Article
Biochemistry & Molecular Biology
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, Mohammad Ali Moni, Pietro Lio, Watshara Shoombuatong
Summary: A new predictor iBitter-Fuse was developed for more accurate identification of bitter peptides by integrating various feature encoding schemes and utilizing genetic algorithm and support vector machine. Benchmarking experiments showed its superior performance, and a web server was established for high-throughput identification of bitter peptides.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Genetics & Heredity
Yanqin Zhang, Zhiyuan Li
Summary: In this study, machine learning methods were used to classify phage virion proteins, and a novel approach called RF_phage virion was proposed for effective classification. The model utilized four protein sequence coding methods as features and employed the random forest algorithm for classification. The results showed that the RF_phage virion model outperformed classical machine learning methods.
FRONTIERS IN GENETICS
(2023)
Article
Computer Science, Artificial Intelligence
Yinan Guo, Zirui Zhang, Fengzhen Tang
Summary: Feature selection is important in machine learning to reduce complexity and simplify interpretation. A novel non-linear method proposed in this paper uses kernelized multi-class support vector machines and fast recursive feature elimination to select features that work well for all classes, resulting in lower computational time complexity.
PATTERN RECOGNITION
(2021)
Article
Management
Asuncion Jimenez-Cordero, Juan Miguel Morales, Salvador Pineda
Summary: Feature selection has become a challenging issue in machine learning, particularly in classification problems. Support Vector Machine is a widely used technique in classification tasks, with various methodologies proposed for selecting the most relevant features in SVM. The authors introduce an embedded feature selection method based on a min-max optimization problem to balance model complexity and classification accuracy, showcasing efficiency and usefulness in benchmark datasets.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Microbiology
Songbo Liu, Chengmin Cui, Huipeng Chen, Tong Liu
Summary: This study introduces an ensemble learning-based method for identifying important features in phage protein, aiming to understand its relationship with host bacteria and develop antimicrobial agents. The selected features are found to have significant biological significance based on the analysis conducted.
FRONTIERS IN MICROBIOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Mohammed Alshutbi, Zhiyong Li, Moath Alrifaey, Masoud Ahmadipour, Muhammad Murtadha Othman
Summary: The decisions of experts and the evaluation of patient data play crucial roles in breast cancer analysis. Machine learning techniques can aid in quickly examining and diagnosing medical data, reducing potential errors caused by inexperienced decision-makers. This study proposes an intelligent cancer classification method that selects a feature subset and optimizes the parameters of the SVM classifier using the Jaya algorithm. The method is applied to accurately characterize a breast cancer dataset and compared with other classifiers, demonstrating its effectiveness.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Hardware & Architecture
Yosef Masoudi-Sobhanzadeh, Shabnam Emami-Moghaddam
Summary: This study proposes a machine learning-based method to predict botnets, addressing the limitations of existing methods in real-time application, functionality, and consideration of attack types. The results show that the proposed method accurately classifies data streams into relevant groups and achieves a trade-off between feature selection and prediction model performance.
Review
Biology
Muhammad Kabir, Chanin Nantasenamat, Sakawrat Kanthawong, Phasit Charoenkwan, Watshara Shoombuatong
Summary: Phage virion proteins (PVPs) effectively recognize and bind to host cell receptors without harming human or animal cells. Understanding their functional mechanisms is crucial for antibacterial drug discovery and development. This study thoroughly evaluates 13 state-of-the-art PVP predictors, exploring key factors for building accurate and stable predictors.
Article
Computer Science, Artificial Intelligence
Xin Yan, Hongmiao Zhu
Summary: This paper proposes a novel support vector machine model with feature mapping and kernel trick to handle datasets with different distributions. The model improves robustness by pre-selecting training points, and converts the problem into a convex quadratic programming problem solved efficiently by the sequential minimal optimization algorithm. Numerical tests demonstrate the superior performance of the proposed method compared to other classification methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Javier Alcaraz, Martine Labbe, Mercedes Landete
Summary: This paper introduces a Support Vector Machine with feature selection and proposes a bi-objective evolutionary algorithm to approximate the Pareto optimal frontier. Extensive computational experiments are conducted to compare the results obtained by different methods, and the properties of points in the Pareto frontier are discussed.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Biochemistry & Molecular Biology
Adeel Malik, Watshara Shoombuatong, Chang-Bae Kim, Balachandran Manavalan
Summary: A machine learning-based predictor called GPApred was developed to identify LPXTG-like proteins from their primary sequences. This predictor can be utilized for functional characterization and drug targeting in further research.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Article
Multidisciplinary Sciences
Haitao Han, Wenhong Zhu, Chenchen Ding, Taigang Liu
Summary: The classic structure of a bacteriophage exhibits complex symmetry with icosahedral symmetry in the head and helical symmetry in the tail. The phage virion protein (PVP) plays a crucial role in viral infection and understanding the interaction between phages and host bacteria. Developing computational methods, such as the iPVP-MCV model, can efficiently and accurately identify PVPs for potential antimicrobial drug development.
Article
Computer Science, Artificial Intelligence
Asuncion Jimenez-Cordero, Sebastian Maldonado
Summary: Functional Data Analysis (FDA) is important, but classifying hybrid functional data with both functional and static covariates is challenging. This paper proposes an embedded feature selection approach for SVM classification, optimizing bandwidths and SVM parameters to improve classification rates. The methodology outperformed 17 other approaches, demonstrating robustness through sensitivity analysis.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Ziad Akram-Ali-Hammouri, Manuel Fernandez-Delgado, Eva Cernadas, Senen Barro
Summary: The Support Vector Machine is an important machine learning algorithm that performs well on many classification problems. However, it is slow and requires a lot of memory when dealing with large datasets. To address this issue, a fast support vector classifier is proposed with efficient training, small prototypes collection, and fast kernel spread selection method.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Engineering, Civil
Amir Houshang Ayati, Ali Haghighi, Hamid Reza Ghafouri
Summary: This study introduces a general framework for real-time leak detection in pipelines by coupling machine learning to transient hydraulics. The performance of Support Vector Machines (SVM) as a superior pattern recognition algorithm is investigated in an experimental condition. The results indicate that the classification-based model has higher performance and can accurately detect leaks with stability and reliability against various uncertainties.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2022)
Article
Biochemical Research Methods
Sho Tsukiyama, Md Mehedi Hasan, Hong-Wen Deng, Hiroyuki Kurata
Summary: In this study, a novel approach called BERT6mA was proposed for detecting the 6mA modification in DNA, and its performance was evaluated and compared. Through pretraining and fine-tuning, BERT6mA showed high performance in prediction and achieved good results even in species with small sample sizes. Furthermore, the study analyzed the process of feature extraction by the BERT6mA model.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biology
Md Faruk Hosen, S. M. Hasan Mahmud, Kawsar Ahmed, Wenyu Chen, Mohammad Ali Moni, Hong-Wen Deng, Watshara Shoombuatong, Md Mehedi Hasan
Summary: In this paper, a novel predictor called DeepDNAbP has been developed to accurately predict DNA-binding proteins (DBPs) using a convolutional neural network model. The predictor achieves superior performance in cross-validation tests and outperforms existing methods, making it a powerful computational resource for predicting DBPs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Multidisciplinary Sciences
Phasit Charoenkwan, Saeed Ahmed, Chanin Nantasenamat, Julian M. W. Quinn, Mohammad Ali Moni, Pietro Lio, Watshara Shoombuatong
Summary: This study presents a novel meta-predictor, AMYPred-FRL, which utilizes a feature representation learning approach to identify amyloid proteins more accurately. By combining multiple machine learning algorithms and sequence-based feature descriptors, AMYPred-FRL generates 60 probabilistic features and forms a hybrid model. Through cross-validation and independent tests, AMYPred-FRL outperforms existing methods in predictive performance.
SCIENTIFIC REPORTS
(2022)
Article
Biology
Phasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, Pietro Lio, Balachandran Manavalan, Watshara Shoombuatong
Summary: This study presents a novel computational method, SAPPHIRE, for accurately identifying thermophilic proteins (TPPs) using sequence information. The method combines different feature encodings and machine learning algorithms to train baseline models and extract key information of TPPs. SAPPHIRE outperforms existing methods in terms of predictive performance and achieves higher accuracy and correlation coefficient.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biology
Phasit Charoenkwan, Nalini Schaduangrat, Pietro Lio, Mohammad Ali Moni, Balachandran Manavalan, Watshara Shoombuatong
Summary: This study proposes a novel computational approach, NEPTUNE, for the accurate and large-scale identification of Tumor Homing Peptides (THPs) from sequence information. The results demonstrate that NEPTUNE achieves superior performance in THP prediction and improves interpretability using the SHapley additive explanations method.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biology
Phasit Charoenkwan, Chonlatip Pipattanaboon, Chanin Nantasenamat, Md Mehedi Hasan, Mohammad Ali Moni, Pietro Lio, Watshara Shoombuatong
Summary: Despite existing cancer therapies, the development of new and effective treatments is necessary to address the ongoing cancer recurrence and new cases. This study proposes a new machine learning-based approach, PSRTTCA, for improving the identification and characterization of tumor T cell antigens (TTCAs) based on their primary sequences.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Adeel Malik, Watshara Shoombuatong, Chang-Bae Kim, Balachandran Manavalan
Summary: A machine learning-based predictor called GPApred was developed to identify LPXTG-like proteins from their primary sequences. This predictor can be utilized for functional characterization and drug targeting in further research.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Article
Anatomy & Morphology
Chanasorn Poodendan, Athikhun Suwannakhan, Tidarat Chawalchitiporn, Yuichi Kasai, Chanin Nantasenamat, Laphatrada Yurasakpong, Sitthichai Iamsaard, Arada Chaiyamoon
Summary: This study investigated the morphometric parameters of the C1 vertebra and evaluated its potential for sex prediction. The results showed that the C1 vertebra was longer in males compared to females. Evaluation of these parameters is important for preoperative assessment and treatment of atlas dislocation, and they can also be used for sex prediction.
SURGICAL AND RADIOLOGIC ANATOMY
(2023)
Article
Biology
Phasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, Changmin Oh, Balachandran Manavalan, Watshara Shoombuatong
Summary: In this study, a novel computational approach called PSRQSP was developed to improve the prediction and analysis of QSPs. Experimental results showed that PSRQSP outperformed conventional methods in identifying QSPs and demonstrated its predictive capability and effectiveness. PSRQSP also constructed an easy-to-use web server for accelerating the discovery of potential QSPs for drug development.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Phasit Charoenkwan, Nalini Schaduangrat, Nhat Truong Pham, Balachandran Manavalan, Watshara Shoombuatong
Summary: Proposed the first stack-based approach, Pretoria, for accurate and large-scale identification of CD8+ T-cell epitopes (TCEs) of eukaryotic pathogens. Constructed a pool of 144 different machine learning (ML)-based classifiers based on 12 popular ML algorithms and used feature selection method to determine important ML classifiers for building the stacked model. Experimental results demonstrated that Pretoria outperformed several conventional ML classifiers and the existing method, with an accuracy of 0.866, MCC of 0.732, and AUC of 0.921 in the independent test.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Article
Biochemistry & Molecular Biology
Tianshi Yu, Tianyang Huang, Leiye Yu, Chanin Nantasenamat, Nuttapat Anuwongcharoen, Theeraphon Piacham, Ruobing Ren, Ying-Chih Chiang
Summary: Researchers studied Cytochrome P450 17A1 (CYP17A1), a key enzyme in steroidogenesis, and its potential as a druggable target for anti-cancer molecule development. They used cheminformatic analyses and quantitative structure-activity relationship (QSAR) modeling on a dataset of CYP17A1 inhibitors. Different models were built for steroidal and nonsteroidal inhibitors, achieving good accuracy. The findings provide valuable insights for further drug discovery efforts targeting CYP17A1 inhibitors.
Article
Chemistry, Multidisciplinary
Nalini Schaduangrat, Nuttapat Anuwongcharoen, Phasit Charoenkwan, Watshara Shoombuatong
Summary: This study proposes a novel deep learning (DL)-based hybrid framework, named DeepAR, to accurately and rapidly identify AR antagonists by using only the SMILES notation. Experimental results indicate that DeepAR is a more accurate and stable approach for identifying AR antagonists, achieving an accuracy of 0.911 and MCC of 0.823 on an independent test dataset. In addition, the framework provides feature importance information and allows for characterization and analysis of potential AR antagonist candidates.
JOURNAL OF CHEMINFORMATICS
(2023)
Article
Chemistry, Multidisciplinary
Tianshi Yu, Chanin Nantasenamat, Supicha Kachenton, Nuttapat Anuwongcharoen, Theeraphon Piacham
Summary: This study used cheminformatic analysis and machine learning modeling to investigate the chemical space, scaffolds, structure-activity relationship, and landscape of human androgen receptor antagonists. The findings revealed differences in physicochemical properties between potent/active class molecules and intermediate/inactive class molecules. Low scaffold diversity was observed, especially in the potent/active class molecules, indicating the need for developing molecules with novel scaffolds. The study also identified significant activity cliff generators and provided insights and guidelines for the development of novel androgen receptor antagonists.
Article
Multidisciplinary Sciences
Phasit Charoenkwan, Sajee Waramit, Pramote Chumnanpuen, Nalini Schaduangrat, Watshara Shoombuatong
Summary: HCV infection causes chronic liver diseases, and there is no effective vaccine available. This study proposes a novel approach called TROLLOPE to accurately identify TCE-HCVs from sequence information, with superior predictive performance.
