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Lea Eckhart

Showing results (1-10 of 10) with videos related to

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Briefings in Bioinformatics|May 27, 2024
A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity predictionLea Eckhart, Kerstin Lenhof, Lisa-Marie Rolli, et al.
Briefings in Bioinformatics|August 5, 2024
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancerKerstin Lenhof, Lea Eckhart, Lisa-Marie Rolli, et al.
Scientific Reports|August 5, 2022
Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest methodKerstin Lenhof, Lea Eckhart, Nico Gerstner, et al.
Scientific Reports|May 29, 2024
Reliable anti-cancer drug sensitivity prediction and prioritizationKerstin Lenhof, Lea Eckhart, Lisa-Marie Rolli, et al.
Iscience|June 12, 2025
How to predict effective drug combinations - moving beyond synergy scoresLea Eckhart, Kerstin Lenhof, Lutz Herrmann, et al.
Bioinformatics (Oxford, England)|August 5, 2021
MERIDA: a novel Boolean logic-based integer linear program for personalized cancer therapyKerstin Lenhof, Nico Gerstner, Tim Kehl, et al.
Frontiers in Molecular Biosciences|October 4, 2021
GeneTrail: A Framework for the Analysis of High-Throughput ProfilesNico Gerstner, Tim Kehl, Kerstin Lenhof, et al.
Journal of Cellular and Molecular Medicine|February 10, 2025
Machine Learning Accurately Predicts Muscle Invasion of Bladder Cancer Based on Three miRNAsLea Eckhart, Sabrina Rau, Markus Eckstein, et al.
Nucleic Acids Research|May 8, 2020
GeneTrail 3: advanced high-throughput enrichment analysisNico Gerstner, Tim Kehl, Kerstin Lenhof, et al.
Scientific Reports|September 27, 2024
The impact of the tumor microenvironment on the survival of penile cancer patientsStefan Lohse, Jan Niklas Mink, Lea Eckhart, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Briefings in Bioinformatics|May 27, 2024
A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity predictionLea Eckhart, Kerstin Lenhof, Lisa-Marie Rolli, et al.
Briefings in Bioinformatics|August 5, 2024
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancerKerstin Lenhof, Lea Eckhart, Lisa-Marie Rolli, et al.
Scientific Reports|August 5, 2022
Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest methodKerstin Lenhof, Lea Eckhart, Nico Gerstner, et al.
Scientific Reports|May 29, 2024
Reliable anti-cancer drug sensitivity prediction and prioritizationKerstin Lenhof, Lea Eckhart, Lisa-Marie Rolli, et al.
Iscience|June 12, 2025
How to predict effective drug combinations - moving beyond synergy scoresLea Eckhart, Kerstin Lenhof, Lutz Herrmann, et al.
Bioinformatics (Oxford, England)|August 5, 2021
MERIDA: a novel Boolean logic-based integer linear program for personalized cancer therapyKerstin Lenhof, Nico Gerstner, Tim Kehl, et al.
Frontiers in Molecular Biosciences|October 4, 2021
GeneTrail: A Framework for the Analysis of High-Throughput ProfilesNico Gerstner, Tim Kehl, Kerstin Lenhof, et al.
Journal of Cellular and Molecular Medicine|February 10, 2025
Machine Learning Accurately Predicts Muscle Invasion of Bladder Cancer Based on Three miRNAsLea Eckhart, Sabrina Rau, Markus Eckstein, et al.
Nucleic Acids Research|May 8, 2020
GeneTrail 3: advanced high-throughput enrichment analysisNico Gerstner, Tim Kehl, Kerstin Lenhof, et al.
Scientific Reports|September 27, 2024
The impact of the tumor microenvironment on the survival of penile cancer patientsStefan Lohse, Jan Niklas Mink, Lea Eckhart, et al.
Pageof 1