DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes

Attique, Muhammad; Alkhalifah, Tamim; Alturise, Fahad; Khan, Yaser Daanial

doi:10.1016/j.compbiolchem.2023.107874

Cited by 5 publications

(2 citation statements)

References 54 publications

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“…A novel deep learning model, called DeepBCE, was developed to predict immunostimulatory factor B-cell epitopes from protein sequences to understand the binding mechanisms between antigens and antibodies. This model was developed using a combination of deep CNNs and A position and AA composition variant feature-based feature vectors, and was able to accurately predict linear B-cell epitopes [ 101 ]. In addition, there are many other deep learning methods that can be applied to biological data.…”

Section: Discussionmentioning

confidence: 99%

The role of ncRNA regulatory mechanisms in diseases—case on gestational diabetes

Gao,

Ren,

Hao

et al. 2023

Briefings in Bioinformatics

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Non-coding RNAs (ncRNAs) are a class of RNA molecules that do not have the potential to encode proteins. Meanwhile, they can occupy a significant portion of the human genome and participate in gene expression regulation through various mechanisms. Gestational diabetes mellitus (GDM) is a pathologic condition of carbohydrate intolerance that begins or is first detected during pregnancy, making it one of the most common pregnancy complications. Although the exact pathogenesis of GDM remains unclear, several recent studies have shown that ncRNAs play a crucial regulatory role in GDM. Herein, we present a comprehensive review on the multiple mechanisms of ncRNAs in GDM along with their potential role as biomarkers. In addition, we investigate the contribution of deep learning-based models in discovering disease-specific ncRNA biomarkers and elucidate the underlying mechanisms of ncRNA. This might assist community-wide efforts to obtain insights into the regulatory mechanisms of ncRNAs in disease and guide a novel approach for early diagnosis and treatment of disease.

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Section: Discussionmentioning

confidence: 99%

The role of ncRNA regulatory mechanisms in diseases—case on gestational diabetes

Gao,

Ren,

Hao

et al. 2023

Briefings in Bioinformatics

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“…IL4pred achieved crossvalidation and independent test accuracies of 0.758 and 0.690, respectively. Further information regarding related computational approaches developed for the identification of TCEs can be found in references [35][36][37]. However, at present, there is no sequence-based predictor specifically designed for identifying and characterizing TCE-HCVs.…”

Section: Introductionmentioning

confidence: 99%

TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus

Charoenkwan,

Waramit,

Chumnanpuen

et al. 2023

PLoS ONE

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Hepatitis C virus (HCV) infection is a concerning health issue that causes chronic liver diseases. Despite many successful therapeutic outcomes, no effective HCV vaccines are currently available. Focusing on T cell activity, the primary effector for HCV clearance, T cell epitopes of HCV (TCE-HCV) are considered promising elements to accelerate HCV vaccine efficacy. Thus, accurate and rapid identification of TCE-HCVs is recommended to obtain more efficient therapy for chronic HCV infection. In this study, a novel sequence-based stacked approach, termed TROLLOPE, is proposed to accurately identify TCE-HCVs from sequence information. Specifically, we employed 12 different sequence-based feature descriptors from heterogeneous perspectives, such as physicochemical properties, composition-transition-distribution information and composition information. These descriptors were used in cooperation with 12 popular machine learning (ML) algorithms to create 144 base-classifiers. To maximize the utility of these base-classifiers, we used a feature selection strategy to determine a collection of potential base-classifiers and integrated them to develop the meta-classifier. Comprehensive experiments based on both cross-validation and independent tests demonstrated the superior predictive performance of TROLLOPE compared with conventional ML classifiers, with cross-validation and independent test accuracies of 0.745 and 0.747, respectively. Finally, a user-friendly online web server of TROLLOPE (http://pmlabqsar.pythonanywhere.com/TROLLOPE) has been developed to serve research efforts in the large-scale identification of potential TCE-HCVs for follow-up experimental verification.

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An LSTM network-based model with attention techniques for predicting linear T-cell epitopes of the hepatitis C virus

Hosen,

Mahmud,

Goh

et al. 2024

Results in Engineering

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DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes

Cited by 5 publications

References 54 publications

The role of ncRNA regulatory mechanisms in diseases—case on gestational diabetes

The role of ncRNA regulatory mechanisms in diseases—case on gestational diabetes

TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus

An LSTM network-based model with attention techniques for predicting linear T-cell epitopes of the hepatitis C virus

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