Information Extraction of Protein Phosphorylation from Biomedical Literature

Information Extraction of Protein Phosphorylation from Biomedical Literature

M. Narayanaswamy (Anna University, India), K. E. Ravikumar (Anna University, India), Z. Z. Hu (Georgetown University Medical Center, USA), K. Vijay-Shanker (University of Delaware, USA) and C. H. Wu (Georgetown University Medical Center, USA)
DOI: 10.4018/978-1-60566-274-9.ch009
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Abstract

Protein posttranslational modification (PTM) is a fundamental biological process, and currently few text mining systems focus on PTM information extraction. A rule-based text mining system, RLIMS-P (Rule-based LIterature Mining System for Protein Phosphorylation), was recently developed by our group to extract protein substrate, kinase and phosphorylated residue/sites from MEDLINE abstracts. This chapter covers the evaluation and benchmarking of RLIMS-P and highlights some novel and unique features of the system. The extraction patterns of RLIMS-P capture a range of lexical, syntactic and semantic constraints found in sentences expressing phosphorylation information. RLIMS-P also has a second phase that puts together information extracted from different sentences. This is an important feature since it is not common to find the kinase, substrate and site of phosphorylation to be mentioned in the same sentence. Small modifications to the rules for extraction of phosphorylation information have also allowed us to develop systems for extraction of two other PTMs, acetylation and methylation. A thorough evaluation of these two systems needs to be completed. Finally, an online version of RLIMSP with enhanced functionalities, namely, phosphorylation annotation ranking, evidence tagging, and protein entity mapping, has been developed and is publicly accessible.
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Introduction

Protein post translational modification (PTM), a molecular event in which a protein is chemically modified during or after its being translated, is essential to many biological processes. Protein phosphorylation is one of the most common PTMs, which involves the addition of a phosphate group to serine, threonine or tyrosine residues of a protein, and is fundamental to cell metabolism, growth and development. Many cellular signal transduction pathways are activated through phosphorylation of specific proteins that initiate a cascade of protein-protein interactions, leading to specific gene regulation and cellular response. It is estimated that one third of the mammalian genome coding sequences code for phosphoproteins. The phosphorylation state of cellular proteins is also highly dynamic, detection, quantification and functional analysis of the dynamic phosphorylation status of proteins, and the kinases involved are essential for understanding the regulatory networks of biological pathways and processes, which are under extensive investigation by researchers of many areas of biological research.

While PTMs are fundamental to our understanding of cellular processes, the experimental PTM data are largely buried in free-text literature. For example, a recent PubMed query for protein phosphorylation returned 103,478 papers. Although PTMs, especially phosphorylation, are among the most important protein features annotated in protein databases, currently only limited amount of data are annotated in a few resources, such as UniProt Knowledgebase (UniProtKB) (Wu et al., 2006), and specialized databases including Phospho.ELM and PhosphoSite, which can not keep up with the fast-growing literature. With the increasing volume of scientific literature now available electronically, efficient text mining tools will greatly facilitate the extraction of information buried in free text. Information extraction of PTM information on specific proteins, sites/residues being modified, and enzymes involved in the modification are particularly useful not only to assist database curation for protein site features and related pathway or disease information, but also to allow users to quickly browse and analyze the literature, and help other bioinformatics software to integrate text mining component into pathway and network analysis.

There are many BioNLP relation extraction systems that have been developed in the past few years. Some of these employ special rule/pattern based approaches (e.g., Blaschke et al., 1999; Pustejovsky et al., 2002). Other approaches for extracting protein-protein interactions include detecting co-occurring proteins (Proux et al., 2000; Stapley and Benoit, 2000; Stephens et al., 2001), or using a text parser tailored for the specialized language typically found in the biology literature (e.g., Friedman et al., 2001; Daraselia et al., 2004). The rule-based approach involves designing patterns to extract specific types of information, while the parser approach requires development of grammars, methods for disambiguation and further effort to provide methods that map parse information to objects involved in the relation. More modern approaches employ machine learning for relation extraction (e.g., Bunescu and Mooney, Gioliana et al). Such methods require an annotated corpus, where the sentences are marked with the relation and related objects manually. Machine learning techniques are then employed to learn a model that will extract from unseen text.

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Table of Contents
Preface
Violaine Prince, Mathieu Roche
Chapter 1
Sophia Ananiadou
Text mining provides the automated means to manage information overload and overlook. By adding meaning to text, text mining techniques produce a... Sample PDF
Text Mining for Biomedicine
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Chapter 2
Dimitrios Kokkinakis
The identification and mapping of terminology from large repositories of life science data onto concept hierarchies constitute an important initial... Sample PDF
Lexical Granularity for Automatic Indexing and Means to Achieve It: The Case of Swedish MeSH®
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Chapter 3
M. Teresa Martín-Valdivia, Arturo Montejo-Ráez, M. C. Díaz-Galiano, José M. Perea Ortega, L. Alfonso Ureña-López
This chapter argues for the integration of clinical knowledge extracted from medical ontologies in order to improve a Multi-Label Text... Sample PDF
Expanding Terms with Medical Ontologies to Improve a Multi-Label Text Categorization System
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Chapter 4
Piotr Pezik, Antonio Jimeno Yepes, Dietrich Rebholz-Schuhmann
The present chapter discusses the use of terminological resources for Information Retrieval in the biomedical domain. The authors first introduce a... Sample PDF
Using Biomedical Terminological Resources for Information Retrieval
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Chapter 5
Laura Diosan, Alexandrina Rogozan, Jean-Pierre Pécuchet
The automatic alignment between a specialized terminology used by librarians in order to index concepts and a general vocabulary employed by a... Sample PDF
Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval
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Chapter 6
Vincent Claveau
This chapter presents a simple yet efficient approach to translate automatically unknown biomedical terms from one language into another. This... Sample PDF
Translation of Biomedical Terms by Inferring Rewriting Rules
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Chapter 7
Nils Reiter, Paul Buitelaar
This chapter is concerned with lexical enrichment of ontologies, that is how to enrich a given ontology with lexical information derived from a... Sample PDF
Lexical Enrichment of Biomedical Ontologies
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Chapter 8
Torsten Schiemann, Ulf Leser, Jörg Hakenberg
Ambiguity is a common phenomenon in text, especially in the biomedical domain. For instance, it is frequently the case that a gene, a protein... Sample PDF
Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach
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Chapter 9
M. Narayanaswamy, K. E. Ravikumar, Z. Z. Hu, K. Vijay-Shanker, C. H. Wu
Protein posttranslational modification (PTM) is a fundamental biological process, and currently few text mining systems focus on PTM information... Sample PDF
Information Extraction of Protein Phosphorylation from Biomedical Literature
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Chapter 10
Yves Kodratoff, Jérôme Azé, Lise Fontaine
This chapter argues that in order to extract significant knowledge from masses of technical texts, it is necessary to provide the field specialists... Sample PDF
CorTag: A Language for a Contextual Tagging of the Words Within Their Sentence
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Chapter 11
Yun Niu, Graeme Hirst
The task of question answering (QA) is to find an accurate and precise answer to a natural language question in some predefined text. Most existing... Sample PDF
Analyzing the Text of Clinical Literature for Question Answering
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Chapter 12
Nadine Lucas
This chapter presents the challenge of integrating knowledge at higher levels of discourse than the sentence, to avoid “missing the forest for the... Sample PDF
Discourse Processing for Text Mining
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Chapter 13
Dimosthenis Kyriazis, Anastasios Doulamis, Theodora Varvarigou
In this chapter, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of information (medical... Sample PDF
A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems
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Chapter 14
Yitao Zhang, Jon Patrick
The fast growing content of online articles of clinical case studies provides a useful source for extracting domain-specific knowledge for improving... Sample PDF
Extracting Patient Case Profiles with Domain-Specific Semantic Categories
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Chapter 15
Laura I. Furlong, Ferran Sanz
SNPs constitute key elements in genetic epidemiology and pharmacogenomics. While data about genetic variation is found at sequence databases... Sample PDF
Identification of Sequence Variants of Genes from Biomedical Literature: The OSIRIS Approach
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Chapter 16
Francisco M. Couto, Mário J. Silva, Vivian Lee, Emily Dimmer, Evelyn Camon, Rolf Apweiler
Molecular Biology research projects produced vast amounts of data, part of which has been preserved in a variety of public databases. However, a... Sample PDF
Verification of Uncurated Protein Annotations
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Chapter 17
Burr Settles
ABNER (A Biomedical Named Entity Recognizer) is an open-source software tool for text mining in the molecular biology literature. It processes... Sample PDF
A Software Tool for Biomedical Information Extraction (And Beyond)
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Chapter 18
Asanee Kawtrakul, Chaveevarn Pechsiri, Sachit Rajbhandari, Frederic Andres
Valuable knowledge has been distributed in heterogeneous formats on many different Web sites and other sources over the Internet. However, finding... Sample PDF
Problems-Solving Map Extraction with Collective Intelligence Analysis and Language Engineering
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Chapter 19
Christophe Jouis, Magali Roux-Rouquié, Jean-Gabriel Ganascia
Identical molecules could play different roles depending of the relations they may have with different partners embedded in different processes, at... Sample PDF
Seekbio: Retrieval of Spatial Relations for System Biology
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Chapter 20
Jon Patrick, Pooyan Asgari
There have been few studies of large corpora of narrative notes collected from the health clinicians working at the point of care. This chapter... Sample PDF
Analysing Clinical Notes for Translation Research: Back to the Future
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About the Contributors