Micro bactarieal secretion of protein using deep learning

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dc.contributor.authorMicro bactarieal secretion of protein using deep learning
dc.date.accessioned2025-12-16T14:53:40Z
dc.date.available2025-12-16T14:53:40Z
dc.date.issued2022
dc.description.abstractBio informatics has made significant advancements in recent years, particularly in the study of protein secretion. Neural Networks may be able to fill the inherent gaps and limitations in machine learning techniques. In proteomics and genomics, deep learning (DL) and neural networks have gained appeal due to their capacity to unearth complex correlations that are concealed in vast amounts of biological data. This study began by examining state-of-the-art statistical techniques for finding protein secretion. According to studies, machine learning techniques and a range of feature extraction techniques are used in the majority of the work done to predict the protein secretion sites. There are various issues and restrictions with these machine learning-based forecasts that required to be resolved. A possible method to improve the reliability of Micro bactarieal secretion of protein predictions is deep learning. Chou’s five-step rule is used in this paper to include a deep learningbased method for better prediction of Micro bactarieal secretion of protein. the deep learning methodology uses the gated recurrent unit, long short-term memory unit, and convolutional neural network. the results show a dataset improvement with the highest accuracy at 86% using the FCN model
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/16449
dc.language.isoen
dc.publisherUMT Lahore
dc.titleMicro bactarieal secretion of protein using deep learning
dc.typeThesis
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