Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence
Bibliographic record
Abstract
In this study, we identify cortical molecular biomarkers potentially associated with learning in mice using artificial intelligence (AI), inclusive of established and novel feature selection combined with supervised learning technologies. We applied multiple machine learning (ML) algorithms, using public domain ML software, to a public domain dataset, in order to support reproducible findings. We developed technologies tasked with predicting whether a given mouse was shocked to learn, based on protein expression levels extracted from their cortices. Results indicate that it is possible to predict whether a mouse has been shocked to learn or not based only on the following cortical molecular biomarkers: brain-derived neurotrophic factor (BDNF), NR2A subunit of N-methyl-D-aspartate receptor, B-cell lymphoma 2 (BCL2), histone H3 acetylation at lysine 18 (H3AcK18), protein kinase R-like endoplasmic reticulum kinase (pERK), and superoxide dismutase 1 (SOD1). These results were obtained with a novel redundancy-aware feature selection method. Five out of six protein expression biomarkers (BDNF, NR2A, H3AcK18, pERK, SOD1) identified have previously been associated with aspects of learning in the literature. Three of the proteins (BDNF, NR2A, and BCL2) have previously been associated with pruning, and one has previously been associated with apoptosis (BCL2), implying a potential connection between learning and both cortical pruning and apoptosis. The results imply that these six protein expression profiles (BDNF, NR2A, BCL2, H3AcK18, pERK, SOD1) are highly predictive of whether or not a mouse has been shocked to learn.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".