Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network

Yan, Jielu; Zhang, Bob; Zhou, Mingliang; Kwok, Hang Fai; Si, Yain-Whar

doi:10.1016/j.compbiomed.2022.105717

Cited by 17 publications

(26 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5 , we observed that six baseline models based on QSO encoding, and three baseline models based on 1OHE, CTDD, seq2vec, and CKSAAGP encoding respectively contributed two baseline models, four encodings (DDE, CTDC, KSC, and AAC) based their respective model contributed the most in the final MLACP 2.0 prediction. The importance of physicochemical properties has been highlighted in previous studies [49] , [50] . Our analysis also indicates that CKSAAGP is one of the influential features in MLACP 2.0 performance.…”

Section: Resultsmentioning

confidence: 93%

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction

Phan

Park²,

Pitti³

et al. 2022

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 93%

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction

Phan

Park²,

Pitti³

et al. 2022

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

“…The authors [ 39 ] developed a biomedical electrocardiogram (ECG)-based ML technique for detecting heart disease. Jiely Yan et al [ 40 ], proposed a model to predict ion channel peptide from the images. Table 1 outlines the features and limitations of the existing CNN models.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

2022

View full text Add to dashboard Cite

In recent times, coronary artery disease (CAD) has become one of the leading causes of morbidity and mortality across the globe. Diagnosing the presence and severity of CAD in individuals is essential for choosing the best course of treatment. Presently, computed tomography (CT) provides high spatial resolution images of the heart and coronary arteries in a short period. On the other hand, there are many challenges in analyzing cardiac CT scans for signs of CAD. Research studies apply machine learning (ML) for high accuracy and consistent performance to overcome the limitations. It allows excellent visualization of the coronary arteries with high spatial resolution. Convolutional neural networks (CNN) are widely applied in medical image processing to identify diseases. However, there is a demand for efficient feature extraction to enhance the performance of ML techniques. The feature extraction process is one of the factors in improving ML techniques’ efficiency. Thus, the study intends to develop a method to detect CAD from CT angiography images. It proposes a feature extraction method and a CNN model for detecting the CAD in minimum time with optimal accuracy. Two datasets are utilized to evaluate the performance of the proposed model. The present work is unique in applying a feature extraction model with CNN for CAD detection. The experimental analysis shows that the proposed method achieves 99.2% and 98.73% prediction accuracy, with F1 scores of 98.95 and 98.82 for benchmark datasets. In addition, the outcome suggests that the proposed CNN model achieves the area under the receiver operating characteristic and precision-recall curve of 0.92 and 0.96, 0.91 and 0.90 for datasets 1 and 2, respectively. The findings highlight that the performance of the proposed feature extraction and CNN model is superior to the existing models.

show abstract

“…In Yan et al, 16 the author proposed a technique based on deep learning to enhance ion channel peptide classification. They created Multi‐Branch‐CNN to distinguish between three different ion channel peptide binders from intra‐ and interfeature types.…”

Section: Related Workmentioning

confidence: 99%

Machine learning‐based approach for prediction of ion channels and their subclasses

Singh¹,

Tiwari²

2022

J of Cellular Biochemistry

View full text Add to dashboard Cite

Ion channels are ion-permeable protein pores that are found in all cell lipid membranes. Distinct ion channels play multiple roles in biological processes.Proteomic data is fast accumulating as a result of the fast growth of mass spectrometry and giving us the chance to comprehensively explore ion channel classes along with their subclasses. This paper proposes an eXtreme Gradient Boosting (XGBoost)-based method to estimate the ion channel classes and their subclasses. Here, 12 feature vectors are applied to better characterize protein sequences like amino acid composition, pseudo-amino acid composition, normalized moreau-broto autocorrelation, amphiphilic pseudo-amino acid composition, dipeptide composition, Geary autocorrelation, tripeptide composition, sequence-order-coupling number, composition/transition/distribution, conjoint triad, moran autocorrelation, quasisequence-order descriptors. Here, a total of 9920 features are extracted from the protein sequence. The principal component analysis is applied to determine the optimal number of features to optimize the performance. In 10-fold cross-validation the proposed XGBoost based approach with optimal 50 features achieved accuracy of 100%, 98.70%, 98.77%, 97.26%, 87.40%, 97.39%, 98.03%, 96.42%, and F1-Score of 100%, 99%, 99%, 97%, 87%, 97%, 98%, 97%, for prediction of ion channel and nonion channel, voltage-gated and ligand-gated ion channels, subclasses of voltage-gated ion channels (VGICs), subclasses of ligand-gated ion channels (LGICs), subclasses of voltage-gated calcium channels (VGCCs), subclasses of voltage-gated potassium channels (VGKCs), subclasses of voltage-gated sodium channels (VGSCs), and subclasses of voltage-gated chloride channels, respectively. Here the proposed approach also compares with the other approaches such as support vector machine, k-nearest neighbor, Gaussian Naïve Bayes, and random forest and also compares with existing methods such as support vector machine (SVM) with maximum relevance maximum distance with an accuracy of 86.6%, 83.7%, and 85.1%, for ion channels, non-ion channels and overall respectively and SVM with radial basis function kernel-based method with an accuracy of 100%, 97% and 99.9% for ion channels, nonion channels, and overall accuracy, respectively.

show abstract

Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network

Cited by 17 publications

References 43 publications

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction

Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

Machine learning‐based approach for prediction of ion channels and their subclasses

Contact Info

Product

Resources

About