Browsing by Author "Chen, Li"

Now showing 1 - 5 of 5

A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide-resistant Mycobacterium tuberculosis
(Scopus, 2024-09-28) Shaon, Md. Shazzad Hossain; Karim, Tasmin; Ali, Md. Mamun; Ahmed, Kawsar; Bui, Francis M.; Chen, Li; Moni, Mohammad Ali
RNA 5-methyluridine (m5U) sites play a significant role in understanding RNA modifications, which influence numerous biological processes such as gene expression and cellular functioning. Consequently, the identification of m5U sites can play a vital role in the integrity, structure, and function of RNA molecules. Therefore, this study introduces GRUpred-m5U, a novel deep learning based framework based on a gated recurrent unit in mature RNA and full transcript RNA datasets. We used three descriptor groups: nucleic acid composition, pseudo nucleic acid composition, and physicochemical properties, which include five feature extraction methods ENAC, Kmer, DPCP, DPCP type 2, and PseDNC. Initially, we aggregated all the feature extraction methods and created a new merged set. Three hybrid models were developed employing deep-learning methods and evaluated through 10-fold cross-validation with seven evaluation metrics. After a comprehensive evaluation, the GRUpred-m5U model outperformed the other applied models, obtaining 98.41% and 96.70% accuracy on the two datasets, respectively. To our knowledge, the proposed model outperformed all the existing state-of-the-art technology. The proposed supervised machine learning model was evaluated using unsupervised machine learning techniques such as principal component analysis (PCA), and it was observed that the proposed method provided a valid performance for identifying m5U. Considering its multi-layered construction, the GRUpred-m5U model has tremendous potential for future applications in the biological industry. The model, which consisted of neurons processing complicated input, excelled at pattern recognition and produced reliable results. Despite its greater size, the model obtained accurate results, essential in detecting m5U.
An Intelligent Thyroid Diagnosis System Utilising Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes
(Elsevier, 2023-01-15) Sutradhar, Ananda; Al Rafi, Mustahsin; Ghosh, Pronab; Shamrat, F. M.Javed Mehedi; Moniruzzaman, Md.; Ahmed, Kawsar; Azad, AKM; Bui, Francis M.; Chen, Li; Moni, Mohammad Ali
The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including Feature Importance (FIS), Information Gain Selections (IGS), and Least Absolute Shrinkage and Selection Operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as High-Risk Factors (HRF). Second, the models, including the Three Stage Hybrid Classifier (3SHC) and the Three Stage Hybrid Artificial Neural Network (3SHANN), are used as classifiers on the training data set. Third, a Local Interpretable Model-agnostic Explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a Partial Dependence Plot (PDP). Finally, the proposed system is validated with extensive experiments where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector.
DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model
(Elsevier, 2024-09-13) Rahman, Md. Ashikur; Ali, Md. Mamun; Ahmed, Kawsar; Mahmud, Imran; Bui, Francis M.; Chen, Li; Kumar, Santosh; Moni, Mohammad Ali
Quorum Sensing Peptides (QSP) are small molecules crucial for microbial communication, enabling bacterial populations to coordinate behaviors such as biofilm formation and virulence. The identification of QSP is vital for understanding these biological processes. While existing clinical and lab-based methods are available, they can be costly and time-consuming. This study introduces Deep QSP, a novel technique for QSP identification, which combines Latent Semantic Analysis (LSA), a word embedding feature extraction method, with classical amino acid-based extraction Pseudo Amino Acid Composition (PAAC), and a convolutional neural network (CNN) classifier. The DeepQSP model was evaluated using a dataset of 440 peptide sequences, achieving impressive performance metrics: 0.9697 accuracy, 0.9655 sensitivity, 0.9730 specificity, and a Matthews correlation coefficient (MCC) of 0.9385. The LSA combined with PAAC improves peptide sequence representation, while the CNN effectively captures complex patterns, leading to accurate QSP identification. These quantified results demonstrate the effectiveness of the Deep QSP method, offering a powerful tool for advancing the study of microbial interaction and quorum sensing. The enhanced identification of QSPs is critical for microbiology and bioengineering, aiding in the understanding of cell-to-cell communication in microorganisms.
MLAFP-XN: Leveraging neural network model for development of antifungal peptide identification tool
(Elsevier, 2024-09-30) Sultana, Md. Fahim; Shaona, Md. Shazzad Hossain; Chen, Li; Karima, Tasmin; Dhasarathan, Vigneswaran; Moni, ∙ Mohammad Ali
Infectious fungi have been an increasing global concern in the present era. A promising approach to tackle this pressing concern involves utilizing Antifungal peptides (AFP) to develop an antifungal drug that can selectively eliminate fungal pathogens from a host with minimal toxicity to the host. Accordingly, identifying precise therapeutic antifungal peptides is crucial for developing effective drugs and treatments. This study proposed MLAFP-XN, a neural network-based strategy for accurately detecting active AFP in sequencing data to achieve this objective. In this work, eight feature extraction techniques and the XGB feature selection strategy are utilized together to present an enhanced methodology. A total of 24 classification models were evaluated, and the most effective four have been selected. Each of these models demonstrated superior accuracy on independent test sets, with respective scores of 97.93 %, 99.47 %, and 99.48 %. Our model outperforms current state of the art methods. In addition, we created a companion website to demonstrate our AFP recognition process and use SHAP to identify the most influential properties.
StackAMP: Stacking-Based Ensemble Classifier for Antimicrobial Peptide Identification
(Scopus, 2024) Karim, Tasmin; Shaon, Md. Shazzad Hossain; Ali, Md. Mamun; Ahmed, Kawsar; M. Bui, Francis; Chen, Li
Antimicrobial peptides (AMPs) play a vital role in the immune defence systems of various organisms and have garnered significant attention for their potential applications in biotechnology and medicine. There are several approaches to identifying AMPs including clinical isolation and characterization, functional genomics, microbiology techniques, and others. However, these methods are mostly expensive, time-consuming, and require well-equipped labs. To overcome these challenges, machine learning models are a potential solution due to their robustness and high predictive capability with less time and cost. In this study, we explored the efficacy of stacking-based ensemble machine-learning techniques to identify AMPs with higher accuracy and precision. Five distinct feature extraction methods, namely amino acid composition, dipeptide composition, moran autocorrelation, geary autocorrelation, and pseudoamino acid composition, were employed to represent the sequence characteristics of peptides. To build robust predictive models, different traditional machine learning algorithms were applied. Additionally, we developed a novel stacking classifier, aptly named StackAMP, to harness the collective power of these algorithms. Our results demonstrated the exceptional performance of the proposed StackAMP ensemble method in AMP identification, achieving an accuracy of 99.97%, 99.93% specificity, and 100% sensitivity. This high accuracy underscores the effectiveness of our approach, which has promising outcomes for the rapid and accurate identification of AMPs in various biological contexts. This study not only contributes to the growing body of knowledge in the field of AMP recognition but also offers a practical tool with potential applications in drug discovery, biotechnology, and disease prevention.