Noise-precision tradeoff in predicting combinations of mutations and drugs

Many biological problems involve the response to multiple perturbations. Examples include response to combinations of many drugs, and the effects of combinations of many mutations. Such problems have an exponentially large space of combinations, which makes it infeasible to cover the entire space ex...

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Bibliographic Details
Published in:PLoS computational biology 2019-05, Vol.15 (5), p.e1006956-e1006956
Main Authors: Tendler, Avichai, Zimmer, Anat, Mayo, Avi, Alon, Uri
Format: Article
Language:English
Subjects:
Algorithms
Antibiotics
Artificial intelligence
Bias
Biology
Biology and Life Sciences
Combination drug therapy
Combinatorial analysis
Computer and Information Sciences
Datasets
Deoxyribonucleic acid
DNA
Drug dosages
Drug resistance
Drug Therapy, Combination - statistics & numerical data
Drugs
Evolution
Explosions
Fitness
Forecasting - methods
Genetic aspects
Health aspects
Machine learning
Mathematical models
Medicine and Health Sciences
Models, Biological
Mutation
Noise
Noise prediction
Optimization
Pareto efficiency
Pareto optimum
Physical Sciences
Proteins
Reproductive fitness
Research and Analysis Methods
Signal-To-Noise Ratio
Tradeoffs
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Summary:Many biological problems involve the response to multiple perturbations. Examples include response to combinations of many drugs, and the effects of combinations of many mutations. Such problems have an exponentially large space of combinations, which makes it infeasible to cover the entire space experimentally. To overcome this problem, several formulae that predict the effect of drug combinations or fitness landscape values have been proposed. These formulae use the effects of single perturbations and pairs of perturbations to predict triplets and higher order combinations. Interestingly, different formulae perform best on different datasets. Here we use Pareto optimality theory to quantitatively explain why no formula is optimal for all datasets, due to an inherent bias-variance (noise-precision) tradeoff. We calculate the Pareto front of log-linear formulae and find that the optimal formula depends on properties of the dataset: the typical interaction strength and the experimental noise. This study provides an approach to choose a suitable prediction formula for a given dataset, in order to best overcome the combinatorial explosion problem.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1006956