Employing batch reinforcement learning to control gene regulation without explicitly constructing gene regulatory networks
Bibliographic record
Abstract
The goal of controlling a gene regulatory network (GRN) is to generate an intervention strategy, i.e., a control policy, such that by applying the policy the system will avoid undesirable states. In this work, we propose a method to control GRNs by using Batch Mode Reinforcement Learning (Batch RL). Our idea is based on the fact that time series gene expression data can actually be interpreted as a sequence of experience tuples collected from the environment. Existing studies on this control task try to infer a model using gene expression data and then calculate a control policy over the constructed model. However, we propose a method that can directly use the available gene expression data to obtain an approximated control policy for gene regulation that avoids the time consuming model building phase. Results show that we can obtain policies for gene regulation systems of several thousands of genes just in several seconds while existing solutions get stuck for even tens of genes. Interestingly, the reported results also show that our method produces policies that are almost as good as the ones generated by existing model dependent methods. 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".