Identification of Sequence Variants of Genes from Biomedical Literature: The OSIRIS Approach

Identification of Sequence Variants of Genes from Biomedical Literature: The OSIRIS Approach

Laura I. Furlong (Universitat Pompeu Fabra, Spain) and Ferran Sanz (Universitat Pompeu Fabra, Spain)
DOI: 10.4018/978-1-60566-274-9.ch015
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SNPs constitute key elements in genetic epidemiology and pharmacogenomics. While data about genetic variation is found at sequence databases, functional and phenotypic information on consequences of the variations resides in literature. Literature mining is mainly hampered by the terminology problem. Thus, automatic systems for the identification of citations of allelic variants of genes in biomedical texts are required. We have reported the development of OSIRIS, aimed at retrieving literature about allelic variants of genes, a system that evolved towards a new version incorporating a new entity recognition module. The new version is based on a terminology of variations and a pattern-based search algorithm for the identification of variation terms and their disambiguation to dbSNP identifiers. OSIRISv1.2 can be used to link literature references to dbSNP database entries with high accuracy, and is suitable for collecting current knowledge on gene sequence variations for supporting the functional annotation of variation databases.
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In the last years the focus of biological research has shifted from individual genes and proteins towards the study of entire biological systems. The advent of high-throughput experimentation has led to the generation of large data sets, which is reflected in the constant growth of dedicated repositories such as sequence databases and literature collections. Currently, MEDLINE indexes more than 17 million articles in the biomedical sciences, and it’s increasing at a rate of more than 10% each year (Ananiadou et al., 2006). In this scenario, text mining tools are becoming essential for biomedical researchers to manage the literature collection, and to extract, integrate and exploit the knowledge stored therein. Mining textual data can aid in formulating novel hypothesis by combining information from multiple articles and from biological databases, such as genome sequence databases, microarray expression studies, and protein-protein interaction databases (Jensen et al., 2006) (Ananiadou & McNaught, 2006). These kind of approaches are being applied in different scenarios: the prediction of the function of novel genes, functional annotation of molecules, discovering protein-protein interactions, interpreting microarray experiments and association of genes and phenotypes (for a review see (Ananiadou et al., 2006; Jensen et al., 2006)).

The basis of any text mining system is the proper identification of the entities mentioned in the text, also known as Named Entity Recognition (NER). Genes, proteins, drugs, diseases, tissues and biological functions are examples of entities of interest in the biomedical domain. It has been recognised that naming of these biological entities is inconsistent and imprecise, and in consequence tools that automatically extract the terms that refer to the entities are required to obtain an unambiguous identification of such entities (Park & Kim, 2006). In addition to the identification of a term that refer to, for instance, a protein in a text, it is very advantageous to map this term to its corresponding entry in biological databases. This process, also known as normalization, is very relevant from a biomedical perspective, because it provides the correct biological context to the term identified in the text.

NER has been an intense subject of research in the last years in the biology domain, specially for the identification of terms pertaining to genes and proteins (Jensen et al., 2006). Contrasting, few initiatives have been directed to the task of identification of Single Nucleotide Polymorphisms (SNPs) from the literature. Among other types of small sequence variants, SNPs represent the most frequent type of variation between individuals (0.1% of variation in a diploid genome (Levy et al., 2007)). This observation, in addition to their widespread distribution in the genome and their low mutation rate, have positioned the SNPs as the most used genetic markers. SNPs are currently being used in candidate gene association studies, genome wide association studies and in pharmacogenomics. In this context they represent promising tools for finding the genetic determinants of complex diseases and for explaining the inter individual variability of drug responses.

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Table of Contents
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
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®
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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)
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
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
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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