for Cures, Need for Moratorium on Germline . Addressing Foreign Influence on Research … and Harassment in the Research Workplace NIH HEAL InitiativeSM . Trans-NIH research initiative to: – Improve prevention and treatment strategies for opioid misuse and addiction – Enhance pain management . Goals are scientific solutions to the opioid crisis . Coordinating with the HHS Secretary, Surgeon General, federal partners, local government officials and communities www.nih.gov/heal-initiative NIH HEAL Initiative: At a Glance . $500M/year – Will spend $930M in FY2019 . 12 NIH Institute and Centers leading 26 HEAL research projects – Over 20 collaborating Institutes, Centers, and Offices – From prevention research, basic and translational research, clinical trials, to implementation science – Multiple projects integrating research into new settings . -
Top 100 AI Leaders in Drug Discovery and Advanced Healthcare Introduction
Top 100 AI Leaders in Drug Discovery and Advanced Healthcare www.dka.global Introduction Over the last several years, the pharmaceutical and healthcare organizations have developed a strong interest toward applying artificial intelligence (AI) in various areas, ranging from medical image analysis and elaboration of electronic health records (EHRs) to more basic research like building disease ontologies, preclinical drug discovery, and clinical trials. The demand for the ML/AI technologies, as well as for ML/AI talent, is growing in pharmaceutical and healthcare industries and driving the formation of a new interdisciplinary industry (‘data-driven healthcare’). Consequently, there is a growing number of AI-driven startups and emerging companies offering technology solutions for drug discovery and healthcare. Another important source of advanced expertise in AI for drug discovery and healthcare comes from top technology corporations (Google, Microsoft, Tencent, etc), which are increasingly focusing on applying their technological resources for tackling health-related challenges, or providing technology platforms on rent bases for conducting research analytics by life science professionals. Some of the leading pharmaceutical giants, like GSK, AstraZeneca, Pfizer and Novartis, are already making steps towards aligning their internal research workflows and development strategies to start embracing AI-driven digital transformation at scale. However, the pharmaceutical industry at large is still lagging behind in adopting AI, compared to more traditional consumer industries -- finance, retail etc. The above three main forces are driving the growth in the AI implementation in pharmaceutical and advanced healthcare research, but the overall success depends strongly on the availability of highly skilled interdisciplinary leaders, able to innovate, organize and guide in this direction. -
Probabilistic Models for Species Tree Inference and Orthology Analysis
Probabilistic Models for Species Tree Inference and Orthology Analysis IKRAM ULLAH Doctoral Thesis Stockholm, Sweden 2015 TRITA-CSC-A-2015:12 ISSN-1653-5723 KTH School of Computer Science and Communication ISRN-KTH/CSC/A–15/12-SE SE-100 44 Stockholm ISBN 978-91-7595-619-0 SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framläg- ges till offentlig granskning för avläggande av teknologie doktorsexamen i datalogi fredagen den 12 juni 2015, klockan 13.00 i conference room Air, Scilifelab, Solna. © Ikram Ullah, June 2015 Tryck: Universitetsservice US AB iii To my family iv Abstract A phylogenetic tree is used to model gene evolution and species evolution using molecular sequence data. For artifactual and biological reasons, a gene tree may differ from a species tree, a phenomenon known as gene tree-species tree incongruence. Assuming the presence of one or more evolutionary events, e.g, gene duplication, gene loss, and lateral gene transfer (LGT), the incon- gruence may be explained using a reconciliation of a gene tree inside a species tree. Such information has biological utilities, e.g., inference of orthologous relationship between genes. In this thesis, we present probabilistic models and methods for orthology analysis and species tree inference, while accounting for evolutionary factors such as gene duplication, gene loss, and sequence evolution. Furthermore, we use a probabilistic LGT-aware model for inferring gene trees having temporal information for duplication and LGT events. In the first project, we present a Bayesian method, called DLRSOrthology, for estimating orthology probabilities using the DLRS model: a probabilistic model integrating gene evolution, a relaxed molecular clock for substitution rates, and sequence evolution. -
Bioengineering Professor Trey Ideker Wins 2009 Overton Prize
Bioengineering Professor Trey Ideker Wins 2009 Overton Prize March 13, 2009 Daniel Kane University of California, San Diego bioengineering professor Trey Ideker-a network and systems biology pioneer-has won the International Society for Computational Biology's Overton Prize. The Overton prize is awarded each year to an early-to-mid-career scientist who has already made a significant contribution to the field of computational biology. Trey Ideker is an Associate Professor of Bioengineering at UC San Diego's Jacobs School of Engineering, Adjunct Professor of Computer Science, and member of the Moores UCSD Cancer Center. He is a pioneer in using genome-scale measurements to construct network models of cellular processes and disease. His recent research activities include development of software and algorithms for protein network analysis, network-level comparison of pathogens, and genome-scale models of the response to DNA-damaging agents. "Receiving this award is a wonderful honor and helps to confirm that the work we have been doing for the past several years has been useful to people," said Ideker. "This award also provides great recognition to UC San Diego which has fantastic bioinformatics programs both at the undergraduate and graduate level. I could never have done it without the help of some really first-rate bioinformatics and bioengineering graduate students," said Ideker. Ideker is on the faculty of the Jacobs School of Engineering's Department of Bioengineering, which ranks 2nd in the nationfor biomedical engineering, according to the latest US News rankings. The bioengineering department has ranked among the top five programs in the nation every year for the past decade. -
David A. Knowles
David A. Knowles Stanford University School of Medicine 300 Pasteur Drive Stanford, CA 94305-5105 email: [email protected] url: http://cs.stanford.edu/∼davidknowles/ Nationality: British Education 2008-2012 PhD Engineering (Machine Learning) University of Cambridge Thesis: Bayesian non-parametric models and inference for sparse and hierarchical latent structure Advisor: Prof. Zoubin Ghahramani 2007-2008 MSc Bioinformatics and Systems Biology - Distinction Imperial College London Thesis: Statistical tools for ulta-deep pyrosequencing of fast evolving viruses. Thesis advisor: Prof. Susan Holmes, Statistics Department, Stanford University. 2003-2007 MEng Engineering - Distinction Thesis: A non-parametric extension to Independent Components Analysis. Thesis advisor: Prof. Zoubin Ghahramani. BA Natural Sciences (Physics) - First Class University of Cambridge Academic Positions 2014-ongoing Postdoctoral researcher (Genetics, Pathology) Stanford University Co-advisors: Prof. Jonathan Pritchard, Prof. Sylvia Plevritis 2012-2014 Postdoctoral researcher (Computer Science) Stanford University Advisor: Prof. Daphne Koller 2008-2012 PhD Candidate, Roger Needham Scholar, Wolfson College, University of Cambridge Machine Learning Group, Cambridge University Engineering Department 2006 Summer Undergraduate Research Fellow California Institute of Technology Honours & Awards 2017 Stanford Cancer Systems Biology Symposium — Poster Award 2014 The International Society for Bayesian Statistics Travel Award for best invited Bayesian paper 2014 The International Society for Bayesian Statistics Dennis V. Lindley Prize for innovative research in Bayesian Statistics 2007 Charles Lamb University prize for first place in Information Engineering 1 Sir Joseph Larmor Silver Plate for undergraduates adjudged to be the most worthy for intellectual qualifications or moral conduct and practical activities Three other college prizes (Cargill, Cunningham and College) 2005 Wright Prize for ranking 5/600 in Natural Sciences Earle Year Prize for top 4 students across all subjects at St. -
Learning from Graph Neighborhoods Using Lstms
Learning From Graph Neighborhoods Using LSTMs Rakshit Agrawal∗ Luca de Alfaro Vassilis Polychronopoulos [email protected], [email protected], [email protected] Computer Science Department, University of California, Santa Cruz Technical Report UCSC-SOE-16-17 School of Engineering, UC Santa Cruz November 18, 2016 Abstract and of the grades they assigned. If we wish to be more so- phisticated, and try to determine which of these people are Many prediction problems can be phrased as inferences good graders, we could look also at the work performed over local neighborhoods of graphs. The graph repre- by these people, expanding our analysis outwards to the sents the interaction between entities, and the neighbor- 2- or 3-neighborhood of each item. hood of each entity contains information that allows the For another example, consider the problem of predict- inferences or predictions. We present an approach for ap- ing which bitcoin addresses will spend their deposited plying machine learning directly to such graph neighbor- funds in the near future. Bitcoins are held in “addresses”; hoods, yielding predicitons for graph nodes on the basis these addresses can participate in transactions where they of the structure of their local neighborhood and the fea- send or receive bitcoins. To predict which addresses are tures of the nodes in it. Our approach allows predictions likely to spend their bitcoin in the near future, it is natural to be learned directly from examples, bypassing the step to build a graph of addresses and transactions, and con- of creating and tuning an inference model or summarizing sider neighborhoods of each address.