Pascal Iversen
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View article: From Hype to Health Check: Critical Evaluation of Drug Response Prediction Models with DrEval
From Hype to Health Check: Critical Evaluation of Drug Response Prediction Models with DrEval Open
Motivation Large-scale drug sensitivity screens have enabled training drug response prediction models based on cancer cell line omics profiles to facilitate personalized medicine. While model performances reported in the literature appear …
View article: Selecting Synthetic Data for Successful Simulation-Based Transfer Learning in Dynamical Biological Systems
Selecting Synthetic Data for Successful Simulation-Based Transfer Learning in Dynamical Biological Systems Open
Background Accurate prediction of the temporal dynamics of biological systems is crucial for informing timely and effective interventions, e.g., in ecological or epidemiological contexts, or for treatment adjustments in therapy. While mach…
View article: Identifying Drivers of Predictive Aleatoric Uncertainty
Identifying Drivers of Predictive Aleatoric Uncertainty Open
Explainability and uncertainty quantification are key to trustable artificial intelligence. However, the reasoning behind uncertainty estimates is generally left unexplained. Identifying the drivers of uncertainty complements explanations …
View article: SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented Data
SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented Data Open
Training sophisticated machine learning (ML) models requires large datasets that are difficult or expensive to collect for many applications. If prior knowledge about system dynamics is available, mechanistic representations can be used to…
View article: Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction
Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction Open
Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. How…
View article: Matching anticancer compounds and tumor cell lines by neural networks with ranking loss
Matching anticancer compounds and tumor cell lines by neural networks with ranking loss Open
Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of…