1
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Ali Khandokar I, Muzahidul Islam A, Islam S, Shatabda S. A Gradient Boosting Classifier for Purchase Intention Prediction of Online Shoppers. Heliyon 2023; 9:e15163. [PMID: 37095970 PMCID: PMC10121810 DOI: 10.1016/j.heliyon.2023.e15163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/23/2022] [Accepted: 03/28/2023] [Indexed: 04/05/2023] Open
Abstract
Early purchase prediction plays a vital role for an e-commerce website. It enables e-shoppers to enlist consumers for product suggestions, offer discount and for many other interventions. Several work has already been done using session log for analyzing customer behavior whether he performs a purchase on the product or not. In most cases, it is difficult to find out and make a list of customers and offer them discount when their session ends. In this paper, we propose a customer's purchase intention prediction model where e-shoppers can detect customer's purpose earlier. First, we apply feature selection technique to select best features. Then the extracted features are fed to train supervised learning models. Several classifiers like support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), decision tree (DT), and XGBoost classifiers have been applied along with oversampling method for balancing the dataset. The experiments were performed on a standard benchmark dataset. Experimental results show that XGBoost classifier with feature selection techniques and oversampling method has the significantly higher area under ROC curve (auROC) score and are under precision-recall curve (auPR) score which are 0.937 and 0.754 respectively. On the other hand accuracy achieved by XGBoost and Decision tree are significantly improved and they are 90.65% and 90.54% respectively. Overall performance of the gradient boosting method is significantly improved compared to other classifiers and state-of-the-art methods. In addition to this, a method for explainable analysis on the problem was outlined.
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2
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Ahmed F, Dehzangi I, Hasan MM, Shatabda S. Accurately predicting microbial phosphorylation sites using evolutionary and structural features. Gene 2023; 851:146993. [DOI: 10.1016/j.gene.2022.146993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/05/2022] [Accepted: 10/14/2022] [Indexed: 11/27/2022]
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3
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MethEvo: an accurate evolutionary information-based methylation site predictor. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07738-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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4
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Dehzangi I, Sharma A, Shatabda S. iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features. Methods Mol Biol 2022; 2499:125-134. [PMID: 35696077 DOI: 10.1007/978-1-0716-2317-6_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Posttranslational modification (PTM) is an important biological mechanism to promote functional diversity among the proteins. So far, a wide range of PTMs has been identified. Among them, glycation is considered as one of the most important PTMs. Glycation is associated with different neurological disorders including Parkinson and Alzheimer. It is also shown to be responsible for different diseases, including vascular complications of diabetes mellitus. Despite all the efforts have been made so far, the prediction performance of glycation sites using computational methods remains limited. Here we present a newly developed machine learning tool called iProtGly-SS that utilizes sequential and structural information as well as Support Vector Machine (SVM) classifier to enhance lysine glycation site prediction accuracy. The performance of iProtGly-SS was investigated using the three most popular benchmarks used for this task. Our results demonstrate that iProtGly-SS is able to achieve 81.61%, 93.62%, and 92.95% prediction accuracies on these benchmarks, which are significantly better than those results reported in the previous studies. iProtGly-SS is implemented as a web-based tool which is publicly available at http://brl.uiu.ac.bd/iprotgly-ss/ .
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Affiliation(s)
- Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, USA.
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia.
- Department of Medical Science Mathematics, Tokyo Medical and Dental University (TMDU), Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.
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5
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Azim SM, Haque MR, Shatabda S. OriC-ENS: A sequence-based ensemble classifier for predicting origin of replication in S. cerevisiae. Comput Biol Chem 2021; 92:107502. [PMID: 33962169 DOI: 10.1016/j.compbiolchem.2021.107502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/21/2021] [Indexed: 01/08/2023]
Abstract
DNA Replication plays the most crucial part in biological inheritance, ensuring an even flow of genetic information from parent to offspring. The beginning site of DNA Replication which is called the Origin of Replication (ORI), plays a significant role in understanding the molecular mechanisms and genomic analysis of DNA. Hence, it is paramount to accurately identify the origin of replication to gain a more accurate understanding of the biochemical and genomic properties of DNA. In this paper, We have proposed a new approach named OriC-ENS that uses sequence-based feature extraction techniques, K-mer, K-gapped Mono-Di, and Di Mono, and an ensemble classification technique that uses majority voting for the identification of Origin of Replication. We have used three SVM classifiers, one for the K-mer features and two more for K-Gapped Mono-Di and K-Gapped Di-mono features. Finally, we used majority voting to combine the prediction by each predictor. Experimental results on the S. Cerevisiae dataset have shown that our method achieves an accuracy of 91.62 % which outperforms other state-of-the-art methods by a significant margin. We have also tested our method using other evaluation metrics such as Matthews Correlation Coefficient (MCC), Area Under Curve(AUC), Sensitivity, and Specificity, where it has achieved a score of 0.83, 0.98, 0.90, and 0.92 respectively. We have further evaluated our model on an independent test set collected from OriDB, consisting of the sequences of Schizosaccharomyces pombe where we have seen that our model can predict the origin of replication efficiently and with great precision. We have made our python-based source code available at https://github.com/MehediAzim/OriC-ENS.
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Affiliation(s)
- Sayed Mehedi Azim
- Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka, 1212, Bangladesh
| | - Md Rakibul Haque
- Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka, 1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka, 1212, Bangladesh.
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6
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Convolutional neural networks with image representation of amino acid sequences for protein function prediction. Comput Biol Chem 2021; 92:107494. [PMID: 33930742 DOI: 10.1016/j.compbiolchem.2021.107494] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/21/2021] [Indexed: 01/11/2023]
Abstract
Proteins are one of the most important molecules that govern the cellular processes in most of the living organisms. Various functions of the proteins are of paramount importance to understand the basics of life. Several supervised learning approaches are applied in this field to predict the functionality of proteins. In this paper, we propose a convolutional neural network based approach ProtConv to predict the functionality of proteins by converting the amino-acid sequences to a two dimensional image. We have used a protein embedding technique using transfer learning to generate the feature vector. Feature vector is then converted into a square sized single channel image to be fed into a convolutional network. The neural network architecture used here is a combination of convolutional filters and average pooling layers followed by dense fully connected layers to predict a binary function. We have performed experiments on standard benchmark datasets taken from two very important protein function prediction task: proinflammatory cytokines and anticancer peptides. Our experiments show that the proposed method, ProtConv achieves state-of-the-art performances on both of the datasets. All necessary details about implementation with source code and datasets are made available at: https://github.com/swakkhar/ProtConv.
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7
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Haque HMF, Rafsanjani M, Arifin F, Adilina S, Shatabda S. SubFeat: Feature subspacing ensemble classifier for function prediction of DNA, RNA and protein sequences. Comput Biol Chem 2021; 92:107489. [PMID: 33932779 DOI: 10.1016/j.compbiolchem.2021.107489] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 03/07/2021] [Accepted: 04/19/2021] [Indexed: 11/16/2022]
Abstract
The information of a cell is primarily contained in deoxyribonucleic acid (DNA). There is a flow of DNA information to protein sequences via ribonucleic acids (RNA) through transcription and translation. These entities are vital for the genetic process. Recent epigenetics developments also show the importance of the genetic material and knowledge of their attributes and functions. However, the growth in these entities' available features or functionalities is still slow due to the time-consuming and expensive in vitro experimental methods. In this paper, we have proposed an ensemble classification algorithm called SubFeat to predict biological entities' functionalities from different types of datasets. Our model uses a feature subspace-based novel ensemble method. It divides the feature space into sub-spaces, which are then passed to learn individual classifier models. The ensemble is built on these base classifiers that use a weighted majority voting mechanism. SubFeat tested on four datasets comprising two DNA, one RNA, and one protein dataset, and it outperformed all the existing single classifiers and the ensemble classifiers. SubFeat is made available as a Python-based tool. We have made the package SubFeat available online along with a user manual. It is freely accessible from here: https://github.com/fazlulhaquejony/SubFeat.
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Affiliation(s)
- H M Fazlul Haque
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Muhammod Rafsanjani
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Fariha Arifin
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Sheikh Adilina
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh.
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8
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Dipta SR, Taherzadeh G, Ahmad MW, Arafat ME, Shatabda S, Dehzangi A. SEMal: Accurate protein malonylation site predictor using structural and evolutionary information. Comput Biol Med 2020; 125:104022. [PMID: 33022522 DOI: 10.1016/j.compbiomed.2020.104022] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 09/24/2020] [Accepted: 09/25/2020] [Indexed: 10/23/2022]
Abstract
Post Transactional Modification (PTM) is a vital process which plays an important role in a wide range of biological interactions. One of the most recently identified PTMs is Malonylation. It has been shown that Malonylation has an important impact on different biological pathways including glucose and fatty acid metabolism. Malonylation can be detected experimentally using mass spectrometry. However, this process is both costly and time-consuming which has inspired research to find more efficient and fast computational methods to solve this problem. This paper proposes a novel approach, called SEMal, to identify Malonylation sites in protein sequences. It uses both structural and evolutionary-based features to solve this problem. It also uses Rotation Forest (RoF) as its classification technique to predict Malonylation sites. To the best of our knowledge, our extracted features as well as our employed classifier have never been used for this problem. Compared to the previously proposed methods, SEMal outperforms them in all metrics such as sensitivity (0.94 and 0.89), accuracy (0.94 and 0.91), and Matthews correlation coefficient (0.88 and 0.82), for Homo Sapiens and Mus Musculus species, respectively. SEMal is publicly available as an online predictor at: http://brl.uiu.ac.bd/SEMal/.
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Affiliation(s)
- Shubhashis Roy Dipta
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Ghazaleh Taherzadeh
- Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD, 20742, USA
| | - Md Wakil Ahmad
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Md Easin Arafat
- Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, 08102, USA; Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08102, USA.
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9
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Khatun MS, Shoombuatong W, Hasan MM, Kurata H. Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction. Curr Genomics 2020; 21:454-463. [PMID: 33093807 PMCID: PMC7536797 DOI: 10.2174/1389202921999200625103936] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 03/19/2020] [Accepted: 05/27/2020] [Indexed: 12/22/2022] Open
Abstract
Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.
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Affiliation(s)
| | | | - Md. Mehedi Hasan
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan; Tel: +81-948-297-828; E-mail: and Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
| | - Hiroyuki Kurata
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan; Tel: +81-948-297-828; E-mail: and Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
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10
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Arafat ME, Ahmad MW, Shovan S, Dehzangi A, Dipta SR, Hasan MAM, Taherzadeh G, Shatabda S, Sharma A. Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features. Genes (Basel) 2020; 11:E1023. [PMID: 32878321 PMCID: PMC7565944 DOI: 10.3390/genes11091023] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/19/2020] [Accepted: 08/27/2020] [Indexed: 02/07/2023] Open
Abstract
Post Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew's Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.
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Affiliation(s)
- Md. Easin Arafat
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - Md. Wakil Ahmad
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - S.M. Shovan
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh; (S.M.S.); (M.A.M.H.)
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA;
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Shubhashis Roy Dipta
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - Md. Al Mehedi Hasan
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh; (S.M.S.); (M.A.M.H.)
| | - Ghazaleh Taherzadeh
- Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD 20742, USA
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD 4111, Australia
- Department of Medical Science Mathematics, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
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Rashid MM, Shatabda S, Hasan MM, Kurata H. Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites. Curr Genomics 2020; 21:194-203. [PMID: 33071613 PMCID: PMC7521030 DOI: 10.2174/1389202921666200427210833] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 04/12/2020] [Accepted: 04/13/2020] [Indexed: 01/10/2023] Open
Abstract
A variety of protein post-translational modifications has been identified that control many cellular functions. Phosphorylation studies in mycobacterial organisms have shown critical importance in diverse biological processes, such as intercellular communication and cell division. Recent technical advances in high-precision mass spectrometry have determined a large number of microbial phosphorylated proteins and phosphorylation sites throughout the proteome analysis. Identification of phosphorylated proteins with specific modified residues through experimentation is often labor-intensive, costly and time-consuming. All these limitations could be overcome through the application of machine learning (ML) approaches. However, only a limited number of computational phosphorylation site prediction tools have been developed so far. This work aims to present a complete survey of the existing ML-predictors for microbial phosphorylation. We cover a variety of important aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, window size, and software utility. Initially, we review the currently available phosphorylation site databases of the microbiome, the state-of-the-art ML approaches, working principles, and their performances. Lastly, we discuss the limitations and future directions of the computational ML methods for the prediction of phosphorylation.
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Affiliation(s)
| | | | - Md. Mehedi Hasan
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828;, E-mail: and Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
| | - Hiroyuki Kurata
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828;, E-mail: and Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
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López Y, Dehzangi A, Reddy HM, Sharma A. C-iSUMO: A sumoylation site predictor that incorporates intrinsic characteristics of amino acid sequences. Comput Biol Chem 2020; 87:107235. [PMID: 32604027 DOI: 10.1016/j.compbiolchem.2020.107235] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 12/16/2019] [Accepted: 02/12/2020] [Indexed: 12/13/2022]
Abstract
Post-translational modifications are considered important molecular interactions in protein science. One of these modifications is "sumoylation" whose computational detection has recently become a challenge. In this paper, we propose a new computational predictor which makes use of the sine and cosine of backbone torsion angles and the accessible surface area for predicting sumoylation sites. The aforementioned features were computed for all the proteins in our benchmark dataset, and a training matrix consisting of sumoylation and non-sumoylation sites was ultimately created. This training matrix was balanced by undersampling the majority class (non-sumoylation sites) using the NearMiss method. Finally, an AdaBoost classifier was used for discriminating between sumoylation and non-sumoylation sites. Our predictor was called "C-iSumo" because of its effective use of circular functions. C-iSumo was compared with another predictor which was outperformed in statistical metrics such as sensitivity (0.734), accuracy (0.746) and Matthews correlation coefficient (0.494).
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Affiliation(s)
- Yosvany López
- Genesis Institute of Genetic Research, Genesis Healthcare Co., Tokyo, Japan.
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, Maryland, USA
| | | | - Alok Sharma
- School of Engineering and Physics, University of the South Pacific, Suva, Fiji; Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan; Institute for Integrated and Intelligent Systems, Griffith University, Queensland, Australia.
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13
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Ahmad A, Lin H, Shatabda S. Locate-R: Subcellular localization of long non-coding RNAs using nucleotide compositions. Genomics 2020; 112:2583-2589. [PMID: 32068122 DOI: 10.1016/j.ygeno.2020.02.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 11/11/2019] [Accepted: 02/12/2020] [Indexed: 12/12/2022]
Abstract
Knowledge of the sub-cellular localization of the most diverse class of transcribed RNA, long non-coding RNAs (lncRNAs) will lead us to identify different types of cancers and other diseases as lncRNAs play key role in related cellular functions. In recent days with the exponential growth of known records, it becomes essential to establish new machine learning based techniques to identify the new one due to faster and cheaper solutions provided compared to laboratory methods. In this paper, we propose Locate-R, a novel method for predicting the sub-cellular location of lncRNAs. We have used only n-gapped l-mer composition and l-mer composition as features and select best 655 features to build the model. This model is based locally deep support vector machines which significantly enhance the prediction accuracy with respect to exiting state-of-the-art methods. Our predictor is readily available for use as a stand-alone web application from: http://locate-r.azurewebsites.net/.
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Affiliation(s)
- Ahsan Ahmad
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh.
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14
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Chandra AA, Sharma A, Dehzangi A, Tsunoda T. EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction. BMC Genomics 2019; 19:984. [PMID: 30999859 PMCID: PMC7402405 DOI: 10.1186/s12864-018-5383-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 12/17/2018] [Indexed: 01/21/2023] Open
Abstract
Background Post-translational modification (PTM), which is a biological process, tends to modify proteome that leads to changes in normal cell biology and pathogenesis. In the recent times, there has been many reported PTMs. Out of the many modifications, phosphoglycerylation has become particularly the subject of interest. The experimental procedure for identification of phosphoglycerylated residues continues to be an expensive, inefficient and time-consuming effort, even with a large number of proteins that are sequenced in the post-genomic period. Computational methods are therefore being anticipated in order to effectively predict phosphoglycerylated lysines. Even though there are predictors available, the ability to detect phosphoglycerylated lysine residues still remains inadequate. Results We have introduced a new predictor in this paper named EvolStruct-Phogly that uses structural and evolutionary information relating to amino acids to predict phosphoglycerylated lysine residues. Benchmarked data is employed containing experimentally identified phosphoglycerylated and non-phosphoglycerylated lysines. We have then extracted the three structural information which are accessible surface area of amino acids, backbone torsion angles, amino acid’s local structure conformations and profile bigrams of position-specific scoring matrices. Conclusion EvolStruct-Phogly showed a noteworthy improvement in regards to the performance when compared with the previous predictors. The performance metrics obtained are as follows: sensitivity 0.7744, specificity 0.8533, precision 0.7368, accuracy 0.8275, and Mathews correlation coefficient of 0.6242. The software package and data of this work can be obtained from https://github.com/abelavit/EvolStruct-Phogly or www.alok-ai-lab.com
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Affiliation(s)
| | - Alok Sharma
- School of Engineering & Physics, University of the South Pacific, Suva, Fiji. .,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. .,Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. .,CREST, JST, Tokyo, Japan.
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, MD, USA
| | - Tatushiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,CREST, JST, Tokyo, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
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15
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Ahmad A, Shatabda S. EPAI-NC: Enhanced prediction of adenosine to inosine RNA editing sites using nucleotide compositions. Anal Biochem 2019; 569:16-21. [DOI: 10.1016/j.ab.2019.01.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 01/03/2019] [Accepted: 01/11/2019] [Indexed: 01/24/2023]
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16
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CFSBoost: Cumulative feature subspace boosting for drug-target interaction prediction. J Theor Biol 2019; 464:1-8. [DOI: 10.1016/j.jtbi.2018.12.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 12/13/2018] [Accepted: 12/18/2018] [Indexed: 01/12/2023]
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17
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Reddy HM, Sharma A, Dehzangi A, Shigemizu D, Chandra AA, Tsunoda T. GlyStruct: glycation prediction using structural properties of amino acid residues. BMC Bioinformatics 2019; 19:547. [PMID: 30717650 PMCID: PMC7394324 DOI: 10.1186/s12859-018-2547-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 11/29/2018] [Indexed: 02/06/2023] Open
Abstract
Background Glycation is a one of the post-translational modifications (PTM) where sugar molecules and residues in protein sequences are covalently bonded. It has become one of the clinically important PTM in recent times attributed to many chronic and age related complications. Being a non-enzymatic reaction, it is a great challenge when it comes to its prediction due to the lack of significant bias in the sequence motifs. Results We developed a classifier, GlyStruct based on support vector machine, to predict glycated and non-glycated lysine residues using structural properties of amino acid residues. The features used were secondary structure, accessible surface area and the local backbone torsion angles. For this work, a benchmark dataset was extracted containing 235 glycated and 303 non-glycated lysine residues. GlyStruct demonstrated improved performance of approximately 10% in comparison to benchmark method of Gly-PseAAC. The performance for GlyStruct on the metrics, sensitivity, specificity, accuracy and Mathew’s correlation coefficient were 0.7013, 0.7989, 0.7562, and 0.5065, respectively for 10-fold cross-validation. Conclusion Glycation has emerged to be one of the clinically important PTM of proteins in recent times. Therefore, the development of computational tools become necessary to predict glycation, which could help medical professionals administer drugs and manage patients more effectively. The proposed predictor manages to classify glycated and non-glycated lysine residues with promising results consistently on various cross-validation schemes and outperforms other state of the art methods. Electronic supplementary material The online version of this article (10.1186/s12859-018-2547-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Alok Sharma
- School of Engineering & Physics, University of the South Pacific, Suva, Fiji. .,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan. .,Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. .,CREST, JST, Tokyo, Japan.
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, MD, USA
| | - Daichi Shigemizu
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.,CREST, JST, Tokyo, Japan.,Division of Genomic Medicine, Medical Genome Center, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | | | - Tatushiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.,CREST, JST, Tokyo, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
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18
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Nightingale DJ, Geladaki A, Breckels LM, Oliver SG, Lilley KS. The subcellular organisation of Saccharomyces cerevisiae. Curr Opin Chem Biol 2019; 48:86-95. [PMID: 30503867 PMCID: PMC6391909 DOI: 10.1016/j.cbpa.2018.10.026] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 10/29/2018] [Accepted: 10/31/2018] [Indexed: 01/06/2023]
Abstract
Subcellular protein localisation is essential for the mechanisms that govern cellular homeostasis. The ability to understand processes leading to this phenomenon will therefore enhance our understanding of cellular function. Here we review recent developments in this field with regard to mass spectrometry, fluorescence microscopy and computational prediction methods. We highlight relative strengths and limitations of current methodologies focussing particularly on studies in the yeast Saccharomyces cerevisiae. We further present the first cell-wide spatial proteome map of S. cerevisiae, generated using hyperLOPIT, a mass spectrometry-based protein correlation profiling technique. We compare protein subcellular localisation assignments from this map, with two published fluorescence microscopy studies and show that confidence in localisation assignment is attained using multiple orthogonal methods that provide complementary data.
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Affiliation(s)
- Daniel Jh Nightingale
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, United Kingdom; Cambridge Systems Biology Centre, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1GA, United Kingdom
| | - Aikaterini Geladaki
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, United Kingdom; Cambridge Systems Biology Centre, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1GA, United Kingdom; Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, United Kingdom
| | - Lisa M Breckels
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, United Kingdom; Cambridge Systems Biology Centre, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1GA, United Kingdom
| | - Stephen G Oliver
- Cambridge Systems Biology Centre, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1GA, United Kingdom
| | - Kathryn S Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, United Kingdom; Cambridge Systems Biology Centre, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1GA, United Kingdom.
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19
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Application of Support Vector Machines in Viral Biology. GLOBAL VIROLOGY III: VIROLOGY IN THE 21ST CENTURY 2019. [PMCID: PMC7114997 DOI: 10.1007/978-3-030-29022-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Novel experimental and sequencing techniques have led to an exponential explosion and spiraling of data in viral genomics. To analyse such data, rapidly gain information, and transform this information to knowledge, interdisciplinary approaches involving several different types of expertise are necessary. Machine learning has been in the forefront of providing models with increasing accuracy due to development of newer paradigms with strong fundamental bases. Support Vector Machines (SVM) is one such robust tool, based rigorously on statistical learning theory. SVM provides very high quality and robust solutions to classification and regression problems. Several studies in virology employ high performance tools including SVM for identification of potentially important gene and protein functions. This is mainly due to the highly beneficial aspects of SVM. In this chapter we briefly provide lucid and easy to understand details of SVM algorithms along with applications in virology.
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20
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Chandra A, Sharma A, Dehzangi A, Ranganathan S, Jokhan A, Chou KC, Tsunoda T. PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids. Sci Rep 2018; 8:17923. [PMID: 30560923 PMCID: PMC6299098 DOI: 10.1038/s41598-018-36203-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 11/16/2018] [Indexed: 12/22/2022] Open
Abstract
The biological process known as post-translational modification (PTM) contributes to diversifying the proteome hence affecting many aspects of normal cell biology and pathogenesis. There have been many recently reported PTMs, but lysine phosphoglycerylation has emerged as the most recent subject of interest. Despite a large number of proteins being sequenced, the experimental method for detection of phosphoglycerylated residues remains an expensive, time-consuming and inefficient endeavor in the post-genomic era. Instead, the computational methods are being proposed for accurately predicting phosphoglycerylated lysines. Though a number of predictors are available, performance in detecting phosphoglycerylated lysine residues is still limited. In this paper, we propose a new predictor called PhoglyStruct that utilizes structural information of amino acids alongside a multilayer perceptron classifier for predicting phosphoglycerylated and non-phosphoglycerylated lysine residues. For the experiment, we located phosphoglycerylated and non-phosphoglycerylated lysines in our employed benchmark. We then derived and integrated properties such as accessible surface area, backbone torsion angles, and local structure conformations. PhoglyStruct showed significant improvement in the ability to detect phosphoglycerylated residues from non-phosphoglycerylated ones when compared to previous predictors. The sensitivity, specificity, accuracy, Mathews correlation coefficient and AUC were 0.8542, 0.7597, 0.7834, 0.5468 and 0.8077, respectively. The data and Matlab/Octave software packages are available at https://github.com/abelavit/PhoglyStruct .
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Affiliation(s)
- Abel Chandra
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD-4111, Australia.
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Kanagawa, Japan.
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji.
- CREST, JST, Tokyo, 113-8510, Japan.
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, Maryland, USA
| | - Shoba Ranganathan
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Anjeela Jokhan
- Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
| | - Kuo-Chen Chou
- The Gordon Life Science Institute, Boston, MA, 02478, USA
| | - Tatsuhiko Tsunoda
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Kanagawa, Japan
- CREST, JST, Tokyo, 113-8510, Japan
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21
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Rahman MS, Aktar U, Jani MR, Shatabda S. iPromoter-FSEn: Identification of bacterial σ 70 promoter sequences using feature subspace based ensemble classifier. Genomics 2018; 111:1160-1166. [PMID: 30059731 DOI: 10.1016/j.ygeno.2018.07.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 07/07/2018] [Accepted: 07/12/2018] [Indexed: 10/28/2022]
Abstract
Sigma promoter sequences in bacterial genomes are important due to their role in transcription initiation. Sigma 70 is one of the most important and crucial sigma factors. In this paper, we address the problem of identification of σ70 promoter sequences in bacterial genome. We propose iPromoter-FSEn, a novel predictor for identification of σ70 promoter sequences. Our proposed method is based on a feature subspace based ensemble classifier. A large set of of features extracted from the sequence of nucleotides are divided into subsets and each subset is given to individual single classifiers to learn. Based on the decisions of the ensemble an aggregate decision is made by the ensemble voting classifier. We tested our method on a standard benchmark dataset extracted from experimentally validated results. Experimental results shows that iPromoter-FSEn significantly improves over the state-of-the art σ70 promoter sequence predictors. The accuracy and area under receiver operating characteristic curve of iPromoter-FSEn are 86.32% and 0.9319 respectively. We have also made our method readily available for use as an web application from: http://ipromoterfsen.pythonanywhere.com/server.
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Affiliation(s)
- Md Siddiqur Rahman
- Department of Computer Science and Engineering, United International University Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh
| | - Usma Aktar
- Department of Computer Science and Engineering, United International University Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh
| | - Md Rafsan Jani
- Department of Computer Science and Engineering, United International University Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh.
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22
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Al Maruf MA, Shatabda S. iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components. Genomics 2018; 111:966-972. [PMID: 29935224 DOI: 10.1016/j.ygeno.2018.06.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 06/09/2018] [Accepted: 06/13/2018] [Indexed: 11/28/2022]
Abstract
Recombination hotspots in a genome are unevenly distributed. Hotspots are regions in a genome that show higher rates of meiotic recombinations. Computational methods for recombination hotspot prediction often use sophisticated features that are derived from physico-chemical or structure based properties of nucleotides. In this paper, we propose iRSpot-SF that uses sequence based features which are computationally cheap to generate. Four feature groups are used in our method: k-mer composition, gapped k-mer composition, TF-IDF of k-mers and reverse complement k-mer composition. We have used recursive feature elimination to select 17 top features for hotspot prediction. Our analysis shows the superiority of gapped k-mer composition and reverse complement k-mer composition features over others. We have used SVM with RBF kernel as a classification algorithm. We have tested our algorithm on standard benchmark datasets. Compared to other methods iRSpot-SF is able to produce significantly better results in terms of accuracy, Mathew's Correlation Coefficient and sensitivity which are 84.58%, 0.6941 and 84.57%. We have made our method readily available to use as a python based tool and made the datasets and source codes available at: https://github.com/abdlmaruf/iRSpot-SF. An web application is developed based on iRSpot-SF and freely available to use at: http://irspot.pythonanywhere.com/server.html.
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Affiliation(s)
- Md Abdullah Al Maruf
- Department of Computer Science and Engineering, United International University, Madani Aveneue, Satarkul, Badda, Dhaka 1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Madani Aveneue, Satarkul, Badda, Dhaka 1212, Bangladesh.
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23
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Uddin MR, Sharma A, Farid DM, Rahman MM, Dehzangi A, Shatabda S. EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features. J Theor Biol 2018; 443:138-146. [DOI: 10.1016/j.jtbi.2018.02.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Revised: 01/18/2018] [Accepted: 02/03/2018] [Indexed: 12/21/2022]
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