A bi-layer model for identification of piwiRNA using deep neural learning

Article Biochemistry & Molecular Biology

iRNA-ac4C: A novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA

Wei Su et al.

Summary: A novel predictor, iRNA-ac4C, was developed to identify ac4C sites in human mRNA using three feature extraction methods. The results showed promising generalization capabilities.

INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES (2023)

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Article Biochemistry & Molecular Biology

AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins

Fu-Ying Dao et al.

Summary: CRISPR-Cas has attracted extensive attention as a gene editing tool. Anti-CRISPR (Acr) proteins can inhibit the CRISPR-Cas defense system and be utilized for gene editing regulation. The study developed a high-accuracy prediction model called AcrPred, based on a two-step model fusion strategy, which achieved an AUC of 0.952 with independent dataset validation. The model demonstrated strong generalization ability by correctly identifying 9 out of 10 new Acr proteins compared to published tools. Additionally, a user-friendly web-server AcrPred was established for easy identification of potential Anti-CRISPR proteins.

INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES (2023)

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Article Chemistry, Medicinal

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

Adnan Khan et al.

Summary: The development of intracellular ice in cold-blooded organisms can be fatal, but they produce antifreeze proteins (AFPs) to survive in subzero temperatures. AFPs also have applications in various fields. This study introduces a new approach called AFP-SPTS for accurate AFP prediction. By exploring different features and using ensemble learning with machine learning algorithms, the proposed predictor achieved improved accuracies compared to existing models.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2023)

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Review Computer Science, Interdisciplinary Applications

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

Farman Ali et al.

Summary: Viruses like COVID-19, HIV, and hepatitis have caused illness and death on a global scale. Antiviral peptides (AVPs) have been used to develop drugs for these diseases, and accurate prediction of AVPs is crucial. This study thoroughly examines existing predictors of AVPs, discussing datasets, feature representation methods, classification algorithms, and evaluation parameters. It also highlights limitations of current studies and suggests future improvements for more accurate AVP prediction.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2023)

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Article Automation & Control Systems

Comparative analysis of the existing methods for prediction of antifreeze proteins

Adnan Khan et al.

Summary: Antifreeze proteins (AFPs) are found in various organisms and play a crucial role in preventing the formation of ice crystals. The development of accurate predictors for identifying AFPs is essential. This review article provides a comprehensive summary of existing AFP predictors, including their applied datasets, feature descriptors, model training classifiers, performance assessment parameters, and web servers. The drawbacks of current predictors are highlighted, and suggestions for future improvements, such as more effective feature descriptors and efficient classifiers, are discussed.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2023)

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Article Computer Science, Information Systems

Identifying Neuropeptides via Evolutionary and Sequential Based Multi-Perspective Descriptors by Incorporation With Ensemble Classification Strategy

Shahid Akbar et al.

Summary: In this study, an automatic and computationally effective model for identifying neuropeptides was proposed. The model utilized bigram position-specific scoring matrix (Bi-PSSM) and K-spaced bigram (KSB) to represent evolutionary information, and employed discrete wavelet transform (DWT) for noise reduction. Optimal features were selected from extracted features using Shapley Additive exPlanations (SHAP), and trained via six classification models. The ensemble classification approach achieved a higher predictive accuracy of 94.47% on training sequences and 92.55% on independent sequences, which outperformed existing methods by 3%.

IEEE ACCESS (2023)

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Article Computer Science, Information Systems

Prediction of Amyloid Proteins Using Embedded Evolutionary & Ensemble Feature Selection Based Descriptors With eXtreme Gradient Boosting Model

Shahid Akbar et al.

Summary: Amyloid proteins (AMYs) are insoluble fibrous aggregates with major pathogenic effects on tissues, and their abnormal deposition can lead to several diseases. Accurately predicting AMYs and their pathogenic characteristics is crucial, and various computational predictors have been proposed. This study introduces an intelligent computational predictor that combines evolutionary features, coupling information from AMY sequences, and machine learning techniques to achieve improved prediction accuracy of 93.10% on training sequences and 89.67% on independent sequences, surpassing existing predictors by 4%. The predictive approach is anticipated to be valuable in drug development and academic research.

IEEE ACCESS (2023)

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Article Automation & Control Systems

Deep-PCL: A deep learning model for prediction of cancerlectins and non cancerlectins using optimized integrated features

Adnan et al.

Summary: A fast deep learning model was developed to discriminate cancerlectins from noncancerlectins. By combining and selecting different feature descriptive techniques, the model was able to accurately and efficiently distinguish cancerlectins.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2022)

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Article Automation & Control Systems

iAFPs-EnC-GA: Identifying antifungal peptides using sequential and evolutionary descriptors based multi-information fusion and ensemble learning approach

Ashfaq Ahmad et al.

Summary: Fungal infections are a global health concern, and existing treatments have severe side effects. This study presents an intelligent learning approach to accurately predict antifungal peptides by exploring sequential and evolutionary features. The proposed iAFPs-EnC-GA model achieves a high prediction accuracy and has the potential to play a key role in drug development and academic research.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2022)

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Article Biology

Target-DBPPred: An intelligent model for prediction of DNA-binding proteins using discrete wavelet transform based compression and light eXtreme gradient boosting

Farman Ali et al.

Summary: This study presents an intelligent computational method, Target-DBPPred, to improve the accurate prediction of DBPs. The method combines different evolutionary methods and utilizes a new feature descriptor to capture diverse multivariate features. Experimental results show that the proposed method achieves higher accuracies on both training and testing datasets.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

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Article Multidisciplinary Sciences

XGB-DrugPred: computational prediction of druggable proteins using eXtreme gradient boosting and optimized features set

Rahu Sikander et al.

Summary: This study proposes a machine learning-based method for accurate prediction of drug targets. By extracting features from protein sequences and using feature selection and classifiers for training and prediction, this method achieves the best results in empirical experiments.

SCIENTIFIC REPORTS (2022)

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Article Automation & Control Systems

ACP-2DCNN: Deep learning-based model for improving prediction of anticancer peptides using two-dimensional convolutional neural network

Ali Ghulam et al.

Summary: Cancer, the most dangerous disease for humans, has caused numerous deaths and suffering. Researchers have focused on using cancer peptides for cancer treatment. However, predicting anticancer peptides through experimental methods is costly and laborious, leading to a demand for advanced algorithms. This study proposes a deep learning-based method, ACP-2DCNN, which achieves better prediction accuracy for anticancer peptides compared to existing methods.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2022)

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Article Computer Science, Artificial Intelligence

DBP-CNN: Deep learning-based prediction of DNA-binding proteins by coupling discrete cosine transform with two-dimensional convolutional neural network

Omar Barukab et al.

Summary: This paper proposes a deep learning-based method, DBP-CNN, to improve the prediction of DNA-binding Proteins (DBPs). A novel feature descriptor, PSSM-TS-DCT, is designed to efficiently extract important features, and compared with conventional feature descriptors. Experimental results show that DBP-CNN predicts DBPs more accurately than existing predictors.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

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Article Multidisciplinary Sciences

Prediction of antifreeze proteins using machine learning

Adnan Khan et al.

Summary: In this research, a novel computational method called AFP-LXGB has been proposed for more precise prediction of antifreeze proteins (AFPs). By exploring information through various feature sets and selecting the best feature set, the method has shown significant improvements in prediction accuracy. This approach has important applications in fields such as medicine, agriculture, industry, and biotechnology.

SCIENTIFIC REPORTS (2022)

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Article Engineering, Biomedical

Deep-GHBP: Improving prediction of Growth Hormone-binding proteins using deep learning model

Farman Ali et al.

Summary: This study presents an innovative approach for the prediction of Growth hormone-binding proteins (GHBPs) using PSSM, CTD, Geary, Moran, and PseAAC. Important features were selected using a novel MMC feature selection algorithm and the prediction was done using DNN as a classifier. The results showed that this method outperformed existing predictors in terms of accuracy.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

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Article Automation & Control Systems

Prediction of Antiviral peptides using transform evolutionary & SHAP analysis based descriptors by incorporation with ensemble learning strategy

Shahid Akbar et al.

Summary: The paper introduces an intelligent and computationally efficient learning approach for the reliable identification of AVPs. Novel evolutionary descriptors are explored and optimal features are selected using SHAP based global interpretation analysis. The model achieves a higher classification rate through the use of five different classifiers and an ensemble learner.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2022)

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Article Automation & Control Systems

DBP-DeepCNN: Prediction of DNA-binding proteins using wavelet-based denoising and deep learning

Farman Ali et al.

Summary: In this study, a deep learning-based predictor (DBP-DeepCNN) is proposed to improve the prediction of DNA-binding proteins (DBPs). By using a novel feature extraction method and training with various models, the predictor achieved higher accuracies on both training and independent datasets, indicating its potential for large scale DBP prediction and promising therapeutic strategies for chronic diseases.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2022)

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Article Multidisciplinary Sciences

Accurate Identification of DNA Replication Origin by Fusing Epigenomics and Chromatin Interaction Information

Fu-Yin Dao et al.

Summary: In this study, a computational approach named iORI-Epi was designed to recognize DNA replication origins (ORIs) by incorporating multiple features. The method displayed excellent performance in different cell lines and provided insights into the regulation of DNA replication initiation. Furthermore, gene enrichment pathways and the effect of promoters on replication initiation were discussed.

RESEARCH (2022)

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Article Automation & Control Systems

iDBP-PBMD: A machine learning model for detection of DNA-binding proteins by extending compression techniques into evolutionary profile

Ameen Banjar et al.

Summary: DNA-binding proteins (DBPs) play crucial roles in DNA transcription, recombination, and replication, and are associated with diseases like AIDS/HIV, cancer, and asthma. This research encoded DBPs using different feature descriptors and eliminated noisy and redundant features using compression techniques. The resulting features were used to train models with XGBoost and ERT classifiers. The study demonstrated the superiority of this approach over previous methods.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2022)

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Article Automation & Control Systems

Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks

Ashfaq Ahmad et al.

Summary: Fungal infections have become a serious global issue, making accurate identification of antifungal peptides challenging for researchers. A high-performance intelligent model, DeepAntiFP, was proposed and demonstrated to outperform existing computational models in antifungal peptide identification.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2021)

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Article Biology

iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model

Shahid Akbar et al.

Summary: This study proposes the use of an intelligent and reliable prediction model for identifying antitubercular peptides, using an ensemble learning approach to improve results. The approach aims to compensate for the shortcomings of individual classification algorithms and show promising results in accurately predicting tuberculosis.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

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Article Biology

AFP-CMBPred: Computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information

Farman Ali et al.

Summary: In extremely cold environments, organisms produce Antifreeze proteins to survive. A novel predictor AFP-CMBPred was developed based on machine learning for accurate prediction of these proteins, outperforming existing models in terms of prediction accuracy.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

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Article Computer Science, Artificial Intelligence

Risk Prediction of Diabetes: Big data mining with fusion of multifarious physical examination indicators

Hui Yang et al.

Summary: The study designed a computational system to predict diabetes risk by combining various types of physical examination data. Statistical analysis was conducted on different physical examination indexes to develop a model that can distinguish diabetes patients from healthy individuals. A diabetes risk scorecard was established to improve the convenience and flexibility of the model. Lastly, an online diabetes risk assessment system was set up to enhance diabetes cascade screening and personal lifestyle management.

INFORMATION FUSION (2021)

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Article Biochemical Research Methods

DBP-GAPred: An intelligent method for prediction of DNA-binding proteins types by enhanced evolutionary profile features with ensemble learning

Omar Barukab et al.

Summary: This study extends the feature extraction method for protein sequences and trains a model using SVM to improve the prediction performance of DNA-binding proteins. The new predictor achieved higher accuracies than existing predictors, demonstrating the superiority of the method.

JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (2021)

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Letter Oncology

Integrated biomarker profiling of the metabolome associated with impaired fasting glucose and type 2 diabetes mellitus in large-scale Chinese patients

Jianglan Long et al.

CLINICAL AND TRANSLATIONAL MEDICINE (2021)

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Article Biochemical Research Methods

SDBP-Pred: Prediction of single-stranded and double-stranded DNA-binding proteins by extending consensus sequence and K-segmentation strategies into PSSM

Farman Ali et al.

ANALYTICAL BIOCHEMISTRY (2020)

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Article Biochemistry & Molecular Biology

TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree

Muhammad Arif et al.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2020)

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Article Automation & Control Systems

iHBP-DeepPSSM: Identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach

Shahid Akbar et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2020)

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Article Biochemistry & Molecular Biology

DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information

Farman Ali et al.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2019)

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Article Automation & Control Systems

iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via Chou's 5-step rule and pseudo components

Zaheer Ullah Khan et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2019)

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Article Engineering, Biomedical

Brain tumor classification for MR images using transfer learning and fine-tuning

Zar Nawab Khan Swati et al.

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Prediction of membrane protein types by exploring local discriminative information from evolutionary profiles

Muhammad Kabir et al.

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Article Automation & Control Systems

iPredCNC: Computational prediction model for cancerlectins and non-cancerlectins using novel cascade features subset selection

Zaheer Ullah Khan et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2019)

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Article Automation & Control Systems

DBPPred-PDSD: Machine learning approach for prediction of DNA-binding proteins using Discrete Wavelet Transform and optimized integrated features space

Farman Ali et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2018)

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Article Mathematical & Computational Biology

Improving secretory proteins prediction in Mycobacterium tuberculosis using the unbiased dipeptide composition with support vector machine

Saeed Ahmed et al.

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS (2018)

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Article Medicine, Research & Experimental

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function

Bin Liu et al.

MOLECULAR THERAPY-NUCLEIC ACIDS (2017)

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iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset

Jianhua Jia et al.

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Machine learning approaches for discrimination of Extracellular Matrix proteins using hybrid feature space

Farman Ali et al.

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iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets

Jianhua Jia et al.

MOLECULES (2016)

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Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features

Longqiang Luo et al.

PLOS ONE (2016)

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Classification of membrane protein types using Voting Feature Interval in combination with Chou's Pseudo Amino Acid Composition

Farman Ali et al.

JOURNAL OF THEORETICAL BIOLOGY (2015)

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G-Bean: an ontology-graph based web tool for biomedical literature retrieval

James Z. Wang et al.

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Novel dimensions of piRNAs in cancer

Yuping Mei et al.

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NONCODE v3.0: integrative annotation of long noncoding RNAs

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A bi-layer model for identification of piwiRNA using deep neural learning

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