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Gertjan Beheydt

dblp:82/8709 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 2

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
Medical and health informatics · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics
clinical informatics
0.212013
RegaDB: community-driven data management and analysis for infectious diseases · Bioinform. 2013

Methods — techniques the papers use, named apart from their topics

web-service-oriented architecture · 0.2
YearPublicationVenuePosition
2013 RegaDB: community-driven data management and analysis for infectious diseases
abstract
SUMMARY: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface. AVAILABILITY AND IMPLEMENTATION: Source code, binaries and documentation are available on http://rega.kuleuven.be/cev/regadb. RegaDB is written in the Java programming language, using a web-service-oriented architecture.
Pieter Libin, Gertjan Beheydt, Koen Deforche, Stijn Imbrechts, Fossie Ferreira, Kristel Van Laethem, Kristof Theys, Ana Patricia Carvalho, Joana Cavaco-Silva, Giuseppe Lapadula, Carlo Torti, Matthias Assel, Stefan Wesner, Joke Snoeck, Jean Ruelle, Annelies De Bel, Patrick Lacor, Paul De Munter, Eric Van Wijngaerden, Maurizio Zazzi, Rolf Kaiser, Ahidjo Ayouba, Martine Peeters, Tulio de Oliveira, Luiz Carlos Júnior Alcântara, Zehava Grossman, Peter M. A. Sloot, Dan Otelea, Simona Paraschiv, Charles A. Boucher, Ricardo Camacho, Anne-Mieke Vandamme
Bioinform.2
2010 Estimating the individualized HIV-1 genetic barrier to resistance using a nelfinavir fitness landscape
abstract
BACKGROUND: Failure on Highly Active Anti-Retroviral Treatment is often accompanied with development of antiviral resistance to one or more drugs included in the treatment. In general, the virus is more likely to develop resistance to drugs with a lower genetic barrier. Previously, we developed a method to reverse engineer, from clinical sequence data, a fitness landscape experienced by HIV-1 under nelfinavir (NFV) treatment. By simulation of evolution over this landscape, the individualized genetic barrier to NFV resistance may be estimated for an isolate. RESULTS: We investigated the association of estimated genetic barrier with risk of development of NFV resistance at virological failure, in 201 patients that were predicted fully susceptible to NFV at baseline, and found that a higher estimated genetic barrier was indeed associated with lower odds for development of resistance at failure (OR 0.62 (0.45 - 0.94), per additional mutation needed, p = .02). CONCLUSIONS: Thus, variation in individualized genetic barrier to NFV resistance may impact effective treatment options available after treatment failure. If similar results apply for other drugs, then estimated genetic barrier may be a new clinical tool for choice of treatment regimen, which allows consideration of available treatment options after virological failure.
Kristof Theys, Koen Deforche, Gertjan Beheydt, Yves Moreau, Kristel Van Laethem, Philippe Lemey, Ricardo Camacho, Soo-Yon Rhee, Robert W. Shafer, Eric Van Wijngaerden, Anne-Mieke Vandamme
BMC Bioinform.3