Roman Eisner

According to our database1, Roman Eisner authored at least 16 papers between 2004 and 2016.

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Bibliography

2016
ClassyFire: automated chemical classification with a comprehensive, computable taxonomy.
J. Cheminformatics, 2016

2014
Accurate, fully-automated NMR spectral profiling for metabolomics.
CoRR, 2014

2013
HMDB 3.0 - The Human Metabolome Database in 2013.
Nucleic Acids Res., 2013

Phenol-Explorer 3.0: a major update of the Phenol-Explorer database to incorporate data on the effects of food processing on polyphenol content.
Database J. Biol. Databases Curation, 2013

2012
Phenol-Explorer 2.0: a major update of the Phenol-Explorer database integrating data on polyphenol metabolism and pharmacokinetics in humans and experimental animals.
Database J. Biol. Databases Curation, 2012

2011
DrugBank 3.0: a comprehensive resource for 'Omics' research on drugs.
Nucleic Acids Res., 2011

2010
Phenol-Explorer: an online comprehensive database on polyphenol contents in foods.
Database J. Biol. Databases Curation, 2010

2009
HMDB: a knowledgebase for the human metabolome.
Nucleic Acids Res., 2009

2007
HMDB: the Human Metabolome Database.
Nucleic Acids Res., 2007

Biospider: A Web Server for Automating Metabolome Annotations.
Proceedings of the Biocomputing 2007, 2007

2006
Visual Explanation of Evidence with Additive Classifiers.
Proceedings of the Proceedings, 2006

2005
PA-GOSUB: a searchable database of model organism protein sequences with their predicted Gene Ontology molecular function and subcellular localization.
Nucleic Acids Res., 2005

Improving Protein Function Prediction Using the Hierarchical Structure of the Gene Ontology.
Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2005

The Proteome Analyst Suite of Automated Function Prediction Tools.
Proceedings of the Proceedings, 2005

2004
Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations.
Nucleic Acids Res., 2004

Predicting subcellular localization of proteins using machine-learned classifiers.
Bioinform., 2004


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