DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform

Ali, Farman; Barukab, Omar; Gadicha, Ajay B.; Patil, Shruti; Alghushairy, Omar; Sarhan, Akram Y.

doi:10.1155/2022/2987407

Cited by 10 publications

(4 citation statements)

References 66 publications

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“…After construction of a novel model, its effectiveness is validated. For this purpose, tenfold test is widely used by existing methods [ 14 , 37 , 82 ]. In tenfold test, dataset is decomposed into 10-folds.…”

Section: Methodsmentioning

confidence: 99%

Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides

Ali

Kumar

Alghamdi

et al. 2023

Arch Computat Methods Eng

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Viruses have killed and infected millions of people across the world. It causes several chronic diseases like COVID-19, HIV, and hepatitis. To cope with such diseases and virus infections, antiviral peptides (AVPs) have been applied in the design of drugs. Keeping in view the significant role in pharmaceutical industry and other research fields, identification of AVPs is highly indispensable. In this connection, experimental and computational methods were proposed to identify AVPs. However, more accurate predictors for boosting AVPs identification are highly desirable. This work presents a thorough study and reports the available predictors of AVPs. We explained applied datasets, feature representation approaches, classification algorithms, and evaluation parameters of performance. In this study, the limitations of the existing studies and the best methods were emphasized. Provided the pros and cons of the applied classifiers. The future insights demonstrate efficient feature encoding approaches, best feature optimization schemes, and effective classification techniques that can improve the performance of novel method for accurate prediction of AVPs.

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Section: Methodsmentioning

confidence: 99%

Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides

Ali

Kumar

Alghamdi

et al. 2023

Arch Computat Methods Eng

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“…ERT was widely used for the classification of biological problems such as the identification of N 6 -methyladenosine, 43 anti-tubercular peptides, 44 recognition of antifreeze proteins, 28 classification of DNA-binding proteins, 45 protein hot spot identification, 46 cell-penetrating peptides, 47 and anti-inflammatory peptides. 48 ERT was adopted in medical fields like segmentation of brain tumors 49 and identifying genetic issues.…”

Section: Classification Algorithmsmentioning

confidence: 99%

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree

Khan

Uddin

Ali

et al. 2023

J. Chem. Inf. Model.

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The development of intracellular ice in the bodies of cold-blooded living organisms may cause them to die. These species yield antifreeze proteins (AFPs) to live in subzero temperature environments. Additionally, AFPs are implemented in biotechnological, industrial, agricultural, and medical fields. Machine learning-based predictors were presented for AFP identification. However, more accurate predictors are still highly desirable for boosting the AFP prediction. This work presents a novel approach, named AFP-SPTS, for the correct prediction of AFPs. We explored the discriminative features with four schemes, namely, dipeptide deviation from the expected mean (DDE), reduced amino acid alphabet (RAAA), grouped dipeptide composition (GDPC), and a novel representative method, called pseudo-position-specific scoring matrix tri-slicing (PseTS-PSSM). Considering the advantages of ensemble learning strategy, we fused each feature vector into different combinations and trained the models with five machine learning algorithms, i.e., multilayer perceptron (MLP), extremely randomized tree (ERT), decision tree (DT), random forest (RF), and AdaBoost. Among all models, PseTS-PSSM + RAAA with an extremely randomized tree attained the best outcomes. The proposed predictor (AFP-SPTS) boosted the accuracies of AFPs in the literature by 1.82 and 4.1%.

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“…Ali et al [23] introduced the AFP-CMBPred predictor for antifreeze protein identification and performed protein sample classification using SVM and RF-based learning models. In the field of biomedical engineering, several other predictors used ML and XAI methods [24], e.g., DP-BINDER [25] for prediction of DNA-binding proteins, iAFPs-EnC-GA [26] for antifungal peptides prediction, iRNA-PseTNC [27] for identification of RNA 5-methylcytosine sites, and cACP-DeepGram [22] for classification of anticancer peptides.…”

Section: Introductionmentioning

confidence: 99%

Analysis and interpretability of machine learning models to classify thyroid disease

Akter,

Mustafa

2024

PLoS ONE

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Thyroid disease classification plays a crucial role in early diagnosis and effective treatment of thyroid disorders. Machine learning (ML) techniques have demonstrated remarkable potential in this domain, offering accurate and efficient diagnostic tools. Most of the real-life datasets have imbalanced characteristics that hamper the overall performance of the classifiers. Existing data balancing techniques process the whole dataset at a time that sometimes causes overfitting and underfitting. However, the complexity of some ML models, often referred to as “black boxes,” raises concerns about their interpretability and clinical applicability. This paper presents a comprehensive study focused on the analysis and interpretability of various ML models for classifying thyroid diseases. In our work, we first applied a new data-balancing mechanism using a clustering technique and then analyzed the performance of different ML algorithms. To address the interpretability challenge, we explored techniques for model explanation and feature importance analysis using eXplainable Artificial Intelligence (XAI) tools globally as well as locally. Finally, the XAI results are validated with the domain experts. Experimental results have shown that our proposed mechanism is efficient in diagnosing thyroid disease and can explain the models effectively. The findings can contribute to bridging the gap between adopting advanced ML techniques and the clinical requirements of transparency and accountability in diagnostic decision-making.

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DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform

Cited by 10 publications

References 66 publications

Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides

Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree

Analysis and interpretability of machine learning models to classify thyroid disease

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