Classification of Inflammatory Gene Expression Patterns with Machine Learning Models
Published in IEEE, 2023
Recommended citation: Y. Ay, Y. H. Choo, A. Bhatti and C. P. Lim, "Classification of Inflammatory Gene Expression Patterns with Machine Learning Models," 2023 IEEE 4th International Conference on Pattern Recognition and Machine Learning (PRML), Urumqi, China, 2023, pp. 115-119, doi: 10.1109/PRML59573.2023.10348265. https://doi.org/10.1109/PRML59573.2023.10348265
Dementia is a complex neurological disorder characterized by a progressive decline in cognitive functions. Recent studies have suggested a link between neuroinflammation and the development of dementia, particularly in Alzheimer’s disease. This study focuses on the analysis of inflammatory gene expression patterns in the parietal cortex (PCx) and temporal cortex (TCx) from a human brain RNA sequence data set, aiming to derive insights into the underlying genetic link associated with dementia, as well as prediction of dementia. Using five machine learning and statistical methods, our study reveals better detection and classification of dementia patients using PCx-related gene patterns, as compared with those from the TCx. Our findings support the hypothesis that the PCx plays a significant role in neuroinflammatory responses associated with dementia. Based on this preliminary study, machine learning models offer a useful approach to recognize inflammatory gene expression patterns and providing a valuable biomarker analysis and prediction for dementia diagnosis.