Accurate Prediction and Key Feature Recognition of Immunoglobulin

Gong, Yuxin; Liao, Bo; Peng, Dejun; Zou, Quan

doi:10.3390/app11156894

Cited by 6 publications

(4 citation statements)

References 78 publications

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“…We attempt to identify the most crucial components from these selection processes in order to improve forecast accuracy. Swarm optimization approaches [12], Analysis of Variance (ANOVA) [13], and the Information Gain method [14] are among the feature ranking criteria.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Comparative Study on Feature Selection Methods for Protein

Alkady

El-Bahnasy

Gad

2022

IJICIS

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The automated and high-throughput identification of protein function is one of the main issues in computational biology. Predicting the protein's structure is a crucial step in this procedure. In recent years, a wide range of approaches for predicting protein structure has been put forth. They can be divided into two groups: database-based and sequence-based. The first is to identify the principles behind protein structure and attempts to extract valuable characteristics from amino acid sequences. The second one uses pre-existing public annotation databases for data mining. This study emphasizes the sequence-based method and makes use of the ability of amino acid sequences to predict protein activity. The amino acid composition approach, the amino acid tuple approach, and several optimization algorithms were compared. Different protein sequence data sets were used in our experiments. Five classifiers were tested in this research. The best accuracy is 98% using across 10fold cross-validation. This represents the highest performance in the Human dataset.

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Section: Feature Selection Methodsmentioning

confidence: 99%

Comparative Study on Feature Selection Methods for Protein

Alkady

El-Bahnasy

Gad

2022

IJICIS

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“…In this section, we experimentally determine the prediction performance of various classifiers, i.e., KNN [ 62 ], DT [ 63 ], SVM [ 46 , 64 ], and HDnet using various descriptors, i.e., APAAC (physicochemical features), DPC (sequential features), and FEGS (graphical features), as shown in Figure 6 . Each learning engine is computed by conducting a ten-fold CV test on the training dataset D train with four evaluation measures ACC, SN, SP, and MCC.…”

Section: Proposed Framework Evaluationmentioning

confidence: 99%

IGPred‐HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning‐Based Approach

Ali

Alturise

Alkhalifah

et al. 2023

Computational Intelligence and Neuroscience

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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 Matthew’s 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.

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“…In this section, we explain how we created our benchmark data. We retrieved the sequences of immunoglobulins and other proteins from the UniProt [28,37] database. The steps taken were for the purpose of constructing a benchmark dataset.…”

Section: Dataset Constructionmentioning

confidence: 99%

DeepImmuno-PSSM: Identification of Immunoglobulin based on Deep learning and PSSM-Profiles

Ghulam,

Nawab Khan Swati,

Ali

et al. 2023

VAWKUM trans. comput. sci.

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Immunoglobulin has a close connection to a number of disorders and is important in both biological and medicinal contexts. Therefore, it is crucial for illness research to employ efficient techniques to increase the categorization accuracy of immunoglobulins. Computational models have been used in a small number of research to address this important issue, but the accuracy of the predictions is not good enough. As a result, we use a cutting-edge deep learning technique with convolutional neural networks to enhance the performance results. In this study, the immunoglobulin features were extracted using the dipeptide acid composition (DPC) with the position-specific scoring matrix (DPC-PSSM) and position-specific scoring matrix-transition probability composition (PSSM-TPC) methods. we apply extracted features information from the DPC-PSSM profiles and PSSM-TPC profile by using a 1D-convolutional neural network (CNN) over an input shape. The outcomes demonstrated that the DeepImmuno-PSSM method based on sequential minimal optimization was able to properly predict DPC-PSSM accuracy score 93.44% obtained and of the immunoglobulins using the greatest feature subcategory produced by the PSSM-TPC feature mining approach accuracy score 89.92% obtained. Our findings indicate that we are able to provide a useful model for enhancing immunoglobulin proteins' capacity for prediction. Additionally, it implies that employing sequence data in deep learning and PSSM-based features may open up new path for biochemical modelling.

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Accurate Prediction and Key Feature Recognition of Immunoglobulin

Cited by 6 publications

References 78 publications

Comparative Study on Feature Selection Methods for Protein

Comparative Study on Feature Selection Methods for Protein

IGPred‐HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning‐Based Approach

DeepImmuno-PSSM: Identification of Immunoglobulin based on Deep learning and PSSM-Profiles

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