PREDICTION OF T-CELL USING DEEP NEURAL NETWORKS (DNNs)
| dc.contributor.author | MUHAMMAD FARHAN AKHTAR | |
| dc.date.accessioned | 2025-08-28T11:11:35Z | |
| dc.date.available | 2025-08-28T11:11:35Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Accurate T-cell behavior prediction is a crucial objective in immunology research since it aids in a better understanding of immune responses and the development of specialized therapies. This study advances our knowledge of immunology and the creation of novel therapeutic alternatives by forecasting T-cell activity using a neural network-based methodology. T-cells are key players in adaptive immunity because they respond to a wide range of antigens with intricate activation and differentiation patterns. This study uses the strength of neural networks, particularly feedforward neural networks and similar designs, to unravel the intricacy of T-cell responses. This investigation's initial phase comprises a thorough examination of T-cell biology with a focus on their critical role in immunological defense and surveillance. The intricate connections between T-cells demonstrate the complexity of T-cell behavior. To assist in the forecasting of T-cell responses, a sizable dataset encompassing multiple T-cell activation profiles and associated molecular markers was assembled. In order to illustrate the intricate relationships between T-cell receptor sequences, signaling cascades, and biological effects, neural network models that utilize this dataset are trained. | |
| dc.identifier.uri | https://escholar.umt.edu.pk/handle/123456789/5627 | |
| dc.language.iso | en | |
| dc.publisher | UMT, Lahore | |
| dc.title | PREDICTION OF T-CELL USING DEEP NEURAL NETWORKS (DNNs) | |
| dc.type | Thesis |
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