A comprehensive comparison of molecular feature representations for use in predictive modeling. In: Computers in Biology and Medicine, 130 , pp. 104197, 2021, ISSN: 0010-4825.
machine learning
Balancing Utility and Fairness against Privacy in Medical Data
Balancing Utility and Fairness against Privacy in Medical Data. In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1226-1233, IEEE, 2020.
Feature Engineering for a Seismic Loss Prediction Model using Machine Learning, Christchurch Experience
Feature Engineering for a Seismic Loss Prediction Model using Machine Learning, Christchurch Experience. In: 17th World Conference on Earthquake Engineering, 2020.
Your Best Guess When You Know Nothing: Identification and Mitigation of Selection Bias
Your Best Guess When You Know Nothing: Identification and Mitigation of Selection Bias. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 996-1001, IEEE, 2020, ISSN: 2374-8486.
A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake
A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake. In: Earthquake Spectra, 36 (2), pp. 314-339, 2020.
A machine learning approach to quantifying the specificity of color-emotion associations and their cultural differences
A machine learning approach to quantifying the specificity of color-emotion associations and their cultural differences. In: Royal Society Open Science, 6 (9), pp. 190741, 2019.
What can we learn from the air chemistry of crowds
What can we learn from the air chemistry of crowds?. In: Hansel, Armin; Dunkl, Jürgen (Ed.): 8th International Conference on Proton Transfer Reaction Mass Spectrometry and its Applications, pp. 121-123, Innsbruck University Press, Innsbruck, 2019.
The Best Privacy Defense is a Good Privacy Offense: Obfuscating a Search Engine User’s Profile
The Best Privacy Defense is a Good Privacy Offense: Obfuscating a Search Engine User's Profile. In: Data Mining and Knowledge Discovery, 31 (5), pp. 1419-1443, 2017, ISSN: 1573-756X.
Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data
Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data. In: Environmental Science: Process & Impact, 2017.
Large Classifier Systems in Bio- and Cheminformatics
Large Classifier Systems in Bio- and Cheminformatics. Technische Universität München, 2013.