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Shah AA, Daud A, Bukhari A, Alshemaimri B, Ahsan M, Younis R. DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation. BMC Med Inform Decis Mak 2024; 24:198. [PMID: 39039464 DOI: 10.1186/s12911-024-02604-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 07/10/2024] [Indexed: 07/24/2024] Open
Abstract
Genes, expressed as sequences of nucleotides, are susceptible to mutations, some of which can lead to cancer. Machine learning and deep learning methods have emerged as vital tools in identifying mutations associated with cancer. Thyroid cancer ranks as the 5th most prevalent cancer in the USA, with thousands diagnosed annually. This paper presents an ensemble learning model leveraging deep learning techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Bi-directional LSTM (Bi-LSTM) to detect thyroid cancer mutations early. The model is trained on a dataset sourced from asia.ensembl.org and IntOGen.org, consisting of 633 samples with 969 mutations across 41 genes, collected from individuals of various demographics. Feature extraction encompasses techniques including Hahn moments, central moments, raw moments, and various matrix-based methods. Evaluation employs three testing methods: self-consistency test (SCT), independent set test (IST), and 10-fold cross-validation test (10-FCVT). The proposed ensemble learning model demonstrates promising performance, achieving 96% accuracy in the independent set test (IST). Statistical measures such as training accuracy, testing accuracy, recall, sensitivity, specificity, Mathew's Correlation Coefficient (MCC), loss, training accuracy, F1 Score, and Cohen's kappa are utilized for comprehensive evaluation.
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Affiliation(s)
- Asghar Ali Shah
- Center of Excellence in Artificial Intelligence (CoE-AI), Department of Computer Science, Bahria University, Islamabad, 04408, Pakistan
| | - Ali Daud
- Faculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab Emirates.
| | - Amal Bukhari
- Department of Information Systems and Technology, Collage of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Bader Alshemaimri
- Software Engineering Department, College of Computing and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Muhammad Ahsan
- Department of Computer Science, University of Alabama at Birmingham, 1402 10th Avenue S, Birmingham, AL, 35294, USA
| | - Rehmana Younis
- College of Letters and Sciences, Graduate Student of Robotics Engineering, Columbus State University, Columbus, USA
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2
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Adnan A, Hongya W, Ali F, Khalid M, Alghushairy O, Alsini R. A bi-layer model for identification of piwiRNA using deep neural learning. J Biomol Struct Dyn 2024; 42:5725-5733. [PMID: 37608578 DOI: 10.1080/07391102.2023.2243523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/15/2023] [Indexed: 08/24/2023]
Abstract
piwiRNA is a kind of non-coding RNA (ncRNA) that cannot be translated into proteins. It helps in understanding the study of gametes generation and regulation of gene expression over both transcriptional and post-transcriptional levels. piwiRNA has the function of instructing deadenylation, animal fertility, silencing transposons, fighting viruses, and regulating endogenous genes. Due to the great significance of piwiRNA, prediction of piwiRNA is essential for crucial cellular functions. Several predictors were established for prediction of piwiRNA. However, improving the prediction of piwiRNA is highly desirable. In the current study, we developed a more promising predictor named, BLP-piwiRNA. The features are explored by reverse complement k-mer, gapped-k-mer composition, and k-mer composition. The feature set of all descriptors is fused and the best features are selected by cascade and relief feature selection strategies. The best feature sets are provided to random forest (RF), deep neural network (DNN), and support vector machine (SVM). The models validation are examined by 10-fold test. DNN with optimal features of Cascade feature selection approach secured the highest prediction results. The results illustrate that BLP-piwiRNA effectively outperforms the existing studies. The proposed approach would be beneficial for both research community and drug development industry. BLP-piwiRNA would serve as novel biomarkers and therapeutic targets for tumor diagnostics and treatment.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Adnan Adnan
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Wang Hongya
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Farman Ali
- Department of Software Engineering, Sarhad University of Science and Information Technology, Peshawar, Pakistan
| | - Majdi Khalid
- Department of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Omar Alghushairy
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Raed Alsini
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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3
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Khalid M, Ali F, Alghamdi W, Alzahrani A, Alsini R, Alzahrani A. An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform. J Biomol Struct Dyn 2024:1-9. [PMID: 38498362 DOI: 10.1080/07391102.2024.2329777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/19/2024] [Indexed: 03/20/2024]
Abstract
Clathrin protein (CP) plays a pivotal role in numerous cellular processes, including endocytosis, signal transduction, and neuronal function. Dysregulation of CP has been associated with a spectrum of diseases. Given its involvement in various cellular functions, CP has garnered significant attention for its potential applications in drug design and medicine, ranging from targeted drug delivery to addressing viral infections, neurological disorders, and cancer. The accurate identification of CP is crucial for unraveling its function and devising novel therapeutic strategies. Computational methods offer a rapid, cost-effective, and less labor-intensive alternative to traditional identification methods, making them especially appealing for high-throughput screening. This paper introduces CL-Pred, a novel computational method for CP identification. CL-Pred leverages three feature descriptors: Dipeptide Deviation from Expected Mean (DDE), Bigram Position Specific Scoring Matrix (BiPSSM), and Position Specific Scoring Matrix-Tetra Slice-Discrete Cosine Transform (PSSM-TS-DCT). The model is trained using three classifiers: Support Vector Machine (SVM), Extremely Randomized Tree (ERT), and Light eXtreme Gradient Boosting (LiXGB). Notably, the LiXGB-based model achieves outstanding performance, demonstrating accuracies of 94.63% and 93.65% on the training and testing datasets, respectively. The proposed CL-Pred method is poised to significantly advance our comprehension of clathrin-mediated endocytosis, cellular physiology, and disease pathogenesis. Furthermore, it holds promise for identifying potential drug targets across a spectrum of diseases.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Majdi Khalid
- Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Farman Ali
- Sarhad University of Science and Information Technology Peshawar, Mardan Campus, Mardan, Pakistan
| | - Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdulrahman Alzahrani
- Department of Information System and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Raed Alsini
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed Alzahrani
- College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
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4
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Alsini R, Almuhaimeed A, Ali F, Khalid M, Farrash M, Masmoudi A. Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with two-dimensional convolutional neural network. J Biomol Struct Dyn 2024:1-11. [PMID: 38450715 DOI: 10.1080/07391102.2024.2323144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/16/2024] [Indexed: 03/08/2024]
Abstract
Vascular endothelial growth factor (VEGF) is involved in the development and progression of various diseases, including cancer, diabetic retinopathy, macular degeneration and arthritis. Understanding the role of VEGF in various disorders has led to the development of effective treatments, including anti-VEGF drugs, which have significantly improved therapeutic methods. Accurate VEGF identification is critical, yet experimental identification is expensive and time-consuming. This study presents Deep-VEGF, a novel computational model for VEGF prediction based on deep-stacked ensemble learning. We formulated two datasets using primary sequences. A novel feature descriptor named K-Space Tri Slicing-Bigram position-specific scoring metrix (KSTS-BPSSM) is constructed to extract numerical features from primary sequences. The model training is performed by deep learning techniques, including gated recurrent unit (GRU), generative adversarial network (GAN) and convolutional neural network (CNN). The GRU and CNN are ensembled using stacking learning approach. KSTS-BPSSM-based ensemble model secured the most accurate predictive outcomes, surpassing other competitive predictors across both training and testing datasets. This demonstrates the potential of leveraging deep learning for accurate VEGF prediction as a powerful tool to accelerate research, streamline drug discovery and uncover novel therapeutic targets. This insightful approach holds promise for expanding our knowledge of VEGF's role in health and disease.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Raed Alsini
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdullah Almuhaimeed
- Digital Health Institute, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
| | - Farman Ali
- Sarhad University of Science and Information Technology Peshawar, Mardan Campus, Pakistan
| | - Majdi Khalid
- Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Majed Farrash
- Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Atef Masmoudi
- College of Computer Science, King Khalid University, Abha, Saudi Arabia
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5
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Alghushairy O, Ali F, Alghamdi W, Khalid M, Alsini R, Asiry O. Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting. J Biomol Struct Dyn 2023:1-12. [PMID: 37850427 DOI: 10.1080/07391102.2023.2269280] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/04/2023] [Indexed: 10/19/2023]
Abstract
The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits. By identifying new druggable targets, researchers can develop new therapies for a range of diseases, leading to better patient outcomes. Identification of DPs by machine learning strategies is more efficient and cost-effective than conventional methods. In this study, a computational predictor, namely Drug-LXGB, is introduced to enhance the identification of DPs. Features are discovered by composition, transition, and distribution (CTD), composition of K-spaced amino acid pair (CKSAAP), pseudo-position-specific scoring matrix (PsePSSM), and a novel descriptor, called multi-block pseudo amino acid composition (MB-PseAAC). The dimensions of CTD, CKSAAP, PsePSSM, and MB-PseAAC are integrated and utilized the sequential forward selection as feature selection algorithm. The best characteristics are provided by random forest, extreme gradient boosting, and light eXtreme gradient boosting (LXGB). The predictive analysis of these learning methods is measured via 10-fold cross-validation. The LXGB-based model secures the highest results than other existing predictors. Our novel protocol will perform an active role in designing novel drugs and would be fruitful to explore the potential target. This study will help better to capture a more universal view of a potential target.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Omar Alghushairy
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Farman Ali
- Department of Software Engineering, Sarhad University of Science and Information Technology Peshawar Mardan Campus, Peshawar, Pakistan
| | - Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Majdi Khalid
- Department of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Raed Alsini
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Othman Asiry
- Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah, Saudi Arabia
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6
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Ali F, Alghamdi W, Almagrabi AO, Alghushairy O, Banjar A, Khalid M. Deep-AGP: Prediction of angiogenic protein by integrating two-dimensional convolutional neural network with discrete cosine transform. Int J Biol Macromol 2023; 243:125296. [PMID: 37301349 DOI: 10.1016/j.ijbiomac.2023.125296] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 06/12/2023]
Abstract
Angiogenic proteins (AGPs) play a primary role in the formation of new blood vessels from pre-existing ones. AGPs have diverse applications in cancer, including serving as biomarkers, guiding anti-angiogenic therapies, and aiding in tumor imaging. Understanding the role of AGPs in cardiovascular and neurodegenerative diseases is vital for developing new diagnostic tools and therapeutic approaches. Considering the significance of AGPs, in this research, we first time established a computational model using deep learning for identifying AGPs. First, we constructed a sequence-based dataset. Second, we explored features by designing a novel feature encoder, called position-specific scoring matrix-decomposition-discrete cosine transform (PSSM-DC-DCT) and existing descriptors including Dipeptide Deviation from Expected Mean (DDE) and bigram-position-specific scoring matrix (Bi-PSSM). Third, each feature set is fed into two-dimensional convolutional neural network (2D-CNN) and machine learning classifiers. Finally, the performance of each learning model is validated by 10-fold cross-validation (CV). The experimental results demonstrate that 2D-CNN with proposed novel feature descriptor achieved the highest success rate on both training and testing datasets. In addition to being an accurate predictor for identification of angiogenic proteins, our proposed method (Deep-AGP) might be fruitful in understanding cancer, cardiovascular, and neurodegenerative diseases, development of their novel therapeutic methods and drug designing.
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Affiliation(s)
- Farman Ali
- Sarhad University of Science and Information Technology Peshawar, Mardan Campus, Pakistan.
| | - Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Alaa Omran Almagrabi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
| | - Omar Alghushairy
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Ameen Banjar
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Majdi Khalid
- Department of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia
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7
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Butt AH, Alkhalifah T, Alturise F, Khan YD. Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum. Diagnostics (Basel) 2023; 13:diagnostics13111940. [PMID: 37296792 DOI: 10.3390/diagnostics13111940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
Hormone-binding proteins (HBPs) are specific carrier proteins that bind to a given hormone. A soluble carrier hormone binding protein (HBP), which can interact non-covalently and specifically with growth hormone, modulates or inhibits hormone signaling. HBP is essential for the growth of life, despite still being poorly understood. Several diseases, according to some data, are caused by HBPs that express themselves abnormally. Accurate identification of these molecules is the first step in investigating the roles of HBPs and understanding their biological mechanisms. For a better understanding of cell development and cellular mechanisms, accurate HBP determination from a given protein sequence is essential. Using traditional biochemical experiments, it is difficult to correctly separate HBPs from an increasing number of proteins because of the high experimental costs and lengthy experiment periods. The abundance of protein sequence data that has been gathered in the post-genomic era necessitates a computational method that is automated and enables quick and accurate identification of putative HBPs within a large number of candidate proteins. A brand-new machine-learning-based predictor is suggested as the HBP identification method. To produce the desirable feature set for the method proposed, statistical moment-based features and amino acids were combined, and the random forest was used to train the feature set. During 5-fold cross validation experiments, the suggested method achieved 94.37% accuracy and 0.9438 F1-scores, respectively, demonstrating the importance of the Hahn moment-based features.
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Affiliation(s)
- Ahmad Hassan Butt
- Department of Computer Science, Faculty of Computing & Information Technology, University of the Punjab, Lahore 54000, Pakistan
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 51921, Qassim, Saudi Arabia
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 51921, Qassim, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan
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8
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Ali F, Kumar H, Alghamdi W, Kateb FA, Alarfaj FK. Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1-12. [PMID: 37359746 PMCID: PMC10148704 DOI: 10.1007/s11831-023-09933-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
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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Affiliation(s)
- Farman Ali
- Sarhad University of Science and Information Technology Peshawar, Mardan Campus, Khyber Pakhtunkhwa, Pakistan
| | - Harish Kumar
- Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589 Saudi Arabia
| | - Faris A. Kateb
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589 Saudi Arabia
| | - Fawaz Khaled Alarfaj
- Department of Management Information Systems, King Faisal University, Hufof, Saudi Arabia
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9
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Zulfiqar H, Guo Z, Grace-Mercure BK, Zhang ZY, Gao H, Lin H, Wu Y. Empirical Comparison and Recent Advances of Computational Prediction of Hormone Binding Proteins Using Machine Learning Methods. Comput Struct Biotechnol J 2023; 21:2253-2261. [PMID: 37035551 PMCID: PMC10073991 DOI: 10.1016/j.csbj.2023.03.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 03/19/2023] Open
Abstract
Hormone binding proteins (HBPs) belong to the group of soluble carrier proteins. These proteins selectively and non-covalently interact with hormones and promote growth hormone signaling in human and other animals. The HBPs are useful in many medical and commercial fields. Thus, the identification of HBPs is very important because it can help to discover more details about hormone binding proteins. Meanwhile, the experimental methods are time-consuming and expensive for hormone binding proteins recognition. Computational prediction methods have played significant roles in the correct recognition of hormone binding proteins with the use of sequence information and ML algorithms. In this review, we compared and assessed the implementation of ML-based tools in recognition of HBPs in a unique way. We hope that this study will give enough awareness and knowledge for research on HBPs.
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Khan A, Uddin J, Ali F, Kumar H, Alghamdi W, Ahmad A. AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree. J Chem Inf Model 2023; 63:826-834. [PMID: 36649569 DOI: 10.1021/acs.jcim.2c01417] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
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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Affiliation(s)
- Adnan Khan
- Qurtuba University of Science and Information Technology, Peshawar5000, Khyber Pakhtunkhwa, Pakistan
| | - Jamal Uddin
- Qurtuba University of Science and Information Technology, Peshawar5000, Khyber Pakhtunkhwa, Pakistan
| | - Farman Ali
- Sarhad University of Science and Information Technology, Mardan Campus, Peshawar23200, Pakistan.,Department of Elementary and Secondary Education Department, Government of Khyber Pakhtunkhwa, Peshawar5000, Khyber Pakhtunkhwa, Pakistan
| | - Harish Kumar
- Department of Computer Science, College of Computer Science, King Khalid University, Abha61421, Saudi Arabia
| | - Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King AbdulAziz University, Jeddah21589, Saudi Arabia
| | - Aftab Ahmad
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan23200, Pakistan
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11
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Khan A, Uddin J, Ali F, Ahmad A, Alghushairy O, Banjar A, Daud A. Prediction of antifreeze proteins using machine learning. Sci Rep 2022; 12:20672. [PMID: 36450775 PMCID: PMC9712683 DOI: 10.1038/s41598-022-24501-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 11/16/2022] [Indexed: 12/03/2022] Open
Abstract
Living organisms including fishes, microbes, and animals can live in extremely cold weather. To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also referred to as ice-binding proteins. Moreover, AFPs are extensively utilized in many important fields including medical, agricultural, industrial, and biotechnological. Several predictors were constructed to identify AFPs. However, due to the sequence and structural heterogeneity of AFPs, correct identification is still a challenging task. It is highly desirable to develop a more promising predictor. In this research, a novel computational method, named AFP-LXGB has been proposed for prediction of AFPs more precisely. The information is explored by Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Position Specific Scoring Matrix-Segmentation-Autocorrelation Transformation (Sg-PSSM-ACT), and Pseudo Position Specific Scoring Matrix Tri-Slicing (PseTS-PSSM). Keeping the benefits of ensemble learning, these feature sets are concatenated into different combinations. The best feature set is selected by Extremely Randomized Tree-Recursive Feature Elimination (ERT-RFE). The models are trained by Light eXtreme Gradient Boosting (LXGB), Random Forest (RF), and Extremely Randomized Tree (ERT). Among classifiers, LXGB has obtained the best prediction results. The novel method (AFP-LXGB) improved the accuracies by 3.70% and 4.09% than the best methods. These results verified that AFP-LXGB can predict AFPs more accurately and can participate in a significant role in medical, agricultural, industrial, and biotechnological fields.
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Affiliation(s)
- Adnan Khan
- grid.444994.00000 0004 0609 284XQurtuba University of Science and Technology, Peshawar, Khyber Pakhtunkhwa Pakistan
| | - Jamal Uddin
- grid.444994.00000 0004 0609 284XQurtuba University of Science and Technology, Peshawar, Khyber Pakhtunkhwa Pakistan
| | - Farman Ali
- Department of Elementary and Secondary Education, Peshawar, Khyber Pakhtunkhwa Pakistan ,grid.444996.20000 0004 0609 292XSarhad University of Science and Information Technology, Mardan, Pakistan
| | - Ashfaq Ahmad
- grid.440522.50000 0004 0478 6450Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Omar Alghushairy
- grid.460099.2Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Ameen Banjar
- grid.460099.2Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Ali Daud
- Abu Dhabi School of Management, Abu Dhabi, United Arab Emirates ,grid.460099.2Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah, Saudi Arabia
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12
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DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2987407. [PMID: 36211019 PMCID: PMC9534628 DOI: 10.1155/2022/2987407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/19/2022] [Accepted: 09/09/2022] [Indexed: 11/17/2022]
Abstract
DNA-binding proteins (DBPs) have crucial biotic activities including DNA replication, recombination, and transcription. DBPs are highly concerned with chronic diseases and are used in the manufacturing of antibiotics and steroids. A series of predictors were established to identify DBPs. However, researchers are still working to further enhance the identification of DBPs. This research designed a novel predictor to identify DBPs more accurately. The features from the sequences are transformed by F-PSSM (Filtered position-specific scoring matrix), PSSM-DPC (Position specific scoring matrix-dipeptide composition), and R-PSSM (Reduced position-specific scoring matrix). To eliminate the noisy attributes, we extended DWT (discrete wavelet transform) to F-PSSM, PSSM-DPC, and R-PSSM and introduced three novel descriptors, namely, F-PSSM-DWT, PSSM-DPC-DWT, and R-PSSM-DWT. Onward, the training of the four models were performed using LiXGB (Light eXtreme gradient boosting), XGB (eXtreme gradient boosting, ERT (extremely randomized trees), and Adaboost. LiXGB with R-PSSM-DWT has attained 6.55% higher accuracy on training and 5.93% on testing dataset than the best existing predictors. The results reveal the excellent performance of our novel predictor over the past studies. DBP-iDWT would be fruitful for establishing more operative therapeutic strategies for fatal disease treatment.
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