Digitally Enabled Genomic Medicine
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RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY
RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press ISSN: 1790-5125 www.wseas.org ISBN: 978-960-474-141-0 RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press www.wseas.org Copyright © 2009, by WSEAS Press All the copyright of the present book belongs to the World Scientific and Engineering Academy and Society Press. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the Editor of World Scientific and Engineering Academy and Society Press. All papers of the present volume were peer reviewed -
The Principled Design of Large-Scale Recursive Neural Network Architectures–DAG-Rnns and the Protein Structure Prediction Problem
Journal of Machine Learning Research 4 (2003) 575-602 Submitted 2/02; Revised 4/03; Published 9/03 The Principled Design of Large-Scale Recursive Neural Network Architectures–DAG-RNNs and the Protein Structure Prediction Problem Pierre Baldi [email protected] Gianluca Pollastri [email protected] School of Information and Computer Science Institute for Genomics and Bioinformatics University of California, Irvine Irvine, CA 92697-3425, USA Editor: Michael I. Jordan Abstract We describe a general methodology for the design of large-scale recursive neural network architec- tures (DAG-RNNs) which comprises three fundamental steps: (1) representation of a given domain using suitable directed acyclic graphs (DAGs) to connect visible and hidden node variables; (2) parameterization of the relationship between each variable and its parent variables by feedforward neural networks; and (3) application of weight-sharing within appropriate subsets of DAG connec- tions to capture stationarity and control model complexity. Here we use these principles to derive several specific classes of DAG-RNN architectures based on lattices, trees, and other structured graphs. These architectures can process a wide range of data structures with variable sizes and dimensions. While the overall resulting models remain probabilistic, the internal deterministic dy- namics allows efficient propagation of information, as well as training by gradient descent, in order to tackle large-scale problems. These methods are used here to derive state-of-the-art predictors for protein structural features such as secondary structure (1D) and both fine- and coarse-grained contact maps (2D). Extensions, relationships to graphical models, and implications for the design of neural architectures are briefly discussed. -
Methodology for Predicting Semantic Annotations of Protein Sequences by Feature Extraction Derived of Statistical Contact Potentials and Continuous Wavelet Transform
Universidad Nacional de Colombia Sede Manizales Master’s Thesis Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform Author: Supervisor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez A thesis submitted in fulfillment of the requirements for the degree of Master’s on Engineering - Industrial Automation in the Department of Electronic, Electric Engineering and Computation Signal Processing and Recognition Group June 2014 Universidad Nacional de Colombia Sede Manizales Tesis de Maestr´ıa Metodolog´ıapara predecir la anotaci´on sem´antica de prote´ınaspor medio de extracci´on de caracter´ısticas derivadas de potenciales de contacto y transformada wavelet continua Autor: Tutor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez Tesis presentada en cumplimiento a los requerimientos necesarios para obtener el grado de Maestr´ıaen Ingenier´ıaen Automatizaci´onIndustrial en el Departamento de Ingenier´ıaEl´ectrica,Electr´onicay Computaci´on Grupo de Procesamiento Digital de Senales Enero 2014 UNIVERSIDAD NACIONAL DE COLOMBIA Abstract Faculty of Engineering and Architecture Department of Electronic, Electric Engineering and Computation Master’s on Engineering - Industrial Automation Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform by Gustavo Alonso Arango Argoty In this thesis, a method to predict semantic annotations of the proteins from its primary structure is proposed. The main contribution of this thesis lies in the implementation of a novel protein feature representation, which makes use of the pairwise statistical contact potentials describing the protein interactions and geometry at the atomic level. -
Program Book
Pacific Symposium on Biocomputing 2016 January 4-8, 2016 Big Island of Hawaii Program Book PACIFIC SYMPOSIUM ON BIOCOMPUTING 2016 Big Island of Hawaii, January 4-8, 2016 Welcome to PSB 2016! We have prepared this program book to give you quick access to information you need for PSB 2016. Enclosed you will find • Logistics information • Menus for PSB hosted meals • Full conference schedule • Call for Session and Workshop Proposals for PSB 2017 • Poster/abstract titles and authors • Participant List Conference materials are also available online at http://psb.stanford.edu/conference-materials/. PSB 2016 gratefully acknowledges the support the Institute for Computational Biology, a collaborative effort of Case Western Reserve University, the Cleveland Clinic Foundation, and University Hospitals; the National Institutes of Health (NIH), the National Science Foundation (NSF); and the International Society for Computational Biology (ISCB). If you or your institution are interested in sponsoring, PSB, please contact Tiffany Murray at [email protected] If you have any questions, the PSB registration staff (Tiffany Murray, Georgia Hansen, Brant Hansen, Kasey Miller, and BJ Morrison-McKay) are happy to help you. Aloha! Russ Altman Keith Dunker Larry Hunter Teri Klein Maryln Ritchie The PSB 2016 Organizers PACIFIC SYMPOSIUM ON BIOCOMPUTING 2016 Big Island of Hawaii, January 4-8, 2016 SPEAKER INFORMATION Oral presentations of accepted proceedings papers will take place in Salon 2 & 3. Speakers are allotted 10 minutes for presentation and 5 minutes for questions for a total of 15 minutes. Instructions for uploading talks were sent to authors with oral presentations. If you need assistance with this, please see Tiffany Murray or another PSB staff member. -
Deep Learning in Chemoinformatics Using Tensor Flow
UC Irvine UC Irvine Electronic Theses and Dissertations Title Deep Learning in Chemoinformatics using Tensor Flow Permalink https://escholarship.org/uc/item/963505w5 Author Jain, Akshay Publication Date 2017 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, IRVINE Deep Learning in Chemoinformatics using Tensor Flow THESIS submitted in partial satisfaction of the requirements for the degree of MASTER OF SCIENCE in Computer Science by Akshay Jain Thesis Committee: Professor Pierre Baldi, Chair Professor Cristina Videira Lopes Professor Eric Mjolsness 2017 c 2017 Akshay Jain DEDICATION To my family and friends. ii TABLE OF CONTENTS Page LIST OF FIGURES v LIST OF TABLES vi ACKNOWLEDGMENTS vii ABSTRACT OF THE THESIS viii 1 Introduction 1 1.1 QSAR Prediction Methods . .2 1.2 Deep Learning . .4 2 Artificial Neural Networks(ANN) 5 2.1 Artificial Neuron . .5 2.2 Activation Function . .7 2.3 Loss function . .8 2.4 Optimization . .8 3 Deep Recursive Architectures 10 3.1 Recurrent Neural Networks (RNN) . 10 3.2 Recursive Neural Networks . 11 3.3 Directed Acyclic Graph Recursive Neural Networks (DAG-RNN) . 11 4 UG-RNN for small molecules 14 4.1 DAG Generation . 16 4.2 Local Information Vector . 16 4.3 Contextual Vectors . 17 4.4 Activity Prediction . 17 4.5 UG-RNN With Contracted Rings (UG-RNN-CR) . 18 4.6 Example: UG-RNN Model of Propionic Acid . 20 5 Implementation 24 6 Data & Results 26 6.1 Aqueous Solubility Prediction . 26 6.2 Melting Point Prediction . 28 iii 7 Conclusions 30 Bibliography 32 A Source Code 37 A.1 UGRNN . -
Course Outline
Department of Computer Science and Software Engineering COMP 6811 Bioinformatics Algorithms (Reading Course) Fall 2019 Section AA Instructor: Gregory Butler Curriculum Description COMP 6811 Bioinformatics Algorithms (4 credits) The principal objectives of the course are to cover the major algorithms used in bioinformatics; sequence alignment, multiple sequence alignment, phylogeny; classi- fying patterns in sequences; secondary structure prediction; 3D structure prediction; analysis of gene expression data. This includes dynamic programming, machine learning, simulated annealing, and clustering algorithms. Algorithmic principles will be emphasized. A project is required. Outline of Topics The course will focus on algorithms for protein sequence analysis. It will not cover genome assembly, genome mapping, or gene recognition. • Background in Biology and Genomics • Sequence Alignment: Pairwise and Multiple • Representation of Protein Amino Acid Composition • Profile Hidden Markov Models • Specificity Determining Sites • Curation, Annotation, and Ontologies • Machine Learning: Secondary Structure, Signals, Subcellular Location • Protein Families, Phylogenomics, and Orthologous Groups • Profile-Based Alignments • Algorithms Based on k-mers 1 Texts | in Library D. Higgins and W. Taylor (editors). Bioinformatics: Sequence, Structure and Databanks, Oxford University Press, 2000. A. D. Baxevanis and B. F. F. Ouelette. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Wiley, 1998. Richard Durbin, Sean R. Eddy, Anders Krogh, -
Deep Learning in Science Pierre Baldi Frontmatter More Information
Cambridge University Press 978-1-108-84535-9 — Deep Learning in Science Pierre Baldi Frontmatter More Information Deep Learning in Science This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuro- science. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking. Pierre Baldi is Distinguished Professor of Computer Science at University of California, Irvine. His main research interest is understanding intelligence in brains and machines. He has made seminal contributions to the theory of deep learning and its applications to the natural sciences, and has written four other books. © in this web service Cambridge University Press www.cambridge.org Cambridge University Press 978-1-108-84535-9 — Deep Learning in -
Modeling and Analysis of RNA-Seq Data: a Review from a Statistical Perspective
Modeling and analysis of RNA-seq data: a review from a statistical perspective Wei Vivian Li 1 and Jingyi Jessica Li 1;2;∗ Abstract Background: Since the invention of next-generation RNA sequencing (RNA-seq) technolo- gies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels (samples, genes, transcripts, and exons) involve multiple statistical and computational questions, some of which remain challenging up to date. Results: We review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations. Conclusion: The development of statistical and computational methods for analyzing RNA- seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statical models and exhibit dif- ferent performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development. 1 Introduction RNA sequencing (RNA-seq) uses the next generation sequencing (NGS) technologies to reveal arXiv:1804.06050v3 [q-bio.GN] 1 May 2018 the presence and quantity of RNA molecules in biological samples. Since its invention, RNA- seq has revolutionized transcriptome analysis in biological research. RNA-seq does not require any prior knowledge on RNA sequences, and its high-throughput manner allows for genome-wide profiling of transcriptome landscapes [1,2]. -
24019 - Probabilistic Graphical Models
Year : 2019/20 24019 - Probabilistic Graphical Models Syllabus Information Academic Course: 2019/20 Academic Center: 337 - Polytechnic School Study: 3379 - Bachelor's (degree) programme in Telecommunications Network Engineering Subject: 24019 - Probabilistic Graphical Models Credits: 5.0 Course: 3 Teaching languages: Theory: Grup 1: English Practice: Grup 101: English Grup 102: English Seminar: Grup 101: English Grup 102: English Teachers: Vicente Gomez Cerda Teaching Period: Second Quarter Schedule: --- Presentation Probabilistic graphical models (PGMs) are powerful modeling tools for reasoning and decision making under uncertainty. PGMs have many application domains, including computer vision, natural language processing, efficient coding, and computational biology. PGMs connects graph theory and probability theory, and provide a flexible framework for modeling large collections of random variables with complex interactions. This is an introductory course which focuses on two main principles: (1) emphasizing the role of PGMs as a unifying language in machine learning that allows a natural specification of many problem domains with inherent uncertainty, and (2) providing a set of computational tools for probabilistic inference (making predictions that can be used to aid decision making), and learning (estimating the graph structure and their parameters from data). Associated skills Basic Competences That the students can apply their knowledge to their work or vocation of a professional form in a professional way and possess the competences which are usually proved by means of the elaboration and defense of arguments and solving the solution of problems within their study area. That the students have the ability of collecting and interpreting relevant data (normally within their study area) to issue judgements which include a reflection about relevant topics of social, scientific or ethical nature. -
Curriculum Vitae—Nir Friedman
Nir Friedman Last update: February 14, 2019 Curriculum Vitae|Nir Friedman School of Computer Science and Engineering email: [email protected] Alexander Silberman Institute of Life Sciences Office: +972-73-388-4720 Hebrew University of Jerusalem Cellular: +972-54-882-0432 Jerusalem 91904, ISRAEL Professional History Professor 2009{present Alexander Silberman Institute of Life Sciences The Hebrew University of Jerusalem Professor 2007{present School of Computer Science & Engineering The Hebrew University of Jerusalem Associate Professor 2002{2007 School of Computer Science & Engineering The Hebrew University of Jerusalem Senior Lecturer 1998{2002 School of Computer Science & Engineering The Hebrew University of Jerusalem Postdoctoral Scholar 1996{1998 Division of Computer Science University of California, Berkeley Education Ph.D. Computer Science, Stanford University 1992{1997 M.Sc. Math. & Computer Science, Weizmann Institute of Science 1990{1992 B.Sc. Math. & Computer Science, Tel-Aviv University 1983{1987 Awards Alexander von Humboldt Foundation Research Award 2015 Fellow of the International Society of Computational Biology 2014 European Research Council \Advanced Grant" research award 2014-2018 \Test of Time" Award Research in Computational Molecular Biology (RECOMB) 2012 Most cited paper in 12-year window in RECOMB Michael Bruno Memorial Award 2010 \Israeli scholars and scientists of truly exceptional promise, whose achievements to date sug- gest future breakthroughs in their respective fields” [age < 50] European Research Council \Advanced -
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
Learning Belief Networks in the Presence of Missing Values and Hidden Variables Nir Friedman Computer Science Division, 387 Soda Hall University of California, Berkeley, CA 94720 [email protected] Abstract sponds to a random variable. This graph represents a set of conditional independence properties of the represented In recent years there has been a ¯urry of works distribution. This component captures the structure of the on learning probabilistic belief networks. Cur- probability distribution, and is exploited for ef®cient infer- rent state of the art methods have been shown to ence and decision making. Thus, while belief networks can be successful for two learning scenarios: learn- represent arbitrary probability distributions, they provide ing both network structure and parameters from computational advantage for those distributions that can be complete data, and learning parameters for a ®xed represented with a simple structure. The second component network from incomplete dataÐthat is, in the is a collection of local interaction models that describe the presence of missing values or hidden variables. conditional probability of each variable given its parents However, no method has yet been demonstrated in the graph. Together, these two components represent a to effectively learn network structure from incom- unique probability distribution [Pearl 1988]. plete data. Eliciting belief networks from experts can be a laborious In this paper, we propose a new method for and expensive process in large applications. Thus, in recent learning network structure from incomplete data. years there has been a growing interest in learning belief This method is based on an extension of the networks from data [Cooper and Herskovits 1992; Lam and Expectation-Maximization (EM) algorithm for Bacchus 1994; Heckerman et al. -
Using Bayesian Networks to Analyze Expression Data
JOURNAL OFCOMPUTATIONAL BIOLOGY Volume7, Numbers 3/4,2000 MaryAnn Liebert,Inc. Pp. 601–620 Using Bayesian Networks to Analyze Expression Data NIR FRIEDMAN, 1 MICHAL LINIAL, 2 IFTACH NACHMAN, 3 andDANA PE’ER 1 ABSTRACT DNAhybridization arrayssimultaneously measurethe expression level forthousands of genes.These measurementsprovide a “snapshot”of transcription levels within the cell. Ama- jorchallenge in computationalbiology is touncover ,fromsuch measurements,gene/ protein interactions andkey biological features ofcellular systems. In this paper,wepropose a new frameworkfor discovering interactions betweengenes based onmultiple expression mea- surements. This frameworkbuilds onthe use of Bayesian networks forrepresenting statistical dependencies. ABayesiannetwork is agraph-basedmodel of joint multivariateprobability distributions thatcaptures properties ofconditional independence betweenvariables. Such models areattractive for their ability todescribe complexstochastic processes andbecause theyprovide a clear methodologyfor learning from(noisy) observations.We start byshowing howBayesian networks can describe interactions betweengenes. W ethen describe amethod forrecovering gene interactions frommicroarray data using tools forlearning Bayesiannet- works.Finally, we demonstratethis methodon the S.cerevisiae cell-cycle measurementsof Spellman et al. (1998). Key words: geneexpression, microarrays, Bayesian methods. 1.INTRODUCTION centralgoal of molecularbiology isto understand the regulation of protein synthesis and its Areactionsto external