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Approximate Bayesian Computation
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of centralExpand
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Multifaceted Activities of Type I Interferon Are Revealed by a Receptor Antagonist
A variant of type I interferon stimulates expression of only those genes required for an antiviral response. Building a Better Interferon Type I interferons stimulate an antiviral response duringExpand
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Unsupervised modeling of cell morphology dynamics for time-lapse microscopy
Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper introduces an unsupervisedExpand
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Modelling and Optimizing the Process of Learning Mathematics
This paper introduces a computer-based training program for enhancing numerical cognition aimed at children with developmental dyscalculia. Through modelling cognitive processes and controlling theExpand
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Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome
Cells react to nutritional cues in changing environments via the integrated action of signaling, transcriptional, and metabolic networks. Mechanistic insight into signaling processes is oftenExpand
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Optimized expected information gain for nonlinear dynamical systems
This paper addresses the problem of active model selection for nonlinear dynamical systems. We propose a novel learning approach that selects the most informative subset of time-dependent variablesExpand
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Cluster-Based Prediction of Mathematical Learning Patterns
This paper introduces a method to predict and analyse students’ mathematical performance by detecting distinguishable subgroups of children who share similar learning patterns. We employ pairwiseExpand
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Modelling and Optimizing Mathematics Learning in Children
This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancingExpand
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Automatic Generation of Predictive Dynamic Models Reveals Nuclear Phosphorylation as the Key Msn2 Control Mechanism
Topological filtering identifies biological networks compatible with known data and enables quantitative analysis of regulatory mechanisms. Reducing the Options Quantitative analysis of signalingExpand
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Near-optimal experimental design for model selection in systems biology
Motivation: Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces anExpand
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