Table 3.
Tools/servers for allergen/allergenicity prediction.
| Server | URL | Method | Efficiency | Reference |
|---|---|---|---|---|
| AlgPred |
http://www.imtech.res.in/raghava/algpred/ (Accessed on: 21 August 2021) |
SVM and allergen sequence features, epitopes, sequence motifs | Accuracy: 85% Specificity: 88.1% |
[4] |
| AllerHunter |
http://tiger.dbs.nus.edu.sg/AllerHunter/ (Accessed on: 21 August 2021) |
SVM and iterative pairwise sequence similarity | Accuracy: 95.3% Specificity: 96.41% |
[211] |
| PREAL |
http://gmobl.sjtu.edu.cn/PREAL/index.php (Accessed on: 21 August 2021) |
Physicochemical and biochemical descriptors, sequence features, subcellular locations, SVM, and mRMR | Accuracy: 93.42% | [225] |
| AllergenFP |
http://ddg-pharmfac.net/AllergenFP/ (Accessed on: 21 August 2021) |
Descriptor-based fingerprints of residues, auto and cross-covariance | Accuracy: 88% | [226] |
| AllerTOP |
http://www.ddg-pharmfac.net/AllerTOP (Accessed on: 22 August 2021) |
Machine learning, sequence based descriptors, cross and auto -covariance, |
Accuracy: 85.3% Specificity: 88.1% |
[227] |
| AllerCatPro |
https://allercatpro.bii.a-star.edu.sg/ (Accessed on: 22 August 2021) |
Sequence similarity, structure similarity | Accuracy: 84% Specificity: 67% |
[228] |