ArticlePLoS computational biology2022
Deep reinforcement learning for optimal experimental design in biology.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Smart Optogenetics for Real-Time Automated Control of Cardiac Electrical Activity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Leveraging artificial intelligence for efficient microbial production.Bioresource technology · 2026Review
- Optimization of experimental designs for biological rhythm discovery.PLoS computational biology · 2025Article
- A Reinforcement Learning approach to study climbing plant behaviour.Scientific reports · 2024Article
- Predicting potential target genes in molecular biology experiments using machine learning and multifaceted data sources.iScience · 2024Article
- Deep model predictive control of gene expression in thousands of single cells.Nature communications · 2024Article
- Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.ACS synthetic biology · 2023Article
- Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization.Materials (Basel, Switzerland) · 2023Article
- Effective design and inference for cell sorting and sequencing based massively parallel reporter assays.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence-reinforcement learning-to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.