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Naseem A, Khan YD. An intelligent model for prediction of abiotic stress-responsive microRNAs in plants using statistical moments based features and ensemble approaches. Methods 2024; 228:65-79. [PMID: 38768931 DOI: 10.1016/j.ymeth.2024.05.008] [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: 04/04/2024] [Revised: 04/30/2024] [Accepted: 05/10/2024] [Indexed: 05/22/2024] Open
Abstract
This study proposed an intelligent model for predicting abiotic stress-responsive microRNAs in plants. MicroRNAs (miRNAs) are short RNA molecules regulates the stress in genes. Experimental methods are costly and time-consuming, as compare to in-silico prediction. Addressing this gap, the study seeks to develop an efficient computational model for plant stress response prediction. The two benchmark datasets for MiRNA and Pre-MiRNA dataset have been acquired in this study. Four ensemble approaches such as bagging, boosting, stacking, and blending have been employed. Classifiers such as Random Forest (RF), Extra Trees (ET), Ada Boost (ADB), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM). Stacking and Blending employed all stated classifiers as base learners and Logistic Regression (LR) as Meta Classifier. There have been a total of four types of testing used, including independent set, self-consistency, cross-validation with 5 and 10 folds, and jackknife. This study has utilized evaluation metrics such as accuracy score, specificity, sensitivity, Mathew's correlation coefficient (MCC), and AUC. Our proposed methodology has outperformed existing state of the art study in both datasets based on independent set testing. The SVM-based approach has exhibited accuracy score of 0.659 for the MiRNA dataset, which is better than the previous study. The ET classifier has surpassed the accuracy of Pre-MiRNA dataset as compared to the existing benchmark study, achieving an impressive score of 0.67. The proposed method can be used in future research to predict abiotic stresses in plants.
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Affiliation(s)
- Ansar Naseem
- Department of Artificial Intelligence, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan.
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Malebary SJ, Alromema N, Suleman MT, Saleem M. m5c-iDeep: 5-Methylcytosine sites identification through deep learning. Methods 2024; 230:80-90. [PMID: 39089345 DOI: 10.1016/j.ymeth.2024.07.008] [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: 05/09/2024] [Revised: 07/16/2024] [Accepted: 07/23/2024] [Indexed: 08/03/2024] Open
Abstract
5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/.
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Affiliation(s)
- Sharaf J Malebary
- Department of Information Technology, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia
| | - Nashwan Alromema
- Department of Computer Science, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia.
| | - Muhammad Taseer Suleman
- Department of Criminology and Forensic Sciences, Lahore Garrison University, Lahore Pakistan; Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54770 Pakistan
| | - Maham Saleem
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54770 Pakistan
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Suleman MT, Alturise F, Alkhalifah T, Khan YD. m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models. BioData Min 2024; 17:4. [PMID: 38360720 PMCID: PMC10868122 DOI: 10.1186/s13040-023-00353-x] [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: 06/30/2023] [Accepted: 12/31/2023] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND 1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites. OBJECTIVE Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated. METHODOLOGY The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models. RESULTS The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics. CONCLUSION For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/ .
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Affiliation(s)
- Muhammad Taseer Suleman
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, 54770, Pakistan
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia.
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, 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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Suleman MT, Alturise F, Alkhalifah T, Khan YD. iDHU-Ensem: Identification of dihydrouridine sites through ensemble learning models. Digit Health 2023; 9:20552076231165963. [PMID: 37009307 PMCID: PMC10064468 DOI: 10.1177/20552076231165963] [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: 12/29/2022] [Accepted: 03/09/2023] [Indexed: 04/04/2023] Open
Abstract
Background Dihydrouridine (D) is one of the most significant uridine modifications that have a prominent occurrence in eukaryotes. The folding and conformational flexibility of transfer RNA (tRNA) can be attained through this modification. Objective The modification also triggers lung cancer in humans. The identification of D sites was carried out through conventional laboratory methods; however, those were costly and time-consuming. The readiness of RNA sequences helps in the identification of D sites through computationally intelligent models. However, the most challenging part is turning these biological sequences into distinct vectors. Methods The current research proposed novel feature extraction mechanisms and the identification of D sites in tRNA sequences using ensemble models. The ensemble models were then subjected to evaluation using k-fold cross-validation and independent testing. Results The results revealed that the stacking ensemble model outperformed all the ensemble models by revealing 0.98 accuracy, 0.98 specificity, 0.97 sensitivity, and 0.92 Matthews Correlation Coefficient. The proposed model, iDHU-Ensem, was also compared with pre-existing predictors using an independent test. The accuracy scores have shown that the proposed model in this research study performed better than the available predictors. Conclusion The current research contributed towards the enhancement of D site identification capabilities through computationally intelligent methods. A web-based server, iDHU-Ensem, was also made available for the researchers at https://taseersuleman-idhu-ensem-idhu-ensem.streamlit.app/.
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Affiliation(s)
- Muhammad Taseer Suleman
- Department of Computer Science, School of systems and technology, University of Management and Technology, Lahore, Pakistan
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia
- Fahad Alturise, Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia.
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, School of systems and technology, University of Management and Technology, Lahore, Pakistan
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Shah AA, Alturise F, Alkhalifah T, Khan YD. Evaluation of deep learning techniques for identification of sarcoma-causing carcinogenic mutations. Digit Health 2022; 8:20552076221133703. [PMID: 36312852 PMCID: PMC9597026 DOI: 10.1177/20552076221133703] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022] Open
Abstract
The abnormal growth of human healthy cells is called cancer. One of the major
types of cancer is sarcoma, mostly found in human bones and soft tissue cells.
It commonly occurs in children. According to a survey of the United States of
America, there are more than 17,000 sarcoma patients registered each year which
is 15% of all cancer cases. Recognition of cancer at its early stage saves many
lives. The proposed study developed a framework for the early detection of human
sarcoma cancer using deep learning Recurrent Neural Network (RNN) algorithms.
The DNA of a human cell is made up of 25,000 to 30,000 genes. Each gene is
represented by sequences of nucleotides. The nucleotides in a sequence of a
driver gene can change which is termed as mutations. Some mutations can cause
cancer. There are seven types of a gene whose mutation causes sarcoma cancer.
The study uses the dataset which has been taken from more than 134 samples and
includes 141 mutations in 8 driver genes. On these gene sequences RNN algorithms
Long and Short-Term Memory (LSTM), Gated Recurrent Units and Bi-directional LSTM
(Bi-LSTM) are used for training. Rigorous testing techniques such as
Self-consistency testing, independent set testing, 10-fold cross-validation test
are applied for the validation of results. These validation techniques yield
several metrics such as Area Under the Curve (AUC), sensitivity, specificity,
Mathew's correlation coefficient, loss, and accuracy. The proposed algorithm
exhibits an accuracy of 99.6% with an AUC value of 1.00.
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Affiliation(s)
- Asghar Ali Shah
- Department of Computer Science, University of Management and
Technology, Lahore, Pakistan,Department of Computer Sciences, Bahria University Lahore Campus, Lahore, Pakistan
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia,Fahad Alturise, Department of Computer,
College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim,
Saudi Arabia. ,
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, University of Management and
Technology, Lahore, Pakistan
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Mini-review: Recent advances in post-translational modification site prediction based on deep learning. Comput Struct Biotechnol J 2022; 20:3522-3532. [PMID: 35860402 PMCID: PMC9284371 DOI: 10.1016/j.csbj.2022.06.045] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 11/23/2022] Open
Abstract
Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.
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Key Words
- AAindex, Amino acid index
- ATP, Adenosine triphosphate
- AUC, Area under curve
- Ac, Acetylation
- BE, Binary encoding
- BLOSUM, Blocks substitution matrix
- Bi-LSTM, Bidirectional LSTM
- CKSAAP, Composition of k-spaced amino acid Pairs
- CNN, Convolutional neural network
- CNNOH, CNN with the one-hot encoding
- CNNWE, CNN with the word-embedding encoding
- CNNrgb, CNN red green blue
- CV, Cross-validation
- DC-CNN, Densely connected convolutional neural network
- DL, Deep learning
- DNNs, Deep neural networks
- Deep learning
- E. coli, Escherichia coli
- EBGW, Encoding based on grouped weight
- EGAAC, Enhanced grouped amino acids content
- IG, Information gain
- K, Lysine
- KNN, k nearest neighbor
- LASSO, Least absolute shrinkage and selection operator
- LSTM, Long short-term memory
- LSTMWE, LSTM with the word-embedding encoding
- M.musculus, Mus musculus
- MDC, Modular densely connected convolutional networks
- MDCAN, Multilane dense convolutional attention network
- ML, Machine learning
- MLP, Multilayer perceptron
- MMI, Multivariate mutual information
- Machine learning
- Mass spectrometry
- NMBroto, Normalized Moreau-Broto autocorrelation
- P, Proline
- PSP, PhosphoSitePlus
- PSSM, Position-specific scoring matrix
- PTM, Post-translational modifications
- Ph, Phosphorylation
- Post-translational modification
- Prediction
- PseAAC, Pseudo-amino acid composition
- R, Arginine
- RF, Random forest
- RNN, Recurrent neural network
- ROC, Receiver operating characteristic
- S, Serine
- S. typhimurium, Salmonella typhimurium
- S.cerevisiae, Saccharomyces cerevisiae
- SE, Squeeze and excitation
- SEV, Split to Equal Validation
- ST, Source and target
- SUMO, Small ubiquitin-like modifier
- SVM, Support vector machines
- T, Threonine
- Ub, Ubiquitination
- Y, Tyrosine
- ZSL, Zero-shot learning
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Alghamdi W, Attique M, Alzahrani E, Ullah MZ, Khan YD. LBCEPred: a machine learning model to predict linear B-cell epitopes. Brief Bioinform 2022; 23:6543896. [PMID: 35262658 DOI: 10.1093/bib/bbac035] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 01/03/2022] [Accepted: 01/25/2022] [Indexed: 01/15/2023] Open
Abstract
B-cell epitopes have the capability to recognize and attach to the surface of antigen receptors to stimulate the immune system against pathogens. Identification of B-cell epitopes from antigens has a great significance in several biomedical and biotechnological applications, provides support in the development of therapeutics, design and development of an epitope-based vaccine and antibody production. However, the identification of epitopes with experimental mapping approaches is a challenging job and usually requires extensive laboratory efforts. However, considerable efforts have been placed for the identification of epitopes using computational methods in the recent past but deprived of considerable achievements. In this study, we present LBCEPred, a python-based web-tool (http://lbcepred.pythonanywhere.com/), build with random forest classifier and statistical moment-based descriptors to predict the B-cell epitopes from the protein sequences. LBECPred outperforms all sequence-based available models that are currently in use for the B-cell epitopes prediction, with 0.868 accuracy value and 0.934 area under the curve. Moreover, the prediction performance of proposed models compared to other state-of-the-art models is 56.3% higher on average for Mathews Correlation Coefficient. LBCEPred is easy to use tool even for novice users and has also shown the models stability and reliability, thus we believe in its significant contribution to the research community and the area of bioinformatics.
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Affiliation(s)
- Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 80221, Jeddah, Saudi Arabia
| | - Muhammad Attique
- Department of Computer Science, University of Management and Technology, Lahore, 54000, Pakistan.,Department of Information Technology, University of Gujrat, Gujrat, 50700, Pakistan
| | - Ebraheem Alzahrani
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
| | - Malik Zaka Ullah
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, University of Management and Technology, Lahore, 54000, Pakistan
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Shahid M, Ilyas M, Hussain W, Khan YD. ORI-Deep: improving the accuracy for predicting origin of replication sites by using a blend of features and long short-term memory network. Brief Bioinform 2022; 23:6511972. [PMID: 35048955 DOI: 10.1093/bib/bbac001] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/30/2021] [Accepted: 01/02/2022] [Indexed: 11/14/2022] Open
Abstract
Replication of DNA is an important process for the cell division cycle, gene expression regulation and other biological evolution processes. It also has a crucial role in a living organism's physical growth and structure. Replication of DNA comprises of three stages known as initiation, elongation and termination, whereas the origin of replication sites (ORI) is the location of initiation of the DNA replication process. There exist various methodologies to identify ORIs in the genomic sequences, however, these methods have used either extensive computations for execution, or have limited optimization for the large datasets. Herein, a model called ORI-Deep is proposed to identify ORIs from the multiple cell type genomic sequence benchmark data. An efficient method is proposed using a deep neural network to identify ORIs for four different eukaryotic species. For better representation of data, a feature vector is constructed using statistical moments for the training and testing of data and is further fed to a long short-term memory (LSTM) network. To prove the effectiveness of the proposed model, we applied several validation techniques at different levels to obtain seven accuracy metrics, and the accuracy score for self-consistency, 10-fold cross-validation, jackknife and the independent set test is observed to be 0.977, 0.948, 0.976 and 0.977, respectively. Based on the results, it can be concluded that ORI-Deep can efficiently predict the sites of origin replication in DNA sequence with high accuracy. Webserver for ORI-Deep is available at (https://share.streamlit.io/waqarhusain/orideep/main/app.py), whereas source code is available at (https://github.com/WaqarHusain/OriDeep).
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Affiliation(s)
- Mahwish Shahid
- School of Systems and Technologies, University of Management and Technology, Lahore, Pakistan
| | - Maham Ilyas
- University of Management and Technology, Lahore, Pakistan
| | - Waqar Hussain
- University of Management and Technology, Lahore, Pakistan
| | - Yaser Daanial Khan
- Department of Computer Science, University of Management and Technology, Lahore, Pakistan
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