Iptsw (3l)
| dc.contributor.author | MAHA ASHRAF | |
| dc.date.accessioned | 2025-12-08T09:27:37Z | |
| dc.date.available | 2025-12-08T09:27:37Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | The promoter sequence in genomic data are comprising of 81-1000base pairs, exists in the upstream part of the genic transcriptional start site. It modulates the transcription mechanism of many genes in association with a variety of transcriptional factors. With the revolution in the advanced-genomic era, it is essential to statistically classify the promoters. Such classification may help in drug development. Some forecasting methods were established. Most of them were restricted to the pure recognition of a DNA query sequence. However, based on their differing levels of strength for transcriptional activation, the advertising expression can be split into three layers: promoter vs non-promoter, types of promotors, and their strengths. A modern three-layer predictor, named "iPTSW (3L)" has been established by integrating the information of nucleotide abundance and physiochemical properties into position and composition-dependent features. Its first layer decides whether the query DNA sequence is a valid promoter, the second layer classifies the type of a promoter, while its third layer determines the strength of the promoter. A model is trained by using the Random forest technique to learn the pattern and sequence of the data for prediction. An accuracy of 97.57% for 10-Fold cross-validation, 99.8% for Jackknife validation, and 89.0% for Independent Testing was achieved. These results indicate that the proposed methodology plays a vital role in prediction instead of performing all tests in the laboratory utilizing conventional ways and also cost Efficient. This research can also help in solving relevant prediction problems. The web server http://biopred.org/promotersis developed for Sequence Prediction. | |
| dc.identifier.uri | https://escholar.umt.edu.pk/handle/123456789/15370 | |
| dc.language.iso | en | |
| dc.publisher | UMT Lahore | |
| dc.title | Iptsw (3l) | |
| dc.title.alternative | a three-layer predictor to identify a promoter its type and strength by position and composition-dependent features | |
| dc.type | Thesis |