Prediction of nitrosocysteine sites using position and composition variant features
| dc.contributor.author | Batool, Aroosa | |
| dc.date.accessioned | 2018-01-24T05:55:15Z | |
| dc.date.available | 2018-01-24T05:55:15Z | |
| dc.date.issued | 2017 | |
| dc.description | Supervised by:Dr. Yaser Daanial Khan and Dr. Nouman Rasool | en_US |
| dc.description.abstract | Protein S-nitrosylation, a significant posttranslational modification of protein, involves the addition of nitrogen oxide group to cysteine thiols to form S-nitrosocysteine. Growing evidence has suggested that S-nitrosylation plays a major role in numerous human diseases. So, it is highly anticipated for the intuition into biological research and drug discovery to develop such techniques for timely identification of S-nitrosylated proteins. The proposed system endeavors a novel strategy based on numerous intellectual computational method for the identification of S-nitrosocystiene site from a protein sequence. Statistical moments were used to extract the features and built a multilayer neural network model using Gradient Descending and Adaptive Learning Algorithm. The comparison results on the-state-of-the-art benchmark datasets have shown that this proposed scheme is very propitious, accurate and exceptionally effective for the prediction of S-nitrosocystiene in protein sequence | en_US |
| dc.identifier.uri | https://escholar.umt.edu.pk/handle/123456789/2560 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of Management and Technology Lahore | en_US |
| dc.subject | S-nitrosylation | en_US |
| dc.subject | Drug discovery | en_US |
| dc.subject | MS Thesis | en_US |
| dc.title | Prediction of nitrosocysteine sites using position and composition variant features | en_US |
| dc.type | Thesis | en_US |
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