IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach
Open Access
- 25 January 2023
- journal article
- research article
- Published by Hindawi Limited in Computational Intelligence and Neuroscience
- Vol. 2023, 1-13
- https://doi.org/10.1155/2023/2465414
Abstract
Motivation. Immunoglobulin proteins (IGP) (also called antibodies) are glycoproteins that act as B-cell receptors against external or internal antigens like viruses and bacteria. IGPs play a significant role in diverse cellular processes ranging from adhesion to cell recognition. IGP identifications via the in-silico approach are faster and more cost-effective than wet-lab technological methods. Methods. In this study, we developed an intelligent theoretical deep learning framework, IGPred-HDnet for the discrimination of IGPs and non-IGPs. Three types of promising descriptors are feature extraction based on graphical and statistical features (FEGS), amphiphilic pseudo-amino acid composition (Amp-PseAAC), and dipeptide composition (DPC) to extract the graphical, physicochemical, and sequential features. Next, the extracted attributes are evaluated through machine learning, i.e., decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), and hierarchical deep network (HDnet) classifiers. The proposed predictor IGPred-HDnet was trained and tested using a 10-fold cross-validation and independent test. Results and Conclusion. The success rates in terms of accuracy (ACC) and Matthews correlation coefficient (MCC) of IGPred-HDnet on training and independent dataset (Dtrain Dtest) are ACC=98.00, 99.10, and MCC=0.958, and 0.980 points, respectively. The empirical outcomes demonstrate that the IGPred-HDnet model efficacy on both datasets using the novel FEGS feature and HDnet algorithm achieved superior predictions to other existing computational models. We hope this research will provide great insights into the large-scale identification of IGPs and pharmaceutical companies in new drug design.Keywords
Funding Information
- Qassim University
This publication has 66 references indexed in Scilit:
- Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection techniqueMolecular BioSystems, 2016
- A Prediction Model for Membrane Proteins Using Moments Based FeaturesBioMed Research International, 2016
- Database development and automatic speech recognition of isolated Pashto spoken digits using MFCC and K-NNInternational Journal of Speech Technology, 2015
- PEASE: predicting B-cell epitopes utilizing antibody sequenceBioinformatics, 2014
- An Efficient Algorithm for Recognition of Human ActionsThe Scientific World Journal, 2014
- Antibody informatics for drug discoveryBiochimica et Biophysica Acta (BBA) - Proteins and Proteomics, 2014
- Rep‐Seq: uncovering the immunological repertoire through next‐generation sequencingImmunology, 2012
- The use of classification trees for bioinformaticsWIREs Data Mining and Knowledge Discovery, 2011
- Extremely randomized treesMachine Learning, 2006
- High-Dose Intravenous Immunoglobulin in Cutaneous Lupus ErythematosusArchives of Dermatology, 1999