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407 results on '"Lisacek, Frédérique"'

15. Glycomics@ExPASy: Bridging the Gap

Author

Mariethoz, Julien, Alocci, Davide, Gastaldello, Alessandra, Horlacher, Oliver, Gasteiger, Elisabeth, Rojas-Macias, Miguel, Karlsson, Niclas G., Packer, Nicolle H., and Lisacek, Frédérique

Published
2018
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17. pROC: an open-source package for R and S+ to analyze and compare ROC curves

Author

Robin, Xavier, Turck, Natacha, Hainard, Alexandre, Tiberti, Natalia, Lisacek, Frédérique, Sanchez, Jean-Charles, and Müller, Markus

Subjects
Networking and Information Technology R&D (NITRD), Biomarkers, Computational Biology, Confidence Intervals, Data Interpretation, Statistical, Humans, Programming Languages, ROC Curve, Software, Biomedical Imaging, Cancer, Clinical Research, Breast Cancer, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Published
2011

19. SugarBindDB

Author

Mariethoz, Julien, Khatib, Khaled, Mannic, Tiphaine, Alocci, Davide, Campbell, Matthew P., Packer, Nicolle H., Mullen, Elaine H., Lisacek, Frederique, and Aoki-Kinoshita, Kiyoko F., editor

Published
2017
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31. Data Integration in Proteomics

Author

Lisacek, Frédérique, Hoogland, Christine, Bougueleret, Lydie, Bairoch, Amos, Wilkins, Marc R., editor, Appel, Ron D., editor, Williams, Keith L., editor, and Hochstrasser, Denis F., editor

Published
2007
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45. GlycoBioinformatics

Author

Aoki-Kinoshita, Kiyoko F, primary, Lisacek, Frédérique, additional, Karlsson, Niclas, additional, Kolarich, Daniel, additional, and Packer, Nicolle H, additional

Published
2021
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49. Fifteen years SIB Swiss Institute of Bioinformatics: life science databases, tools and support

Author

Stockinger, Heinz, Altenhoff, Adrian M., Arnold, Konstantin, Bairoch, Amos, Bastian, Frederic, Bergmann, Sven, Bougueleret, Lydie, Bucher, Philipp, Delorenzi, Mauro, Lane, Lydie, Le Mercier, Philippe, Lisacek, Frédérique, Michielin, Olivier, Palagi, Patricia M., Rougemont, Jacques, Schwede, Torsten, von Mering, Christian, van Nimwegen, Erik, Walther, Daniel, Xenarios, Ioannis, Zavolan, Mihaela, Zdobnov, Evgeny M., Zoete, Vincent, and Appel, Ron D.

Published
2014
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