iAcetylK-PseAAC: Improving Accuracy of Lysine Acetylation Predictor by Incorporating Statistical Moments and Position Relative Features into PseAAC

Simple item page
dc.contributor.authorAQSA ANWAR
dc.date.accessioned2025-09-27T09:34:38Z
dc.date.available2025-09-27T09:34:38Z
dc.date.issued2019
dc.description.abstractAmong different Post-Translational Modification (PTM) the most vital one is lysine acetylation in protein. Its importance can’t be undermined related to different diseases and essential biological practice. The key step to find the underneath layer of acetylation along with their site is to completely apprehend the mechanism behind this biological process. In the previous research models based on artificial neural network (ANN), they have used different techniques like position weighted matrix (PWM), support vector machine (SVM), k nearest neighbors (KNN) score and many others but still they weren’t able to maximize the accuracy of the prediction. Our predictor model have used SVV, SM, FV, PRIM, RPRIM, AAPIV and RAAPIV based on ANN to compute the accuracy, sensitivity, specificity and MCC which are 82.9918%, 98.2242%, 94.504% and 0.50226 respectively using 10-fold cross validation. The results of independent dataset testing were 87.74% accuracy, 95.72% sensitivity, 70.19% specificity and 0.7067 MCC. Our model have given the more accuracy than other research models, using ANN
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/7198
dc.language.isoen
dc.publisherUMT, Lahore
dc.titleiAcetylK-PseAAC: Improving Accuracy of Lysine Acetylation Predictor by Incorporating Statistical Moments and Position Relative Features into PseAAC
dc.typeThesis
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
iAcetylK-PseAAC Improving Accuracy of Lysine Acetylation Predictor by Incorporating Statistical Moments and Position Relative Features into PseAAC.docx
Size:
3.19 MB
Format:
Microsoft Word XML
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
2019