NLP-BCH-Ens: NLP-based intelligent computational model for discrimination of malaria parasite

Hayat, Maqsood; Tahir, Muhammad; Alarfaj, Fawaz Khaled; Alturki, Ryan; Gazzawe, Foziah

doi:10.1016/j.compbiomed.2022.105962

Cited by 6 publications

(4 citation statements)

References 43 publications

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“…Generally, ExPseAAC can be formulated as E x P s e A A C = false[ f 1 , ... , f 20 , f 20 + 1 , ... , f 20 + λ false] T , ( λ < N ) where the first 20 attributes denote the frequency information on 20 natural AAs in the peptide sequence and the 21st feature vector, i.e., f 20+1 denotes the additional correlation factor related to first tier sequence, the 22nd factor to the second tier, and so on . In this study, after experimental analysis, we kept the value for encoding Anti-MRSA peptides.…”

Section: Methodsmentioning

confidence: 99%

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

Arif,

Fang,

Fida

et al. 2024

ACS Omega

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Methicillin-resistant Staphylococcus aureus (MRSA) is a growing concern for human lives worldwide. Anti-MRSA peptides act as potential antibiotic agents and play significant role to combat MRSA infection. Traditional laboratory-based methods for annotating Anti-MRSA peptides are although precise but quite challenging, costly, and time-consuming. Therefore, computational methods capable of identifying Anti-MRSA peptides accelerate the drug designing process for treating bacterial infections. In this study, we developed a novel sequence-based predictor "iM-RSAPred" for screening Anti-MRSA peptides by incorporating energy estimation and physiochemical and sequential information. We successfully resolved the skewed imbalance phenomena by using synthetic minority oversampling technique plus Tomek link (SMOTETomek) algorithm. Furthermore, the Shapley additive explanation method was leveraged to analyze the impact of topranked features in the prediction task. We evaluated multiple machine learning algorithms, i.e., CatBoost, Cascade Deep Forest, Kernel and Tree Boosting, support vector machine, and HistGBoost classifiers by 10-fold cross-validation and independent testing. The proposed iMRSAPred method significantly improved the overall performance in terms of accuracy and Matthew's correlation coefficient (MCC) by 5.45 and 0.083%, respectively, on the training data set. On the independent data set, iMRSAPred improved accuracy and MCC by 3.98 and 0.055%, respectively. We believe that the proposed method would be useful in large-scale Anti-MRSA peptide prediction and provide insights into other bioactive peptides.

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Section: Methodsmentioning

confidence: 99%

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

Arif,

Fang,

Fida

et al. 2024

ACS Omega

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“…Hayat et al [ 178 ] discover Malaria infection using Genetic Algorithm (GA) and Computational Linguistic (CL) to classify the Plasmodium falciparum. Malaria is a contagious and lethal infection that is brought on by Plasmodium falciparum.…”

Section: Deep Learning For Medical Image Analysis and Cadmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

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“…Moreover, incorporating an ensemble strategy in classical machine learning approaches proves particularly advantageous as it reduces variance arising from inconsistent prediction rates of conventional classifiers [52,53]. Consequently, scientists have extensively employed ensemble learning methods across diverse domains over last few years, encompassing topics such as neuro-peptides [54], protein subcellular localization [55], antiviral peptides [56], anti-cancer peptides [34], anti-fungal peptides [57], recombination spots [58], and malaria parasite [59] . In our study, we utilized an ensemble learning method using optimized genetic algorithm (GA) to assess the predictive outcomes of the composite features.…”

Section: ) Ensemble Learningmentioning

confidence: 99%

pAtbP-EnC: Identifying Anti-Tubercular Peptides Using Multi-Feature Representation and Genetic Algorithm-Based Deep Ensemble Model

Akbar,

Raza,

Shloul

et al. 2023

IEEE Access

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Mycobacterium tuberculosis, a highly perilous pathogen in humans, serves as the causative agent of tuberculosis (TB), affecting nearly 33% of the global population. With the increasing prevalence of multidrug-resistant TB, there is a needs for novel and efficacious alternative therapies. Peptide therapies have emerged as a favorable alternative due to its remarkable specificity in targeting effected cells without effecting healthy cells. However, the experimental identification of anti-tubercular peptides (AtbPs) is labor-intensive and costly. Therefore, accurate prediction of AtbPs has become challenging due to the large number of peptide samples. In this paper, we propose an ensemble learning model to enhance the prediction outcomes by addressing the limitations of individual learning models. We formulate the training samples by utilizing four distinct representation methods: AAindex, Composition/Transition/Distribution, Dipeptide Deviation from Expected Mean, and Enhanced Grouped Amino Acid Composition to numerically encode peptide samples. The feature vectors extracted from these methods are fused to develop a compact vector. We evaluate the prediction rates using three different classification models, employing both individual and heterogeneous vectors. Furthermore, we enhance the prediction and training capabilities of the proposed model by using the predicted labels of the individual classifiers for implementing an ensemble deep model via genetic algorithm. Through evaluation on both the training datasets and independent datasets, our proposed ensemble learner achieves impressive accuracies of 97.80%, 95.13%, 93.91%, and 94.17%, using RD training, MD training, RD independent, and MD independent datasets, respectively. Our findings demonstrate that the proposed pAtbP-EnC model outperforms existing predictors by reporting approximately 11% higher training accuracy. We conclude that the pAtbP-EnC predictor will be a considerable tool in the field of pharmaceutical design and research academia. The used datasets and the source code are publicly available at https://github.com/Intelligent-models/pAtbP-EnC2023.

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NLP-BCH-Ens: NLP-based intelligent computational model for discrimination of malaria parasite

Cited by 6 publications

References 43 publications

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

pAtbP-EnC: Identifying Anti-Tubercular Peptides Using Multi-Feature Representation and Genetic Algorithm-Based Deep Ensemble Model

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