Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence
Abstract
1. Introduction
1.1. Closely Related Work
1.2. Hypothesis
2. Results
2.1. Predicting Whether a Mouse Was Shocked to Learn
2.2. Results of the Alternative Approach to Detecting Potentially Learning-Linked Proteins
2.3. Visualization of Findings
3. Discussion
3.1. Protein Expression Potential Significance
3.2. Potential Implications: Causality and Correlation
3.3. Discussion of Alternative Analysis
3.4. Literature Comparison
3.5. Machine Learning and Feature Selection
3.6. Strengths, Limitations, and Future Work
4. Materials and Methods
4.1. Dataset Description
4.2. Machine Learning
4.3. Statistical Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
acc | accuracy |
AI | artificial intelligence |
assoc | filter-based association FS |
auroc | area under the receiver operating characteristic curve |
bal-acc | balanced accuracy |
BCL2 | B-cell lymphoma 2 |
BDNF | brain-derived neurotrophic factor |
DS | Down syndrome |
embed_lgbm | embedded lgbm FS |
embed_linear | embedded linear FS |
f1 | F1 score |
FS | feature selection |
H3AcK18 | histone H3 acetylation at lysine 18 |
KNN | k-nearest neighbor |
lgbm | Light Gradient-Boosting Machine |
lr | logistic regression |
ML | Machine Learning |
NDMA | N-methyl-D-aspartate receptor |
npv | Negative Predictive Value |
NR2A | the subunit of the NDMA receptor |
pERK | protein kinase R-like endoplasmic reticulum kinase |
ppv | positive predictive value |
pred | filter-based prediction FS |
rf | random forest |
S2L | shocked to learn |
sens | sensitivity |
sgd | stochastic gradient descent |
SOD1 | superoxide dismutase 1 |
spec | specificity |
SOM | self-organizing map |
wrap | wrapper-based redundancy aware FS |
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Model | Selection | Embed Selector | Acc | AUROC | Bal-Acc | F1 | NPV | PPV | Sens | Spec |
---|---|---|---|---|---|---|---|---|---|---|
lr | wrap | none | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
sgd | wrap | none | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
lr | embed_lgbm | lgbm | 0.993 | 1.000 | 0.993 | 0.993 | 1.000 | 0.984 | 0.993 | 0.987 |
sgd | embed_lgbm | lgbm | 0.992 | 0.996 | 0.993 | 0.992 | 1.000 | 0.984 | 0.993 | 0.987 |
lgbm | embed_linear | linear | 0.983 | 1.000 | 0.983 | 0.983 | 0.980 | 0.988 | 0.983 | 0.982 |
lgbm | assoc | none | 0.981 | 1.000 | 0.981 | 0.981 | 0.980 | 0.984 | 0.981 | 0.979 |
sgd | pred | none | 0.981 | 1.000 | 0.983 | 0.981 | 0.964 | 1.000 | 0.983 | 1.000 |
lr | embed_linear | linear | 0.981 | 1.000 | 0.983 | 0.981 | 0.972 | 0.988 | 0.983 | 0.990 |
lgbm | wrap | none | 0.980 | 1.000 | 0.980 | 0.979 | 0.975 | 0.984 | 0.980 | 0.980 |
lgbm | pred | none | 0.980 | 1.000 | 0.979 | 0.979 | 0.980 | 0.981 | 0.979 | 0.976 |
lgbm | none | none | 0.980 | 1.000 | 0.979 | 0.979 | 0.980 | 0.981 | 0.979 | 0.976 |
lgbm | embed_lgbm | lgbm | 0.980 | 1.000 | 0.979 | 0.979 | 0.980 | 0.981 | 0.979 | 0.976 |
sgd | embed_linear | linear | 0.975 | 1.000 | 0.978 | 0.975 | 0.956 | 0.991 | 0.978 | 0.993 |
lr | pred | none | 0.975 | 1.000 | 0.978 | 0.975 | 0.955 | 1.000 | 0.978 | 1.000 |
sgd | assoc | none | 0.975 | 1.000 | 0.978 | 0.975 | 0.961 | 0.988 | 0.978 | 0.990 |
gandalf | embed_lgbm | lgbm | 0.972 | 0.998 | 0.969 | 0.971 | 0.975 | 0.976 | 0.969 | 0.973 |
sgd | none | none | 0.970 | 1.000 | 0.973 | 0.969 | 0.952 | 0.988 | 0.973 | 0.990 |
lr | none | none | 0.964 | 1.000 | 0.968 | 0.964 | 0.941 | 0.988 | 0.968 | 0.990 |
gandalf | embed_linear | linear | 0.958 | 0.996 | 0.953 | 0.955 | 0.944 | 0.982 | 0.953 | 0.990 |
rf | embed_linear | linear | 0.957 | 1.000 | 0.957 | 0.956 | 0.976 | 0.945 | 0.957 | 0.934 |
rf | embed_lgbm | lgbm | 0.957 | 1.000 | 0.957 | 0.956 | 0.973 | 0.948 | 0.957 | 0.937 |
rf | assoc | none | 0.957 | 1.000 | 0.957 | 0.956 | 0.973 | 0.948 | 0.957 | 0.937 |
lr | assoc | none | 0.956 | 1.000 | 0.962 | 0.956 | 0.929 | 0.988 | 0.962 | 0.990 |
rf | none | none | 0.954 | 1.000 | 0.953 | 0.952 | 0.973 | 0.942 | 0.953 | 0.930 |
rf | pred | none | 0.954 | 0.999 | 0.953 | 0.952 | 0.973 | 0.942 | 0.953 | 0.930 |
rf | wrap | none | 0.954 | 0.999 | 0.953 | 0.952 | 0.973 | 0.942 | 0.953 | 0.930 |
knn | embed_lgbm | lgbm | 0.953 | 0.976 | 0.958 | 0.946 | 0.924 | 0.984 | 0.958 | 0.987 |
gandalf | pred | none | 0.948 | 0.998 | 0.943 | 0.927 | 0.933 | 0.979 | 0.943 | 0.969 |
knn | wrap | none | 0.927 | 0.938 | 0.933 | 0.923 | 0.929 | 0.960 | 0.933 | 0.950 |
knn | pred | none | 0.924 | 0.937 | 0.927 | 0.915 | 0.894 | 0.955 | 0.927 | 0.956 |
knn | embed_linear | linear | 0.916 | 0.950 | 0.917 | 0.915 | 0.881 | 0.959 | 0.917 | 0.956 |
knn | none | none | 0.916 | 0.950 | 0.917 | 0.915 | 0.881 | 0.959 | 0.917 | 0.956 |
knn | assoc | none | 0.916 | 0.950 | 0.917 | 0.915 | 0.881 | 0.959 | 0.917 | 0.956 |
gandalf | none | none | 0.889 | 0.992 | 0.884 | 0.883 | 0.842 | 0.984 | 0.884 | 0.978 |
gandalf | wrap | none | 0.869 | 0.990 | 0.879 | 0.860 | 0.861 | 0.964 | 0.879 | 0.967 |
gandalf | assoc | none | 0.830 | 0.954 | 0.834 | 0.826 | 0.788 | 0.924 | 0.834 | 0.921 |
dummy | embed_linear | linear | 0.443 | 0.500 | 0.500 | 0.307 | 0.429 | 0.452 | 0.500 | 0.400 |
dummy | none | none | 0.443 | 0.500 | 0.500 | 0.307 | 0.429 | 0.452 | 0.500 | 0.400 |
dummy | assoc | none | 0.443 | 0.500 | 0.500 | 0.307 | 0.429 | 0.452 | 0.500 | 0.400 |
dummy | embed_lgbm | lgbm | 0.443 | 0.500 | 0.500 | 0.307 | 0.429 | 0.452 | 0.500 | 0.400 |
dummy | pred | none | 0.443 | 0.500 | 0.500 | 0.307 | 0.429 | 0.452 | 0.500 | 0.400 |
dummy | wrap | none | 0.443 | 0.500 | 0.500 | 0.307 | 0.429 | 0.452 | 0.500 | 0.400 |
Feature | Score |
---|---|
SOD1N | 0.944 |
pERKN | 0.994 |
BDNFN_NAN | 0.994 |
NR2AN | 0.964 |
H3AcK18N_NAN | 0.983 |
BCL2N_NAN | 0.961 |
Model | Selection | Embed Selector | Acc | AUROC | Bal-Acc | F1 | NPV | PPV | Sens | Spec |
---|---|---|---|---|---|---|---|---|---|---|
sgd | embed_lgbm | lgbm | 0.877 | 0.893 | 0.876 | 0.873 | 0.850 | 0.917 | 0.876 | 0.891 |
rf | none | none | 0.876 | 0.890 | 0.889 | 0.875 | 0.812 | 0.959 | 0.889 | 0.964 |
rf | pred | none | 0.874 | 0.893 | 0.887 | 0.873 | 0.811 | 0.955 | 0.887 | 0.960 |
rf | embed_linear | linear | 0.874 | 0.893 | 0.887 | 0.873 | 0.811 | 0.955 | 0.887 | 0.960 |
knn | embed_lgbm | lgbm | 0.873 | 0.889 | 0.867 | 0.869 | 0.853 | 0.889 | 0.867 | 0.840 |
rf | assoc | none | 0.872 | 0.875 | 0.883 | 0.871 | 0.811 | 0.952 | 0.883 | 0.953 |
rf | wrap | none | 0.872 | 0.908 | 0.884 | 0.870 | 0.803 | 0.971 | 0.884 | 0.969 |
lr | embed_lgbm | lgbm | 0.860 | 0.917 | 0.847 | 0.853 | 0.872 | 0.863 | 0.847 | 0.796 |
gandalf | none | none | 0.825 | 0.903 | 0.771 | 0.773 | 0.914 | 0.810 | 0.771 | 0.596 |
lgbm | wrap | none | 0.823 | 0.900 | 0.807 | 0.805 | 0.799 | 0.884 | 0.807 | 0.804 |
gandalf | pred | none | 0.819 | 0.918 | 0.792 | 0.799 | 0.888 | 0.801 | 0.792 | 0.660 |
gandalf | embed_lgbm | lgbm | 0.817 | 0.926 | 0.782 | 0.765 | 0.913 | 0.814 | 0.782 | 0.618 |
lgbm | embed_lgbm | lgbm | 0.815 | 0.920 | 0.797 | 0.799 | 0.824 | 0.858 | 0.797 | 0.758 |
rf | embed_lgbm | lgbm | 0.809 | 0.859 | 0.797 | 0.797 | 0.815 | 0.852 | 0.797 | 0.764 |
lr | none | none | 0.808 | 0.894 | 0.794 | 0.798 | 0.778 | 0.850 | 0.794 | 0.762 |
lr | embed_linear | linear | 0.806 | 0.891 | 0.792 | 0.796 | 0.778 | 0.847 | 0.792 | 0.758 |
lgbm | pred | none | 0.806 | 0.878 | 0.801 | 0.800 | 0.769 | 0.850 | 0.801 | 0.789 |
knn | wrap | none | 0.805 | 0.867 | 0.775 | 0.789 | 0.840 | 0.800 | 0.775 | 0.667 |
sgd | embed_linear | linear | 0.803 | 0.821 | 0.800 | 0.798 | 0.775 | 0.844 | 0.800 | 0.787 |
lr | assoc | none | 0.800 | 0.891 | 0.784 | 0.789 | 0.777 | 0.842 | 0.784 | 0.749 |
sgd | wrap | none | 0.799 | 0.899 | 0.768 | 0.778 | 0.808 | 0.807 | 0.768 | 0.669 |
lr | wrap | none | 0.799 | 0.904 | 0.771 | 0.781 | 0.808 | 0.805 | 0.771 | 0.673 |
sgd | none | none | 0.799 | 0.898 | 0.787 | 0.790 | 0.778 | 0.839 | 0.787 | 0.760 |
sgd | pred | none | 0.798 | 0.879 | 0.790 | 0.791 | 0.779 | 0.820 | 0.790 | 0.738 |
lgbm | assoc | none | 0.797 | 0.878 | 0.784 | 0.787 | 0.784 | 0.839 | 0.784 | 0.758 |
sgd | assoc | none | 0.796 | 0.808 | 0.782 | 0.786 | 0.775 | 0.835 | 0.782 | 0.751 |
gandalf | assoc | none | 0.792 | 0.886 | 0.744 | 0.748 | 0.833 | 0.781 | 0.744 | 0.547 |
lr | pred | none | 0.791 | 0.883 | 0.786 | 0.784 | 0.782 | 0.818 | 0.786 | 0.738 |
lgbm | embed_linear | linear | 0.788 | 0.889 | 0.773 | 0.776 | 0.781 | 0.831 | 0.773 | 0.736 |
lgbm | none | none | 0.783 | 0.888 | 0.764 | 0.768 | 0.790 | 0.818 | 0.764 | 0.709 |
knn | pred | none | 0.746 | 0.785 | 0.733 | 0.729 | 0.741 | 0.774 | 0.733 | 0.649 |
knn | embed_linear | linear | 0.745 | 0.800 | 0.722 | 0.730 | 0.732 | 0.764 | 0.722 | 0.629 |
knn | assoc | none | 0.745 | 0.800 | 0.722 | 0.730 | 0.732 | 0.764 | 0.722 | 0.629 |
knn | none | none | 0.745 | 0.800 | 0.722 | 0.730 | 0.732 | 0.764 | 0.722 | 0.629 |
gandalf | wrap | none | 0.730 | 0.865 | 0.707 | 0.687 | 0.808 | 0.784 | 0.707 | 0.629 |
dummy | embed_linear | linear | 0.611 | 0.500 | 0.500 | 0.378 | – | 0.611 | 0.500 | 0.000 |
dummy | pred | none | 0.611 | 0.500 | 0.500 | 0.378 | – | 0.611 | 0.500 | 0.000 |
dummy | none | none | 0.611 | 0.500 | 0.500 | 0.378 | – | 0.611 | 0.500 | 0.000 |
dummy | assoc | none | 0.611 | 0.500 | 0.500 | 0.378 | – | 0.611 | 0.500 | 0.000 |
dummy | embed_lgbm | lgbm | 0.611 | 0.500 | 0.500 | 0.378 | – | 0.611 | 0.500 | 0.000 |
dummy | wrap | none | 0.611 | 0.500 | 0.500 | 0.378 | – | 0.611 | 0.500 | 0.000 |
gandalf | embed_linear | linear | 0.589 | 0.696 | 0.539 | 0.435 | 0.358 | 0.638 | 0.539 | 0.311 |
Feature | Score |
---|---|
CaNAN | 0.720 |
SOD1N | 0.893 |
pP70S6N | 0.906 |
BADN_NAN | 0.912 |
UbiquitinN | 0.916 |
H3AcK18N | 0.918 |
pRSKN | 0.914 |
pPKCABN | 0.901 |
NR2AN | 0.905 |
pCASP9N | 0.903 |
GSK3BN | 0.897 |
pCAMKIIN | 0.862 |
Measurement | Data | Description |
---|---|---|
Genotype | c | Control mouse |
t | Trisomy mouse (Ts65Dn model) | |
Treatment Type | m | Mouse injected with Memantine |
s | Mouse injected with saline (control) | |
Behavior | CS | Context-shock: Mice explored test chamber before shock |
SC | Shock-context: Mice received shock before exploration | |
Class (Target) | c-CS-s | Control mouse: Context-shock conditioning + saline |
c-CS-m | Control mouse: Context-shock conditioning + memantine | |
c-SC-s | Control mouse: Shock-context conditioning + saline | |
c-SC-m | Control mouse: Shock-context conditioning + memantine | |
t-CS-s | Ts65Dn mouse: Context-shock conditioning + saline | |
t-CS-m | Ts65Dn mouse: Context-shock conditioning + memantine | |
t-SC-s | Ts65Dn mouse: Shock-context conditioning + saline | |
t-SC-m | Ts65Dn mouse: Shock-context conditioning + memantine |
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Huang, X.; Gauthier, C.; Berger, D.; Cai, H.; Levman, J. Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence. Int. J. Mol. Sci. 2025, 26, 6878. https://doi.org/10.3390/ijms26146878
Huang X, Gauthier C, Berger D, Cai H, Levman J. Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence. International Journal of Molecular Sciences. 2025; 26(14):6878. https://doi.org/10.3390/ijms26146878
Chicago/Turabian StyleHuang, Xiyao, Carson Gauthier, Derek Berger, Hao Cai, and Jacob Levman. 2025. "Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence" International Journal of Molecular Sciences 26, no. 14: 6878. https://doi.org/10.3390/ijms26146878
APA StyleHuang, X., Gauthier, C., Berger, D., Cai, H., & Levman, J. (2025). Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence. International Journal of Molecular Sciences, 26(14), 6878. https://doi.org/10.3390/ijms26146878