An architecture for biological information extraction and representation

By A. Vailaya, P. Bluvas, R. Kincaid, Allan Kuchinsky, Michael L. Creech, Annette Adler. ACM Symposium on Applied Computing, 2004

378 citations recorded by the source corpus.

Technological advances in biomedical research are generating a plethora of heterogeneous data at a high rate. There is a critical need for extraction, integration and management tools for information discovery and synthesis from these heterogeneous data. In this paper, we present a general architecture, called ALFA, for information extraction and representation from diverse biological data. The ALFA architecture consists of: (i) a networked, hierarchical object model for representing information from heterogeneous data sources in a standardized, structured format; and (ii) a suite of integrated, interactive software tools for information extraction and representation from diverse biological data sources. As part of our research efforts to explore this space, we have currently prototyped the ALFA object model and a set of interactive software tools for searching, filtering, and extracting information from scientific text. In particular, we describe BioFerret, a meta-search tool for searching and filtering relevant information from the web, and ALFA Text Viewer, an interactive tool for user-guided extraction, disambiguation, and representation of information from scientific text. We further demonstrate the potential of our tools in integrating the extracted information with experimental data and diagrammatic biological models via the common underlying ALFA representation.

Subfields: Artificial Intelligence, Information Systems and Management, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f93a372ee266a1417f5fdd07a1aef6ce9b91e810

How researchers see this paper

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In Artificial Intelligence

1. Douglas G. Altman
2. Marcela G. Cuellar

In Molecular Biology

1. Douglas G. Altman
2. A. Ramasamy
3. Adrian Mondry
4. Chris Holmes
5. Johan Rung
6. Alvis Brazma

Citations in the corpus

• Key Issues in Conducting a Meta-Analysis of Gene Expression Microarray Datasets (2008, methodology): “…be used to identify genes expressed differentially between two groups [19,22,29,3 0,32,33,35,37,38,40], to robustify cross-platform classification [34], to identify overlaps between samples from heterologous datasets [30], to identify co-expressed genes, or to reconstruct gene networks [31,36,39].”
• BIOLOGICAL NETWORK EXPLORATION WITH CYTOSCAPE 3 (2014, neutral): “…above, there are many other Cytoscape 3 apps that enables data importing from data repositories such as Reactome (Joshi-Tope and Gillespie, 2005), KEGG (Kanehisa, 2002), WikiPathways (Kelder et al. , 2012), MetScape (Karnovsky et al. , 2012), Agilent Literature Search (Vailaya et al. , 2005).”
• A travel guide to Cytoscape plugins (2012, neutral): “The chief means in Cytoscape (v.2.5 or later) of building networks from the literature is AgilentLiteratureSearch 17 , a plugin that mines literature abstracts from sources such as Medline, Online Mendelian Inheritance in Man (OMIM) 18 and the US patent database to identify putative interactions and”