From data to knowledge.
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Artificial Intelligence↗
Biomedical subjects
F Rechenmann
Publications and source records attributed to F Rechenmann.
A pragmatic information extraction strategy for gathering data on genetic interactions.
We present in this paper a pragmatic strategy to perform information extraction from biologic texts. Since the emergence of the information extraction field, techniques have evolved, become more robust and proved their efficiency on specific domains. We are using a combination of existing linguistic and knowledge processing tools to automatically extract information about gene interactions in the literature. Our ultimate goal is to build a network of gene interactions. The methodologies used and the current results are discussed in this paper.
Animals↗
Grasping at molecular interactions and genetic networks in Drosophila melanogaster using FlyNets, an Internet database.
FlyNets (http://gifts.univ-mrs.fr/FlyNets/FlyNets_home_page.++ +html) is a WWW database describing molecular interactions (protein-DNA, protein-RNA and protein-protein) in the fly Drosophila melanogaster. It is composed of two parts, as follows. (i) FlyNets-base is a specialized database which focuses on molecular interactions involved in Drosophila development. The information content of FlyNets-base is distributed among several specific lines arranged according to a GenBank-like format and grouped into five thematic zones to improve human readability. The FlyNets database achieves a high level of integration with other databases such as FlyBase, EMBL, GenBank and SWISS-PROT through numerous hyperlinks. (ii) FlyNets-list is a very simple and more general databank, the long-term goal of which is to report on any published molecular interaction occuring in the fly, giving direct web access to corresponding s in Medline and in FlyBase. In the context of genome projects, databases describing molecular interactions and genetic networks will provide a link at the functional level between the genome, the proteome and the transcriptome worlds of different organisms. Interaction databases therefore aim at describing the contents, structure, function and behaviour of what we herein define as the interactome world.
Animals↗
Imagene: an integrated computer environment for sequence annotation and analysis.
MOTIVATION: To be fully and efficiently exploited, data coming from sequencing projects together with specific sequence analysis tools need to be integrated within reliable data management systems. Systems designed to manage genome data and analysis tend to give a greater importance either to the data storage or to the methodological aspect, but lack a complete integration of both components. RESULTS: This paper presents a co-operative computer environment (called Imagenetrade mark) dedicated to genomic sequence analysis and annotation. Imagene has been developed by using an object-based model. Thanks to this representation, the user can directly manipulate familiar data objects through icons or lists. Imagene also incorporates a solving engine in order to manage analysis tasks. A global task is solved by successive divisions into smaller sub-tasks. During program execution, these sub-tasks are graphically displayed to the user and may be further re-started at any point after task completion. In this sense, Imagene is more transparent to the user than a traditional menu-driven package. Imagene also provides a user interface to display, on the same screen, the results produced by several tasks, together with the capability to annotate these results easily. In its current form, Imagene has been designed particularly for use in microbial sequencing projects. AVAILABILITY: Imagene best runs on SGI (Irix 6.3 or higher) workstations. It is distributed free of charge on a CD-ROM, but requires some Ilog licensed software to run. Some modules also require separate license agreements. Please contact the authors for specific academic conditions and other Unix platforms. CONTACT: imagene home page: http://wwwabi.snv.jussieu.fr/imagene
Bacillus subtilis↗
Detecting Gene Symbols and Names in Biological Texts: A First Step toward Pertinent Information Extraction.
Gathering data on molecular interactions to be fed into a specialized database has motivated the development of a computer system to help extracting pertinent information from texts, relying on advanced linguistic tools, completed with object-oriented knowledge modeling capabilities. As a first step toward this challenging objective, a program for the identification of gene symbols and names inside sentences has been devised. The main difficulty is that these names and symbols do not appear to follow construction rules. The program is thus made up of a series of sieves of different natures, lexical, morphological and semantic, to distinguish among the words of a sentence those which can only be potential gene symbols or names. Its performance has been evaluated, in terms of coverage and precision ratios, on a corpus of texts concerning D. melanogaster for which the list of names of known genes is available for checking.
Journal Article↗
Object-oriented knowledge bases for the analysis of prokaryotic and eukaryotic genomes.
The amount of biological sequences introduced in the general collections, and the growing complexity of the biological knowledge require the construction of models to formalize this knowledge and particularly the relationships between several data types. Two examples of such situations are presented here, they result from the biological research lead in our team in the field of molecular evolution. ColiGene is a modelling of E. coli genetics devoted to the analysis of relationships between genomic sequences and gene expressivity. MultiMap implements a new formalization of genome maps allowing manipulation of "maps of maps" in two species. Application of ColiGene and MultiMap are not restricted to molecular evolution and, for instance, MultiMap offers new capabilities for infering data on a genome from knowledge on another species. This could be essential for many mapping projects (human, mouse but also other mammals like pig). Development and implementation of those models have been done using an object-oriented knowledge base management system (SHIRKA) interfaced with a dedicated genomic data base management system (ACNUC). Graphical interfaces have been designed to give an environment similar to the biological representations used by biologists.
Animals↗
Building large knowledge bases in molecular biology.
Large scale genome sequencing projects are now producing hugh amounts of data which can be readily stored and managed within data base management systems, and analyzed using dedicated software packages. The results of these analyzes should also be stored with the input DNA sequences. The increasing complexity and size of the objects to be described and managed have led biologists to rely on advanced data models such as the object-oriented model. As a joint effort between our computer science and molecular biology research projects, the knowledge bases we have developed in molecular genetics have shown however that the basic object-oriented model is not fully adapted to the complexity of some biological situations encountered. Advanced descriptive capabilities, provided only by knowledge models originated from the AI field, are required. Composite or evolving objects, multiple viewpoints, constraints, tasks and methods, textual annotations are some examples of such capabilities. They are illustrated by biological situations for which they appeared to be necessary. Supporting powerful reasoning mechanisms (e.g. object classification, constraint propagation or qualitative simulators), they allow the development of large knowledge bases in molecular biology. These knowledge bases are expected to become the adequate support for co-operative distributed research efforts.
Artificial Intelligence↗