Seekbio: Retrieval of Spatial Relations for System Biology

Seekbio: Retrieval of Spatial Relations for System Biology

Christophe Jouis (Université Paris III and LIP6, France), Magali Roux-Rouquié (LIP6, France) and Jean-Gabriel Ganascia (LIP6, France)
DOI: 10.4018/978-1-60566-274-9.ch019
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Abstract

Identical molecules could play different roles depending of the relations they may have with different partners embedded in different processes, at different time and/or localization. To address such intricate networks that account for the complexity of living systems, systems biology is an emerging field that aims at understanding such dynamic interactions from the knowledge of their components and the relations between these components. Among main issues in system biology, knowledge on entities spatial relations is of importance to assess the topology of biological networks. In this perspective, mining data and texts could afford specific clues. To address this issue we examine the use of contextual exploration method to develop extraction rules that can retrieve information on relations between biological entities in scientific literature. We propose the system Seekbio that could be plugged at Pubmed output as an interface between results of PubMed query and articles selection following spatial relationships requests.
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Introduction

For decades, it was thought that more an organism was complex, more the number of genes they contain had to be high. At the completion of the Human Genome Project, it was found that living systems were having approximately the same number of genes coding for proteins (about 20,000) and these assessments resulted in biology’s big bang undergoing a paradigmatic change to address biological complexity. Accordingly, the universe of biologists was changing and the challenge of unravelling complexity of living organisms was stipulated on the spatio-temporal variety of the biological components and their relations. Countless examples show that identical molecules could play different roles depending of the relations they may have with different partners embedded in different processes, at different time and/or localization; let mention, muscle differentiation that is orchestrated by the differential localization of a molecular species (Misca et al., 2001) or molecular gradients that regulate the developing eye polarity (Strutt, 1999). To address such intricate networks that account for the complexity of living systems, systems biology is an emerging field that aims at understanding such dynamic interactions from the knowledge of their components and the relations between these components. Among main issues in systems biology, knowledge on entities spatial relations is of importance to assess the topology of biological networks; in this perspective, mining data and texts could afford specific clues. To address this issue, we examine the use of contextual exploration method (Jouis, 1993; Jouis, 2007) to develop extraction rules that can retrieve information on relations between biological entities in scientific literature. To achieve this, we propose SEEKbio tool. Among further uses, SEEKbio could be plugged at Pubmed (Srinivasan, P., 2001) output as an interface between results of PubMed query and articles selection following spatial relationships requests. These spatial relationships requests would consist of terms graph of the original query and location relations put by the user in the SEEKbio graphs editor. The built graph would act as a filter to select relevant articles from a spatial point of view (see Figure 1). Enabling this strategy should return selected documents in which relations arguments could be added automatically.

Figure 1.

Utilisation of SEEKbio for collecting all relevant information through Pubmed applied to Medline. As a first step, the user makes a query in the form of a Boolean expression of terms. In step (2), the request is submitted to Pubmed via Medline database. Quite often, the user gets out of a hundred references. This result is not exploitable. So, in a third step, the user specifies his request in the form of a graph in the system SEEKbio. This graph represents a hierarchy of spatial terms of the original request. The graph serves as a filter. With the contextual exploration rules, SEEKbio detects texts in which appear the spatial relationships expressed by the graph. Finally, in Step 4, we get a few abstracts which verify the conditions expressed by the spatial hierarchy.

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Two different approaches to extracting relationships from biological texts are used (Ananiadou, S., Kell, D. B. & J. Tsujii., 2006).

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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