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Iliana Avila-Campillo

dblp:55/5904 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › network bioinformatics › biological network analysis › network integration
biological network integration
0.112007
BioNetBuilder: automatic integration of biological networks · Bioinform. 2007
Bioinformatics and computational biology › data integration
database integration
0.112007
BioNetBuilder: automatic integration of biological networks · Bioinform. 2007
YearPublicationVenuePosition
2007 BioNetBuilder: automatic integration of biological networks
abstract
UNLABELLED: BioNetBuilder is an open-source client-server Cytoscape plugin that offers a user-friendly interface to create biological networks integrated from several databases. Users can create networks for approximately 1500 organisms, including common model organisms and human. Currently supported databases include: DIP, BIND, Prolinks, KEGG, HPRD, The BioGrid and GO, among others. The BioNetBuilder plugin client is available as a Java Webstart, providing a platform-independent network interface to these public databases. AVAILABILITY: http://err.bio.nyu.edu/cytoscape/bionetbuilder/
Iliana Avila-Campillo, Kevin Drew, John Lin, David J. Reiss, Richard Bonneau
Bioinform.1
2005 Tools enabling the elucidation of molecular pathways active in human disease: Application to Hepatitis C virus infection
abstract
BACKGROUND: The extraction of biological knowledge from genome-scale data sets requires its analysis in the context of additional biological information. The importance of integrating experimental data sets with molecular interaction networks has been recognized and applied to the study of model organisms, but its systematic application to the study of human disease has lagged behind due to the lack of tools for performing such integration. RESULTS: We have developed techniques and software tools for simplifying and streamlining the process of integration of diverse experimental data types in molecular networks, as well as for the analysis of these networks. We applied these techniques to extract, from genomic expression data from Hepatitis C virus-infected liver tissue, potentially useful hypotheses related to the onset of this disease. Our integration of the expression data with large-scale molecular interaction networks and subsequent analyses identified molecular pathways that appear to be induced or repressed in the response to Hepatitis C viral infection. CONCLUSION: The methods and tools we have implemented allow for the efficient dynamic integration and analysis of diverse data in a major human disease system. This integrated data set in turn enabled simple analyses to yield hypotheses related to the response to Hepatitis C viral infection.
David J. Reiss, Iliana Avila-Campillo, Vesteinn Thorsson, Benno Schwikowski, Timothy Galitski
BMC Bioinform.2