Browsing by Author "Moni, Mohammad Ali"

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A Comprehensive Review of Green Computing
(IEEE, 2023-08-01) Paul, Showmick Guha; Saha, Arpa; Arefin, Mohammad Shamsul; Bhuiyan, Touhid; Biswas, Al Amin; Reza, Ahmed Wasif; Alotaibi, Naif M.; Alyami, Salem A.; Moni, Mohammad Ali
Green computing, also called sustainable computing, is the process of developing and optimizing computer chips, systems, networks, and software in such a manner that can maximize efficiency by utilizing energy more efficiently and minimizing the negative environmental influence on the surrounding. The term “green computing” refers to practices that lessen the negative effects of technology on the environment. Due to the improvements in modern technology, various devices, mechanisms, and software have been developed, and lots of studies have been conducted to optimize and increase those technologies’ green computing abilities. Thus, review and summarization of green computing-based studies are required to identify the current advancements, challenges, and future research opportunities. This study reviewed and summarized green computing in each area studies, by exploring green computing’s twelve areas. Current research trends, datasets or testing mechanisms, and the construction or implementation of various technologies to accomplish green computing and sustainable development have been discussed. This study, after conducting a thorough comparison and analysis, provides responses to the proposed state-of-the-art research questions. Furthermore, this study presents the current challenges and future research opportunities with respect to each green computing area. This study will provide organizations, researchers, and institutions conducting research on green computing with insights and ideas. Furthermore, environmental organizations, companies, and government agencies concerned with reducing carbon emissions and energy consumption will also benefit from this review study.
A Machine Learning Approach for Risk Factors Analysis and Survival Prediction of Heart Failure Patients
(Elsevier, 2023-04-18) Ali, Md. Mamun; Al-Doori, Vian S.; Mirzah, Nubogh; Hemu, Asifa Afsari; Mahmud, Imran; Azam, Sami; Al-tabatabaie, Kusay Faisal; Ahmed, Kawsar; Bu, Francis M.; Moni, Mohammad Ali
In this study, we propose machine learning (ML) for risk factors analysis and survival prediction of Heart Failure (HF) patients using a survival dataset. Five supervised ML methods are applied to the dataset: Decision Tree (DT), Decision Tree Regressor (DTR), Random Forest (RF), XGBoost, and Gradient Boosting (GB) algorithms. We compare the applied algorithms’ performances based on accuracy, precision, recall, F-measure, and log loss value and show RF provides the highest accuracy of 97.78%. The analysis of the risk factors shows the most predictive features based on coefficients and feature importance. The top six risk factors for HF patients are serum creatinine (SC), age, ejection fraction (EF), platelets, creatinine phosphokinase (CPK), and SS (SS). Further analysis of these factors shows significant clustering of the features. The survival analysis finds that the increment of SC, age, and SS and the decrement of EF are the most significant risk factors for HF patients. Our results suggest that HF survival prediction is possible with higher accuracy using the proposed model. Our ML models are useful in clinical settings for screening patients with HF probability.
A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron
(MDPI, 2023-06-18) Aziz, Md. Tarek; Mahmud, S. M. Hasan; Elahe, Md. Fazla; Jahan, Hosney; Rahman, Md Habibur; Nandi, Dip; Smirani, Lassaad K.; Ahmed, Kawsar; Bui, Francis M.; Moni, Mohammad Ali
Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction.
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.
Advancing Thyroid Care: An Accurate Trustworthy Diagnostics System with Interpretable Ai and Hybrid Machine Learning Techniques
(Elsevier, 2024-08-20) Sutradhar, Ananda; Akter, Sharmin; Shamrat, F M Javed Mehedi; Idris, Mohd Yamani Idna Bin; Ahmed, Kawsar; Moni, Mohammad Ali
The worldwide prevalence of thyroid disease is on the rise, representing a chronic condition that significantly impacts global mortality rates. Machine learning (ML) approaches have demonstrated potential superiority in mitigating the occurrence of this disease by facilitating early detection and treatment. However, there is a growing demand among stakeholders and patients for reliable and credible explanations of the generated predictions in sensitive medical domains. Hence, we propose an interpretable thyroid classification model to illustrate outcome explanations and investigate the contribution of predictive features by utilizing explainable AI. Two real-time thyroid datasets underwent various preprocessing approaches, addressing data imbalance issues using the Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTE-ENN). Subsequently, two hybrid classifiers, namely RDKVT and RDKST, were introduced to train the processed and selected features from Univariate and Information Gain feature selection techniques. Following the training phase, the Shapley Additive Explanation (SHAP) was applied to identify the influential characteristics and corresponding values contributing to the outcomes. The conducted experiments ultimately concluded that the presented RDKST classifier achieved the highest performance, demonstrating an accuracy of 98.98 % when trained on Information Gain selected features. Notably, the features T3 (triiodothyronine), TT4 (total thyroxine), TSH (thyroid-stimulating hormone), FTI (free thyroxine index), and T3_measured significantly influenced the generated outcomes. By balancing classification accuracy and outcome explanation ability, this study aims to enhance the clinical decision-making process and improve patient care.
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.
Bioinformatics and System Biology Approach to Identify the Influences of Sars-cov-2 Infections to Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease Patients
(Briefings in bioinformatics, 2021) Mahmud, S M Hasan; Al-Mustanjid, Md; Akter, Farzana; Rahman, Md Shazzadur; Ahmed, Kawsar; Rahman, Md Habibur; Chen, Wenyu; Moni, Mohammad Ali
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.
BOO-ST and CBCEC: Two Novel Hybrid Machine Learning Methods Aim To Reduce the Mortality of Heart Failure Patients
(Springer Nature Limited, 2023-12-18) Sutradhar, Ananda; Al Rafi, Mustahsin; Shamrat, F M Javed Mehedi; Ghosh, Pronab; Das, Subrata; Islam, Md Anaytul; Ahmed, Kawsar; Zhou, Xujuan; Azad, A. K. M.; Alyami, Salem A.; Moni, Mohammad Ali
Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.
Cancer Classification Utilizing Voting Classifier With Ensemble Feature Selection Method and Transcriptomic Data
(MDPI Publications, 2023-09-14) Khatun, Rabea; Akter, Maksuda; Islam, Md. Manowarul; Uddin, Md. Ashraf; Talukder, Md. Alamin; Kamruzzaman, Joarder; Azad, AKM; Paul, Bikash Kumar; Almoyad, Muhammad Ali Abdulllah; Aryal, Sunil; Moni, Mohammad Ali
Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods.
Comorbidity Effects of Mitochondrial Dysfunction to the Progression of Neurological Disorders
(2019 22nd International Conference on Computer and Information Technology (ICCIT), IEEE, 2019-12-20) Satu, Md. Shahriare; Howlader, Koushik Chandra; Akhund, Tajim Md. Niamat Ullah; Quinn, Julian M. W.; Lio, Pietro; Moni, Mohammad Ali
Mitochondrial-dysfunction is linked to various neurological diseases. To understand these complications we developed a quantitative framework to explore how mitochondrial-dysfunction influences the progression of Alzheimer's, Parkin-son's, Huntington's, Amyotrophic Lateral Sclerosis and Cerebral Palsy. We sought insights from the gene profiles of mitochondrial and associated neurological disorders by constructing gene-disease networks. We also employed KEGG pathways and Gene Ontology to explore functional enrichment, and protein-protein interaction networks to identify the protein groups shared between these diseases. These identified potential biomarkers were verified using gold-benchmark databases. Our identified signature genes and pathways are useful to identify co-morbidity outcomes for the mitochondrial dysfunction.
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.
Development and Performance Analysis of Machine Learning Methods for Predicting Depression Among Menopausal Women
(Elsevier, 2023-05-25) Ali, Md. Mamun; Ali, Hussein; Algashamy, A.; Alzidi, Enas; Ahmed, Kawsar; Bui, Francis M.; Patel, Shobhit K.; Azam, Sami; Abdulrazak, Lway Faisal; Moni, Mohammad Ali
Menopause is an obligatory phenomenon in a woman’s life. Some women face mental and physical issues during their menopausal period. Depression is one of the issues some women struggle with during their menopausal period. The scarcity of specialists, lack of knowledge, and awareness is the motivating factor in this research to predict depression among menopausal women and enhance their quality of life. The prediction of depression symptoms among menopausal women with machine learning techniques is promising and challenging in artificial intelligence. This study develops a system with significant accuracy using a supervised machine-learning approach. Various classification algorithms are used to determine the best-performing classifier by evaluating multiple parameters, including accuracy, sensitivity, specificity, precision, recall, F-Measure, Receiver Operating Characteristic (ROC), Precision–Recall Curve (PRC), and Area Under the Curve (AUC). We found that Random Forest and XGBoost classifiers are the performers with 99.04% accuracy employing the 14 most significant features.
Discovering Common Pathophysiological Processes Between COVID-19 and Cystic Fibrosis by Differential Gene Expression Pattern Analysis
(Daffodil International University, 2022-04-29) Hasan, Md. Tanvir; Abdulrazak, Lway Faisal; Alam, Mohammad Khursheed; Islam, Md. Rezwan; Sathi, Yeasmin Hena; Al-Zahrani, Fahad Ahmed; Ahmed, Kawsar; Bui, Francis M.; Moni, Mohammad Ali
Coronaviruses are a family of viruses that infect mammals and birds. Coronaviruses cause infections of the respiratory system in humans, which can be minor or fatal. A comparative transcriptomic analysis has been performed to establish essential profiles of the gene expression of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) linked to cystic fibrosis (CF). Transcriptomic studies have been carried out in relation to SARS-CoV-2 since a number of people have been diagnosed with CF. The recognition of differentially expressed genes demonstrated 8 concordant genes shared between the SARS-CoV-2 and CF. Extensive gene ontology analysis and the discovery of pathway enrichment demonstrated SARS-CoV-2 response to CF. The gene ontological terms and pathway enrichment mechanisms derived from this research may affect the production of successful drugs, especially for the people with the following disorder. Identification of TF-miRNA association network reveals the interconnection between TF genes and miRNAs, which may be effective to reveal the other influenced disease that occurs for SARS-CoV-2 to CF. The enrichment of pathways reveals SARS-CoV-2-associated CF mostly engaged with the type of innate immune system, Toll-like receptor signaling pathway, pantothenate and CoA biosynthesis, allograft rejection, graft-versus-host disease, intestinal immune network for IgA production, mineral absorption, autoimmune thyroid disease, legionellosis, viral myocarditis, inflammatory bowel disease (IBD), etc. The drug compound identification demonstrates that the drug targets of IMIQUIMOD and raloxifene are the most significant with the significant hub DEGs.
Drug Compound Prediction-based Analysis of Cigarette Smoking to Pancreatic Cancer Patients
(2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE, 2021-04-12) Taz, Tasnimul Alam; Kawsar, Md; Siddique, Sinthia; Ahmed, Kawsar; Moni, Mohammad Ali; Paul, Bikash Kumar
Considering the fact of survival rate, pancreatic cancer (PC) can be categorized among the most fatal cancer diseases as the survival rate is medium among most of the cases. Cigarette smoking is regarded as a significant risk factor for PC. In this study, therapeutic results are attempted to be found by the assist of a number of Bioinformatics tools. Two microarray datasets GSE144909 and GSE26307 are used for pancreatic cancer and active smoker lung cell samples respectively. Preprocessing and filtering of the datasets and common differentially expressed genes (DEGs) are identified with the assist of R programming language. Regulation of the DEGs are expressed with a Venn diagram. Then Protein-protein interactions (PPIs) network is designed based on the common DEGs and hub nodes are identified using topological analysis. RPA1, RPA2, BLM, FANCM and APITD1 genes are the top 5 mostly interconnected genes in PPIs network and visibility of RPA1 and RPA2 is found in inflammatory pancreatic cancer cell and smoker lung cell. Gene ontology (GO) and pathway identification is regarded as the future study of this research. Finally, a number of therapeutic targets have been identified based on the common DEGs.
DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images
(MDPI Publications, 2023-03-02) Ahamed, Md. Khabir Uddin; Islam, Md Manowarul; Uddin, Md. Ashraf; Akhter, Arnisha; Acharjee, Uzzal Kumar; Paul, Bikash Kumar; Moni, Mohammad Ali
COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing this coronavirus, most developing countries are having difficulty in detecting COVID-19 cases for the mass population. In many countries, there is a scarcity of COVID-19 testing kits and other resources due to the increasing rate of COVID-19 infections. Therefore, this deficit of testing resources and the increasing figure of daily cases encouraged us to improve a deep learning model to aid clinicians, radiologists and provide timely assistance to patients. In this article, an efficient deep learning-based model to detect COVID-19 cases that utilizes a chest X-ray images dataset has been proposed and investigated. The proposed model is developed based on ResNet50V2 architecture. The base architecture of ResNet50V2 is concatenated with six extra layers to make the model more robust and efficient. Finally, a Grad-CAM-based discriminative localization is used to readily interpret the detection of radiological images. Two datasets were gathered from different sources that are publicly available with class labels: normal, confirmed COVID-19, bacterial pneumonia and viral pneumonia cases. Our proposed model obtained a comprehensive accuracy of 99.51% for four-class cases (COVID-19/normal/bacterial pneumonia/viral pneumonia) on Dataset-2, 96.52% for the cases with three classes (normal/ COVID-19/bacterial pneumonia) and 99.13% for the cases with two classes (COVID-19/normal) on Dataset-1. The accuracy level of the proposed model might motivate radiologists to rapidly detect and diagnose COVID-19 cases.
EAH-Net: A Novel Ensemble Attention-Based Hybrid Architecture for Breast Cancer Diagnosis Utilizing Ultrasound Images
(Scopus, 2024-10-31) Hasan, Md. Zahid; Hossain, Shahed; Jim, Risul Islam; Bulbul, Abdullah Al-Mamun; Rahman, Md. Tanvir; Moni, Mohammad Ali
Breast cancer is a complex and often fatal malignancy in women worldwide, requiring thorough medical examinations. Accurately detecting breast cancer is challenging due to its diverse forms, stages, symptoms, and diagnostic techniques. With advancements in artificial intelligence, an automated computerized method can potentially aid radiologists in the early detection of breast cancer. This study presents a novel and robust deep neural network, EAH-Net, for breast cancer diagnosis using ultrasound images. The EAH-Net architecture comprises an ensemble attention module, a modified UNet model that performs segmentation by isolating regions of interest, and a hybrid approach to classify breast cancers accurately. Besides, we employed explainable AI techniques to highlight the most significant regions, assisting radiologists in making more informed decisions. The proposed segmentation framework yields promising outcomes across Jaccard, Precision, Recall, Specificity, and Dice metrics, averaging 89.26 ± 0.36, 91.79 ± 1.13, 92.98 ± 1.08, 99.38 ± 0.35, and 95.26 ± 0.45 percents, respectively. The hybrid classification framework demonstrates outstanding performance with an accuracy of 98.48 ± 0.18%. Overall, EAH-Net offers a reliable and robust computer-aided solution for automated breast cancer diagnosis.
Exploring Gene Regulatory Interaction Networks and Predicting Therapeutic Molecules for Hypopharyngeal Cancer and Egfr-mutated Lung Adenocarcinoma
(John Wiley & Sons Ltd, 2024-04-16) Bhattacharjya, Abanti; Islam, Md Manowarul; Uddin, Md Ashraf; Talukder, Md Alamin; Azad, AKM; Aryal, Sunil; Paul, Bikash Kumar; Tasnim, Wahia; Almoyad, Muhammad Ali Abdulllah; Moni, Mohammad Ali
Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.
Heart Disease Prediction Using Supervised Machine Learning Algorithms
(Computers in Biology and Medicine, 2021) Ali, Md Mamun; Paul, Bikash Kumar; Ahmed, Kawsar; M.Bui, Francis; Quinn, Julian M.W.; Moni, Mohammad Ali
Machine learning and data mining-based approaches to prediction and detection of heart disease would be of great clinical utility, but are highly challenging to develop. In most countries there is a lack of cardiovascular expertise and a significant rate of incorrectly diagnosed cases which could be addressed by developing accurate and efficient early-stage heart disease prediction by analytical support of clinical decision-making with digital patient records. This study aimed to identify machine learning classifiers with the highest accuracy for such diagnostic purposes. Several supervised machine-learning algorithms were applied and compared for performance and accuracy in heart disease prediction. Feature importance scores for each feature were estimated for all applied algorithms except MLP and KNN. All the features were ranked based on the importance score to find those giving high heart disease predictions. This study found that using a heart disease dataset collected from Kaggle three-classification based on k-nearest neighbor (KNN), decision tree (DT) and random forests (RF) algorithms the RF method achieved 100% accuracy along with 100% sensitivity and specificity. Thus, we found that a relatively simple supervised machine learning algorithm can be used to make heart disease predictions with very high accuracy and excellent potential utility.
Identification of Biomarkers and Pathways for the Sars-cov-2 Infections That Make Complexities in Pulmonary Arterial Hypertension Patients
(Briefings in Bioinformatics, 2021-03) Taz, Tasnimul Alam; Ahmed, Kawsar; Paul, Bikash Kumar; Al-Zahrani, Fahad Ahmed; Mahmud, S M Hasan; Moni, Mohammad Ali
This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients’ lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein–protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.
Identification of Biomarkers and Pathways for the Sars-cov-2 Infections That Make Complexities in Pulmonary Arterial Hypertension Patients
(Briefings in Bioinformatics, 2021-02-22) Taz, Tasnimul Alam; Ahmed, Kawsar; Paul, Bikash Kumar; Al-Zahrani, Fahad Ahmed; Mahmud, S M Hasan; Moni, Mohammad Ali
This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients’ lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein–protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.