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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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2
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Ali F, Almuhaimeed A, Khalid M, Alshanbari H, Masmoudi A, Alsini R. DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery. Methods 2024; 226:49-53. [PMID: 38621436 DOI: 10.1016/j.ymeth.2024.04.004] [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: 02/27/2024] [Revised: 04/06/2024] [Accepted: 04/08/2024] [Indexed: 04/17/2024] Open
Abstract
Epigenetic proteins (EP) play a role in the progression of a wide range of diseases, including autoimmune disorders, neurological disorders, and cancer. Recognizing their different functions has prompted researchers to investigate them as potential therapeutic targets and pharmacological targets. This paper proposes a novel deep learning-based model that accurately predicts EP. This study introduces a novel deep learning-based model that accurately predicts EP. Our approach entails generating two distinct datasets for training and evaluating the model. We then use three distinct strategies to transform protein sequences to numerical representations: Dipeptide Deviation from Expected Mean (DDE), Dipeptide Composition (DPC), and Group Amino Acid (GAAC). Following that, we train and compare the performance of four advanced deep learning models algorithms: Ensemble Residual Convolutional Neural Network (ERCNN), Generative Adversarial Network (GAN), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). The DDE encoding combined with the ERCNN model demonstrates the best performance on both datasets. This study demonstrates deep learning's potential for precisely predicting EP, which can considerably accelerate research and streamline drug discovery efforts. This analytical method has the potential to find new therapeutic targets and advance our understanding of EP activities in disease.
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Affiliation(s)
- Farman Ali
- Department of Computer Science, Bahria University Islamabad Campus, Pakistan.
| | - Abdullah Almuhaimeed
- Digital Health Institute, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia
| | - Majdi Khalid
- Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah 21955, Saudi Arabia
| | - Hanan Alshanbari
- Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah 21955, Saudi Arabia
| | - Atef Masmoudi
- College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Raed Alsini
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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3
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Chu H, Liu T. Comprehensive Research on Druggable Proteins: From PSSM to Pre-Trained Language Models. Int J Mol Sci 2024; 25:4507. [PMID: 38674091 PMCID: PMC11049818 DOI: 10.3390/ijms25084507] [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: 03/21/2024] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
Identification of druggable proteins can greatly reduce the cost of discovering new potential drugs. Traditional experimental approaches to exploring these proteins are often costly, slow, and labor-intensive, making them impractical for large-scale research. In response, recent decades have seen a rise in computational methods. These alternatives support drug discovery by creating advanced predictive models. In this study, we proposed a fast and precise classifier for the identification of druggable proteins using a protein language model (PLM) with fine-tuned evolutionary scale modeling 2 (ESM-2) embeddings, achieving 95.11% accuracy on the benchmark dataset. Furthermore, we made a careful comparison to examine the predictive abilities of ESM-2 embeddings and position-specific scoring matrix (PSSM) features by using the same classifiers. The results suggest that ESM-2 embeddings outperformed PSSM features in terms of accuracy and efficiency. Recognizing the potential of language models, we also developed an end-to-end model based on the generative pre-trained transformers 2 (GPT-2) with modifications. To our knowledge, this is the first time a large language model (LLM) GPT-2 has been deployed for the recognition of druggable proteins. Additionally, a more up-to-date dataset, known as Pharos, was adopted to further validate the performance of the proposed model.
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Affiliation(s)
| | - Taigang Liu
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China;
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4
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Arif M, Fang G, Ghulam A, Musleh S, Alam T. DPI_CDF: druggable protein identifier using cascade deep forest. BMC Bioinformatics 2024; 25:145. [PMID: 38580921 DOI: 10.1186/s12859-024-05744-3] [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: 06/01/2023] [Accepted: 03/13/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory. METHODS In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF. RESULTS The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew's-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process. AVAILABILITY The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .
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Affiliation(s)
- Muhammad Arif
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Ge Fang
- State Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing 210023, P. R. China, Nanjing 210023, China
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bankok, 10700, Thailand
| | - Ali Ghulam
- Information Technology Centre, Sindh Agriculture University, Sindh, Pakistan
| | - Saleh Musleh
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Tanvir Alam
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
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5
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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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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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7
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Akbar S, Raza A, Zou Q. Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model. BMC Bioinformatics 2024; 25:102. [PMID: 38454333 PMCID: PMC10921744 DOI: 10.1186/s12859-024-05726-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/01/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Viral infections have been the main health issue in the last decade. Antiviral peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to protect the human body against various viral diseases. However, there has been significant production of antiviral vaccines and medications. Recently, the development of AVPs as an antiviral agent suggests an effective way to treat virus-affected cells. Recently, the involvement of intelligent machine learning techniques for developing peptide-based therapeutic agents is becoming an increasing interest due to its significant outcomes. The existing wet-laboratory-based drugs are expensive, time-consuming, and cannot effectively perform in screening and predicting the targeted motif of antiviral peptides. METHODS In this paper, we proposed a novel computational model called Deepstacked-AVPs to discriminate AVPs accurately. The training sequences are numerically encoded using a novel Tri-segmentation-based position-specific scoring matrix (PSSM-TS) and word2vec-based semantic features. Composition/Transition/Distribution-Transition (CTDT) is also employed to represent the physiochemical properties based on structural features. Apart from these, the fused vector is formed using PSSM-TS features, semantic information, and CTDT descriptors to compensate for the limitations of single encoding methods. Information gain (IG) is applied to choose the optimal feature set. The selected features are trained using a stacked-ensemble classifier. RESULTS The proposed Deepstacked-AVPs model achieved a predictive accuracy of 96.60%%, an area under the curve (AUC) of 0.98, and a precision-recall (PR) value of 0.97 using training samples. In the case of the independent samples, our model obtained an accuracy of 95.15%, an AUC of 0.97, and a PR value of 0.97. CONCLUSION Our Deepstacked-AVPs model outperformed existing models with a ~ 4% and ~ 2% higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed Deepstacked-AVPs model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia.
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Affiliation(s)
- Shahid Akbar
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, 23200, KP, Pakistan
| | - Ali Raza
- Department of Physical and Numerical Sciences, Qurtuba University of Science and Information Technology, Peshawar, 25124, KP, Pakistan
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, People's Republic of China.
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8
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Emami N, Ferdousi R. HormoNet: a deep learning approach for hormone-drug interaction prediction. BMC Bioinformatics 2024; 25:87. [PMID: 38418979 PMCID: PMC10903040 DOI: 10.1186/s12859-024-05708-7] [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: 12/05/2023] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is essential to understand the hormone-drug associations. Here, we present HormoNet to predict the HDI pairs and their risk level by integrating features derived from hormone and drug target proteins. To the best of our knowledge, this is one of the first attempts to employ deep learning approach for prediction of HDI prediction. Amino acid composition and pseudo amino acid composition were applied to represent target information using 30 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied synthetic minority over-sampling technique technique. Additionally, we constructed novel datasets for HDI prediction and the risk level of their interaction. HormoNet achieved high performance on our constructed hormone-drug benchmark datasets. The results provide insights into the understanding of the relationship between hormone and a drug, and indicate the potential benefit of reducing risk levels of interactions in designing more effective therapies for patients in drug treatments. Our benchmark datasets and the source codes for HormoNet are available in: https://github.com/EmamiNeda/HormoNet .
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Affiliation(s)
- Neda Emami
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
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9
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Chen J, Gu Z, Lai L, Pei J. In silico protein function prediction: the rise of machine learning-based approaches. MEDICAL REVIEW (2021) 2023; 3:487-510. [PMID: 38282798 PMCID: PMC10808870 DOI: 10.1515/mr-2023-0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/11/2023] [Indexed: 01/30/2024]
Abstract
Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.
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Affiliation(s)
- Jiaxiao Chen
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Zhonghui Gu
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
- Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, China
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10
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Beltrán JF, Belén LH, Farias JG, Zamorano M, Lefin N, Miranda J, Parraguez-Contreras F. VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation. Brief Bioinform 2023; 25:bbad434. [PMID: 38033292 PMCID: PMC10753651 DOI: 10.1093/bib/bbad434] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 10/18/2023] [Accepted: 11/05/2023] [Indexed: 12/02/2023] Open
Abstract
Throughout evolution, pathogenic viruses have developed different strategies to evade the response of the adaptive immune system. To carry out successful replication, some pathogenic viruses encode different proteins that manipulate the molecular mechanisms of host cells. Currently, there are different bioinformatics tools for virus research; however, none of them focus on predicting viral proteins that evade the adaptive system. In this work, we have developed a novel tool based on machine and deep learning for predicting this type of viral protein named VirusHound-I. This tool is based on a model developed with the multilayer perceptron algorithm using the dipeptide composition molecular descriptor. In this study, we have also demonstrated the robustness of our strategy for data augmentation of the positive dataset based on generative antagonistic networks. During the 10-fold cross-validation step in the training dataset, the predictive model showed 0.947 accuracy, 0.994 precision, 0.943 F1 score, 0.995 specificity, 0.896 sensitivity, 0.894 kappa, 0.898 Matthew's correlation coefficient and 0.989 AUC. On the other hand, during the testing step, the model showed 0.964 accuracy, 1.0 precision, 0.967 F1 score, 1.0 specificity, 0.936 sensitivity, 0.929 kappa, 0.931 Matthew's correlation coefficient and 1.0 AUC. Taking this model into account, we have developed a tool called VirusHound-I that makes it possible to predict viral proteins that evade the host's adaptive immune system. We believe that VirusHound-I can be very useful in accelerating studies on the molecular mechanisms of evasion of pathogenic viruses, as well as in the discovery of therapeutic targets.
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Affiliation(s)
- Jorge F Beltrán
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
| | | | - Jorge G Farias
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
| | - Mauricio Zamorano
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
| | - Nicolás Lefin
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
| | - Javiera Miranda
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
| | - Fernanda Parraguez-Contreras
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
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11
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Raza A, Uddin J, Almuhaimeed A, Akbar S, Zou Q, Ahmad A. AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks. J Chem Inf Model 2023; 63:6537-6554. [PMID: 37905969 DOI: 10.1021/acs.jcim.3c01563] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Inflammation is a biologically resistant response to harmful stimuli, such as infection, damaged cells, toxic chemicals, or tissue injuries. Its purpose is to eradicate pathogenic micro-organisms or irritants and facilitate tissue repair. Prolonged inflammation can result in chronic inflammatory diseases. However, wet-laboratory-based treatments are costly and time-consuming and may have adverse side effects on normal cells. In the past decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, we developed a highly discriminative prediction model called AIPs-SnTCN to predict anti-inflammatory peptides accurately. The peptide samples are encoded using word embedding techniques such as skip-gram and attention-based bidirectional encoder representation using a transformer (BERT). The conjoint triad feature (CTF) also collects structure-based cluster profile features. The fused vector of word embedding and sequential features is formed to compensate for the limitations of single encoding methods. Support vector machine-based recursive feature elimination (SVM-RFE) is applied to choose the ranking-based optimal space. The optimized feature space is trained by using an improved self-normalized temporal convolutional network (SnTCN). The AIPs-SnTCN model achieved a predictive accuracy of 95.86% and an AUC of 0.97 by using training samples. In the case of the alternate training data set, our model obtained an accuracy of 92.04% and an AUC of 0.96. The proposed AIPs-SnTCN model outperformed existing models with an ∼19% higher accuracy and an ∼14% higher AUC value. The reliability and efficacy of our AIPs-SnTCN model make it a valuable tool for scientists and may play a beneficial role in pharmaceutical design and research academia.
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Affiliation(s)
- Ali Raza
- Department of Physical and Numerical Sciences, Qurtuba University of Science and Information Technology, Peshawar, Khyber Pakhtunkhwa 25124, Pakistan
- Department of Computer Science, MY University, Islamabad 45750, Pakistan
| | - Jamal Uddin
- Department of Physical and Numerical Sciences, Qurtuba University of Science and Information Technology, Peshawar, Khyber Pakhtunkhwa 25124, Pakistan
| | - Abdullah Almuhaimeed
- Digital Health Institute, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia
| | - Shahid Akbar
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Khyber Pakhtunkhwa 23200, Pakistan
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, PR China
| | - Ashfaq Ahmad
- Department of Computer Science, MY University, Islamabad 45750, Pakistan
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12
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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: 5] [Impact Index Per Article: 5.0] [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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13
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Sun J, Wu S, Mou Z, Wen J, Wei H, Zou J, Li Q, Liu Z, Xu SH, Kang M, Ling Q, Huang H, Chen X, Wang Y, Liao X, Tan G, Shao Y. Prediction model of ocular metastasis from primary liver cancer: Machine learning-based development and interpretation study. Cancer Med 2023; 12:20482-20496. [PMID: 37795569 PMCID: PMC10652349 DOI: 10.1002/cam4.6540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 08/21/2023] [Accepted: 09/05/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML). METHODS We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non-ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10-fold cross-validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM. RESULTS Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA. CONCLUSION We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients.
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Affiliation(s)
- Jin‐Qi Sun
- Fuxing Hospital, The Eighth Clinical Medical CollegeCapital Medical UniversityBeijingPeople's Republic of China
| | - Shi‐Nan Wu
- Department of OphthalmologyThe First Affiliated Hospital of Nanchang University, Jiangxi Branch of the National Clinical Research Center for Ocular DiseaseNanchangPeople's Republic of China
- Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Eye Institute of Xiamen UniversitySchool of Medicine, Xiamen UniversityXiamenPeople's Republic of China
| | - Zheng‐Lin Mou
- Department of OphthalmologyThe First Affiliated Hospital of Nanchang University, Jiangxi Branch of the National Clinical Research Center for Ocular DiseaseNanchangPeople's Republic of China
| | - Jia‐Yi Wen
- Department of OphthalmologyThe First Affiliated Hospital of Nanchang University, Jiangxi Branch of the National Clinical Research Center for Ocular DiseaseNanchangPeople's Republic of China
| | - Hong Wei
- Department of OphthalmologyThe First Affiliated Hospital of Nanchang University, Jiangxi Branch of the National Clinical Research Center for Ocular DiseaseNanchangPeople's Republic of China
| | - Jie Zou
- Department of OphthalmologyThe First Affiliated Hospital of Nanchang University, Jiangxi Branch of the National Clinical Research Center for Ocular DiseaseNanchangPeople's Republic of China
| | - Qing‐Jian Li
- Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Eye Institute of Xiamen UniversitySchool of Medicine, Xiamen UniversityXiamenPeople's Republic of China
| | - Zhao‐Lin Liu
- Department of OphthalmologyThe First Affiliated Hospital of University of South China, Hunan Branch of The National Clinical Research Center for Ocular DiseaseHengyangPeople's Republic of China
| | - San Hua Xu
- Department of OphthalmologyThe First Affiliated Hospital of Nanchang University, Jiangxi Branch of the National Clinical Research Center for Ocular DiseaseNanchangPeople's Republic of China
| | - Min Kang
- Department of OphthalmologyThe First Affiliated Hospital of Nanchang University, Jiangxi Branch of the National Clinical Research Center for Ocular DiseaseNanchangPeople's Republic of China
| | - Qian Ling
- Department of OphthalmologyThe First Affiliated Hospital of Nanchang University, Jiangxi Branch of the National Clinical Research Center for Ocular DiseaseNanchangPeople's Republic of China
| | - Hui Huang
- Department of OphthalmologyThe First Affiliated Hospital of Nanchang University, Jiangxi Branch of the National Clinical Research Center for Ocular DiseaseNanchangPeople's Republic of China
| | - Xu Chen
- Department of Ophthalmology and Visual SciencesMaastricht UniversityMaastrichtNetherlands
| | - Yi‐Xin Wang
- School of Optometry and Vision SciencesCardiff UniversityCardiffUK
| | - Xu‐Lin Liao
- Department of Ophthalmology and Visual SciencesThe Chinese University of Hong KongHong KongPeople's Republic of China
| | - Gang Tan
- Department of OphthalmologyThe First Affiliated Hospital of University of South China, Hunan Branch of The National Clinical Research Center for Ocular DiseaseHengyangPeople's Republic of China
| | - Yi Shao
- Department of OphthalmologyThe First Affiliated Hospital of Nanchang University, Jiangxi Branch of the National Clinical Research Center for Ocular DiseaseNanchangPeople's Republic of China
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14
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Shoombuatong W, Schaduangrat N, Nikom J. Empirical comparison and analysis of machine learning-based approaches for druggable protein identification. EXCLI JOURNAL 2023; 22:915-927. [PMID: 37780939 PMCID: PMC10539545 DOI: 10.17179/excli2023-6410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 08/15/2023] [Indexed: 10/03/2023]
Abstract
Efficiently and precisely identifying drug targets is crucial for developing and discovering potential medications. While conventional experimental approaches can accurately pinpoint these targets, they suffer from time constraints and are not easily adaptable to high-throughput processes. On the other hand, computational approaches, particularly those utilizing machine learning (ML), offer an efficient means to accelerate the prediction of druggable proteins based solely on their primary sequences. Recently, several state-of-the-art computational methods have been developed for predicting and analyzing druggable proteins. These computational methods showed high diversity in terms of benchmark datasets, feature extraction schemes, ML algorithms, evaluation strategies and webserver/software usability. Thus, our objective is to reexamine these computational approaches and conduct a comprehensive assessment of their strengths and weaknesses across multiple aspects. In this study, we deliver the first comprehensive survey regarding the state-of-the-art computational approaches for in silico prediction of druggable proteins. First, we provided information regarding the existing benchmark datasets and the types of ML methods employed. Second, we investigated the effectiveness of these computational methods in druggable protein identification for each benchmark dataset. Third, we summarized the important features used in this field and the existing webserver/software. Finally, we addressed the present constraints of the existing methods and offer valuable guidance to the scientific community in designing and developing novel prediction models. We anticipate that this comprehensive review will provide crucial information for the development of more accurate and efficient druggable protein predictors.
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Affiliation(s)
- Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
| | - Jaru Nikom
- Research Methodology and Data Analytics Program, Faculty of Science & Technology, Prince of Songkla University, Pattani, Thailand, 94000
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15
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Cunningham M, Pins D, Dezső Z, Torrent M, Vasanthakumar A, Pandey A. PINNED: identifying characteristics of druggable human proteins using an interpretable neural network. J Cheminform 2023; 15:64. [PMID: 37468968 PMCID: PMC10354961 DOI: 10.1186/s13321-023-00735-7] [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: 03/23/2023] [Accepted: 07/10/2023] [Indexed: 07/21/2023] Open
Abstract
The identification of human proteins that are amenable to pharmacologic modulation without significant off-target effects remains an important unsolved challenge. Computational methods have been devised to identify features which distinguish between "druggable" and "undruggable" proteins, finding that protein sequence, tissue and cellular localization, biological role, and position in the protein-protein interaction network are all important discriminant factors. However, many prior efforts to automate the assessment of protein druggability suffer from low performance or poor interpretability. We developed a neural network-based machine learning model capable of generating druggability sub-scores based on each of four distinct categories, combining them to form an overall druggability score. The model achieves an excellent performance in separating drugged and undrugged proteins in the human proteome, with an area under the receiver operating characteristic (AUC) of 0.95. Our use of multiple sub-scores allows the assessment of potential protein targets of interest based on distinct contributors to druggability, leading to a more interpretable and holistic model to identify novel targets.
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Affiliation(s)
- Michael Cunningham
- Genomics Research Center, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL, 60064, USA.
| | - Danielle Pins
- Information Research, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL, 60064, USA
| | - Zoltán Dezső
- Genomics Research Center, AbbVie Inc., 1000 Gateway Boulevard, South San Francisco, CA, 94080, USA
| | - Maricel Torrent
- Small Molecule Therapeutics and Platform Technologies, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL, 60064, USA
| | - Aparna Vasanthakumar
- Genomics Research Center, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL, 60064, USA
| | - Abhishek Pandey
- Information Research, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL, 60064, USA
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16
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Madugula SS, Pandey S, Amalapurapu S, Bozdag S. NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool. ACS OMEGA 2023; 8:20379-20388. [PMID: 37323377 PMCID: PMC10268018 DOI: 10.1021/acsomega.3c00286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 05/09/2023] [Indexed: 06/17/2023]
Abstract
The nuclear receptor (NR) superfamily includes phylogenetically related ligand-activated proteins, which play a key role in various cellular activities. NR proteins are subdivided into seven subfamilies based on their function, mechanism, and nature of the interacting ligand. Developing robust tools to identify NR could give insights into their functional relationships and involvement in disease pathways. Existing NR prediction tools only use a few types of sequence-based features and are tested on relatively similar independent datasets; thus, they may suffer from overfitting when extended to new genera of sequences. To address this problem, we developed Nuclear Receptor Prediction Tool (NRPreTo), a two-level NR prediction tool with a unique training approach where in addition to the sequence-based features used by existing NR prediction tools, six additional feature groups depicting various physiochemical, structural, and evolutionary features of proteins were utilized. The first level of NRPreTo allows for the successful prediction of a query protein as NR or non-NR and further subclassifies the protein into one of the seven NR subfamilies in the second level. We developed Random Forest classifiers to test on benchmark datasets, as well as the entire human protein datasets from RefSeq and Human Protein Reference Database (HPRD). We observed that using additional feature groups improved the performance. We also observed that NRPreTo achieved high performance on the external datasets and predicted 59 novel NRs in the human proteome. The source code of NRPreTo is publicly available at https://github.com/bozdaglab/NRPreTo.
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Affiliation(s)
- Sita Sirisha Madugula
- Department
of Computer Science & Engineering, University
of North Texas, Denton, Texas TX 76203, United States
| | - Suman Pandey
- Department
of Computer Science & Engineering, University
of North Texas, Denton, Texas TX 76203, United States
| | - Shreya Amalapurapu
- Department
of Computer Science & Engineering, University
of North Texas, Denton, Texas TX 76203, United States
- The
Texas Academy of Mathematics and Science, University of North Texas, Denton, Texas TX 76203, United States
| | - Serdar Bozdag
- Department
of Computer Science & Engineering, University
of North Texas, Denton, Texas TX 76203, United States
- Department
of Mathematics, University of North Texas, Denton, Texas TX 76203, United
States
- BioDiscovery
Institute, University of North Texas, Denton, Texas TX 76203, United States
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17
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Wang DC, Xu WD, Wang SN, Wang X, Leng W, Fu L, Liu XY, Qin Z, Huang AF. Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis. Inflamm Res 2023:10.1007/s00011-023-01755-7. [PMID: 37300586 DOI: 10.1007/s00011-023-01755-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
OBJECTIVE Diagnosis of lupus nephritis (LN) is a complex process, which usually requires renal biopsy. We aim to establish a machine learning pipeline to help diagnosis of LN. METHODS A cohort of 681 systemic lupus erythematosus (SLE) patients without LN and 786 SLE patients with LN was established, and a total of 95 clinical, laboratory data and 17 meteorological indicators were collected. After tenfold cross-validation, the patients were divided into training set and test set. The features selected by collective feature selection method of mutual information (MI) and multisurf were used to construct the models of logistic regression, decision tree, random forest, naive Bayes, support vector machine (SVM), light gradient boosting (LGB), extreme gradient boosting (XGB), and artificial neural network (ANN), the models were compared and verified in post-analysis. RESULTS Collective feature selection method screens out antistreptolysin (ASO), retinol binding protein (RBP), lupus anticoagulant 1 (LA1), LA2, proteinuria and other features, and the hyperparameter optimized XGB (ROC: AUC = 0.995; PRC: AUC = 1.000, APS = 1.000; balance accuracy: 0.990) has the best performance, followed by LGB (ROC: AUC = 0.992; PRC: AUC = 0.997, APS = 0.977; balance accuracy: 0.957). The worst performance is naive Bayes model (ROC: AUC = 0.799; PRC: AUC = 0.822, APS = 0.823; balance accuracy: 0.693). In the composite feature importance bar plots, ASO, RF, Up/Ucr, and other features play important roles in LN. CONCLUSION We developed and validated a new and simple machine learning pathway for diagnosis of LN, especially the XGB model based on ASO, LA1, LA2, proteinuria, and other features screened out by collective feature selection.
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Affiliation(s)
- Da-Cheng Wang
- Department of Evidence-Based Medicine, Southwest Medical University, 1 Xianglin Road, Luzhou, Sichuan, China
| | - Wang-Dong Xu
- Department of Evidence-Based Medicine, Southwest Medical University, 1 Xianglin Road, Luzhou, Sichuan, China
| | - Shen-Nan Wang
- Luzhou Meteorological Bureau, 3 Songshan Road, Luzhou, Sichuan, China
| | - Xiang Wang
- Luzhou Meteorological Bureau, 3 Songshan Road, Luzhou, Sichuan, China
| | - Wei Leng
- Luzhou Meteorological Bureau, 3 Songshan Road, Luzhou, Sichuan, China
| | - Lu Fu
- Laboratory Animal Center, Southwest Medical University, 1 Xianglin Road, Luzhou, Sichuan, China
| | - Xiao-Yan Liu
- Department of Evidence-Based Medicine, Southwest Medical University, 1 Xianglin Road, Luzhou, Sichuan, China
| | - Zhen Qin
- Department of Rheumatology and Immunology, Affiliated Hospital of Southwest Medical University, 25 Taiping Road, Luzhou, Sichuan, China
| | - An-Fang Huang
- Department of Rheumatology and Immunology, Affiliated Hospital of Southwest Medical University, 25 Taiping Road, Luzhou, Sichuan, China.
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18
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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: 0] [Impact Index Per Article: 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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19
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Wang X, Wang T, Zheng Y, Yin X. Recognition of liver tumors by predicted hyperspectral features based on patient's Computed Tomography radiomics features. Photodiagnosis Photodyn Ther 2023:103638. [PMID: 37247798 DOI: 10.1016/j.pdpdt.2023.103638] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND Primary liver tumors have posed a serious threat to human life and health, and their early diagnosis is urgent. Therefore, enhancing the accuracy of non-invasive early detection of liver tumors is imperative. METHODS Firstly, image enhancement was applied to augment the dataset, resulting in a total of 464 samples after employing seven data augmentation methods. Subsequently, the XGBoost model was utilized to construct and learn the mapping relationship between Computed Tomography (CT) and corresponding hyperspectral imaging (HSI) data. This model enables the prediction of HSI features corresponding to CT features, thereby enriching CT with more comprehensive hyperspectral information. RESULTS Four classifiers were employed to discern the presence of tumors in patients. The results demonstrated exceptional performance, with a classification accuracy exceeding 90%. CONCLUSIONS This study proposes an artificial intelligence-based methodology that utilizes early CT radiomics features to predict HSI features. Subsequently, the results are utilized for non-invasive tumor prediction and early screening, thereby enhancing the accuracy of non-invasive liver tumor detection.
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Affiliation(s)
- Xuehu Wang
- College of Electronic and Information Engineering, Hebei University, Baoding 071000, China; Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding 071000, China; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China
| | - Tianqi Wang
- College of Electronic and Information Engineering, Hebei University, Baoding 071000, China; Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding 071000, China; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100010, P. R. China.
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University, Baoding 071000, China
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20
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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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21
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Naidu A, Nayak SS, Lulu S S, Sundararajan V. Advances in computational frameworks in the fight against TB: The way forward. Front Pharmacol 2023; 14:1152915. [PMID: 37077815 PMCID: PMC10106641 DOI: 10.3389/fphar.2023.1152915] [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: 01/28/2023] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.
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Affiliation(s)
| | | | | | - Vino Sundararajan
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, India
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22
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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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23
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Iraji MS, Tanha J, Habibinejad M. Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method. Comput Biol Med 2022; 151:106276. [PMID: 36410099 DOI: 10.1016/j.compbiomed.2022.106276] [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: 07/05/2022] [Revised: 10/18/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022]
Abstract
Drug targets must be identified and positioned correctly to research and manufacture new drugs. In this study, rather than using traditional methods for drug expansion, the drug target is determined using machine learning. Machine learning has generated significant interest and desire in recent years and extensive research due to its low cost and speed of operation. As a result, it is critical to develop an intelligent classification system for drug proteins. This study proposes two distinct models for the prediction of druggable protein classes based on the deep learning method. The translation of drug-protein sequences is based on six physicochemical properties of amino acids. Following the application of the autocovariance method, converted sequences are used as fixed-length input vectors in deep stacked sparse auto-encoders (DSSAEs) network. The coded protein sequences are also considered and utilized as a six-channel input vector for the deep convolutional neural network model. The experimental results contributing to the deep convolution model are more efficient than previous studies for classifying druggable proteins. The proposed approach achieved a sensitivity of 96.92%, a specificity of 99.51%, and an accuracy of 98.29%.
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Affiliation(s)
- Mohammad Saber Iraji
- Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran; Department of Computer Engineering, University of Tabriz, Tabriz, Iran.
| | - Jafar Tanha
- Department of Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Mahboobeh Habibinejad
- Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran
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24
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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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25
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Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets. Comput Struct Biotechnol J 2022; 21:46-57. [PMID: 36514341 PMCID: PMC9732000 DOI: 10.1016/j.csbj.2022.11.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
Abstract
Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.
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26
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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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27
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Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework. iScience 2022; 25:104883. [PMID: 36046193 PMCID: PMC9421381 DOI: 10.1016/j.isci.2022.104883] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 07/08/2022] [Accepted: 08/02/2022] [Indexed: 11/22/2022] Open
Abstract
Discovery of potential drugs requires rapid and precise identification of drug targets. Although traditional experimental methodologies can accurately identify drug targets, they are time-consuming and inappropriate for high-throughput screening. Computational approaches based on machine learning (ML) algorithms can expedite the prediction of druggable proteins; however, the performance of the existing computational methods remains unsatisfactory. This study proposes a computational tool, SPIDER, to enhance the accurate prediction of druggable proteins. SPIDER employs various feature descriptors pertaining to several aspects, including physicochemical properties, compositional information, and composition-transition-distribution information, coupled with well-known ML algorithms to facilitate the construction of the final meta-predictor. The experimental results showed that SPIDER enabled more precise and robust prediction of druggable proteins than the baseline models and current existing methods in terms of the independent test dataset. An online web server was established and made freely available online. Computational models can expedite the identification of potential druggable proteins SPIDER represents the first stacked model proposed for druggable protein prediction SPIDER enables more precise prediction of druggable proteins than existing methods The SPIDER web server is available at http://pmlabstack.pythonanywhere.com/SPIDER.
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28
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Ali F, Kumar H, Patil S, Kotecha K, Banjar A, Daud A. Target-DBPPred: An intelligent model for prediction of DNA-binding proteins using discrete wavelet transform based compression and light eXtreme gradient boosting. Comput Biol Med 2022; 145:105533. [PMID: 35447463 DOI: 10.1016/j.compbiomed.2022.105533] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/11/2022] [Accepted: 04/13/2022] [Indexed: 11/03/2022]
Abstract
DNA-protein interaction is a critical biological process that performs influential activities, including DNA transcription and recombination. DBPs (DNA-binding proteins) are closely associated with different kinds of human diseases (asthma, cancer, and AIDS), while some of the DBPs are used in the production of antibiotics, steroids, and anti-inflammatories. Several methods have been reported for the prediction of DBPs. However, a more intelligent method is still highly desirable for the accurate prediction of DBPs. This study presents an intelligent computational method, Target-DBPPred, to improve DBPs prediction. Important features from primary protein sequences are investigated via a novel feature descriptor, called EDF-PSSM-DWT (Evolutionary difference formula position-specific scoring matrix-discrete wavelet transform) and several other multi-evolutionary methods, including F-PSSM (Filtered position-specific scoring matrix), EDF-PSSM (Evolutionary difference formula position-specific scoring matrix), PSSM-DPC (Position-specific scoring matrix-dipeptide composition), and Lead-BiPSSM (Lead-bigram-position specific scoring matrix) to encapsulate diverse multivariate features. The best feature set from the features of each descriptor is selected using sequential forward selection (SFS). Further, four models are trained using Adaboost, XGB (eXtreme gradient boosting), ERT (extremely randomized trees), and LiXGB (Light eXtreme gradient boosting) classifiers. LiXGB, with the best feature set of EDF-PSSM-DWT, has attained 6.69% and 15.07% higher performance in terms of accuracies using training and testing datasets, respectively. The obtained results verify the improved performance of our proposed predictor over the existing predictors.
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Affiliation(s)
- Farman Ali
- Department of Elementary and Secondary Education, Peshawar, Khyber Pakhtunkhwa, Pakistan; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Harish Kumar
- Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Shruti Patil
- Symbiosis Institute of Technology, Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, India
| | - Ketan Kotecha
- Symbiosis Institute of Technology, Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, India.
| | - Ameen Banjar
- Department of Information Systems, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Ali Daud
- Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, School of Information Engineering, Zhejiang Ocean University, Zhoushan, 316022, China; Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah, Saudi Arabia.
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