Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence

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.