AITeQ: A machine learning framework for Alzheimer's prediction using a distinctive 5-gene signature

Ahammad, I.; Lamisa, A. B.; Bhattacharjee, A.; Jamal, T. B.; Arefin, M. S.; Chowdhury, Z. M.; Hossain, M. U.; Das, K. C.; Keya, C. A.; Salimullah, M.

doi:10.1101/2023.09.16.558056

bioRxiv · 10.1101/2023.09.16.558056

AITeQ: A machine learning framework for Alzheimer's prediction using a distinctive 5-gene signature

Ahammad, I.·Lamisa, A. B.·Bhattacharjee, A.·Jamal, T. B.·Arefin, M. S.·Chowdhury, Z. M.·Hossain, M. U.·Das, K. C.·Keya, C. A.·Salimullah, M.

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Abstract

Neurodegenerative diseases, such as Alzheimers disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimers Identification Tool using RNA-Seq (AITeQ), a machine learning model based on an optimized random forest algorithm for identification of Alzheimers from RNA-Seq data. Analysis of RNA-Seq data from 433 individuals, including 293 Alzheimers patients and 140 controls led to the discovery of 47,929 differentially expressed genes. This was followed by a machine learning protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of 4 different methodologies, culminated in the identification of a compact yet impactful set of 5 genes. Ten diverse machine learning models were trained and tested using these 5 genes (ITGA10, CXCR4, ADCYAP1, SLC6A12, VGF). Performance metrics, including precision, recall, F1-score, accuracy, receiver operating characteristic area under the curve, and confusion matrices, were assessed before and after hyperparameter tuning. Overall, the random forest model with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ Key PointsO_LIA set of 5 genes (ITGA10, CXCR4, ADCYAP1, SLC6A12, VGF) were identified following differential gene expression and feature importance analysis. C_LIO_LITen diverse machine learning algorithms were trained and tested using the gene expression patterns of the identified 5 genes. The random forest algorithm with customized hyperparameters was found to be the best-performing model for differentiating Alzheimers disease samples from control. C_LIO_LIAITeQ, a user-friendly, reliable, and accurate machine learning framework for Alzheimers disease prediction was developed based on the 5 gene signature. C_LI

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Ahammad, I., Lamisa, A. B., Bhattacharjee, A., Jamal, T. B., Arefin, M. S., Chowdhury, Z. M., Hossain, M. U., Das, K. C., Keya, C. A., Salimullah, M.. 2023-09-18. AITeQ: A machine learning framework for Alzheimer's prediction using a distinctive 5-gene signature. https://doi.org/10.1101/2023.09.16.558056

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