2025

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Browsing 2025 by Author "IRAM SHEHZADI"

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    Deep & machine learning approch for alzheimer’s disease identification
    (UMT Lahore, 2025) IRAM SHEHZADI
    Alzheimer’s disease (AD) is a chronic, multifaceted brain disease that belongs to the group of dementia worldwide. At the mundane level, a molecular analysis of the pathogenesis of Alzheimer’s and especially at the level of proteins is indispensable for the early diagnosis and, subsequently, for the discovery of treatment s. In this research, we are interested in distinguishing between the Alzheimer’s linked protein sequences and the protein sequences linked to the other forms of dementia. We importantly operate from the GOLD standard dataset, that is a commonly used benchmark set that has a total of 754 protein sequences. This dataset is split into two categories: 304 proteins related to Alzheimer’s disease and 450 protein sequences not related to Alzheimer’s but relate to other forms of dementia. Thus, by sorting out these proteins the study wishes to understand the proteins that are part of Alzheimer’s disease process from proteins that are involved in other neurodegenerative diseases. To realize this classification, the following machine learning and deep learning methods were used: Our strategies involve using some of the most advanced modern learning algorithms, such as CNNs and RNNs, which are powerful in the analysis of numerous biological patterns. We also compared them with other machine learning methods of a more classical nature, such as Random Forest and Support Vector Machines (SVMs) that have been used for classification tasks in bioinformatics. The data was divided into training and a test dataset to train the models and evaluate how well they do under different circumstances. The training dataset was compiled with a view to estimating the parameters of the models while the one used for testing was used for measuring how well the conceived models were able to correctly classify new protein sequences