Optimal Design of Experiments by Combining Coarse and Fine Measurements.
By Alpha Albert Lee, Michael P. Brenner, Lucy J. Colwell. 2017
4 citations recorded by the source corpus.
In many contexts, it is extremely costly to perform enough high-quality experimental measurements to accurately parametrize a predictive quantitative model. However, it is often much easier to carry out large numbers of experiments that indicate whether each sample is above or below a given threshold. Can many such categorical or "coarse" measurements be combined with a much smaller number of high-resolution or "fine" measurements to yield accurate models? Here, we demonstrate an intuitive strategy, inspired by statistical physics, wherein the coarse measurements are used to identify the salient features of the data, while the fine measurements determine the relative importance of these features. A linear model is inferred from the fine measurements, augmented by a quadratic term that captures the correlation structure of the coarse data. We illustrate our strategy by considering the problems of predicting the antimalarial potency and aqueous solubility of small organic molecules from their 2D molecular structure.
Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability
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In Computational Theory and Mathematics
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Citations in the corpus
- Ligand biological activity predicted by cleaning positive and negative chemical correlations (2019, background): “The question of discriminating signal from noise due to the under-sampling in the context of molecular fingerprints has been discussed in the literature (27, 29, 30).”