T
Thomas Abeel
Researcher at Delft University of Technology
Publications - 96
Citations - 10940
Thomas Abeel is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Genome & Gene. The author has an hindex of 27, co-authored 81 publications receiving 8039 citations. Previous affiliations of Thomas Abeel include Ghent University & Flanders Institute for Biotechnology.
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Java-ML: A Machine Learning Library
TL;DR: Java-ML is a collection of machine learning and data mining algorithms, which aims to be a readily usable and easily extensible API for both software developers and research scientists.
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Comparative analysis of Mycobacterium and related Actinomycetes yields insight into the evolution of Mycobacterium tuberculosis pathogenesis
Abigail Manson McGuire,Brian Weiner,Sang Tae Park,Ilan Wapinski,Ilan Wapinski,Sahadevan Raman,Gregory Dolganov,Matthew W. Peterson,Robert Riley,Jeremy Zucker,Thomas Abeel,Thomas Abeel,Jared White,Peter Sisk,Christian Stolte,Mike Koehrsen,Robert T Yamamoto,Milena Iacobelli-Martinez,Matthew J Kidd,Andreia M Maer,Gary K. Schoolnik,Aviv Regev,James E. Galagan,James E. Galagan +23 more
TL;DR: A comparative analysis of protein evolution highlights gene families that are associated with the adaptation of environmental Mycobacteria to obligate pathogenesis, including fatty acid metabolism, DNA repair, and molybdopterin biosynthesis and reinforces recent findings suggesting that small noncoding RNAs are more common in Myc Cobacteria than previously expected.
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Evolution of invasion in a diverse set of Fusobacterium species.
Abigail Manson McGuire,Kyla Cochrane,Allison D. Griggs,Brian J. Haas,Thomas Abeel,Thomas Abeel,Qiandong Zeng,Justin B. Nice,Hanlon MacDonald,Bruce W. Birren,Bryan W. Berger,Emma Allen-Vercoe,Ashlee M. Earl +12 more
TL;DR: In the largest and most comprehensive comparison of sequenced Fusobacterium species to date, this study generates a testable model for the molecular pathogenesis of FusOBacterium infection and illuminate new therapeutic or diagnostic strategies.
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ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles
TL;DR: It is shown that unsupervised clustering by using self-organizing maps can clearly distinguish between the structural profiles of promoter sequences and other genomic sequences, and an implementation of this promoter prediction program, called ProSOM, is available and has been compared with the state of the art.
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Toward a gold standard for promoter prediction evaluation
TL;DR: A multi-faceted evaluation strategy is proposed that can be used as a gold standard for promoter prediction evaluation, allowing authors of promoter prediction software to compare their method to existing methods in a proper way.