Browsing by Author "Islam, Md Manowarul"

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Analysis of Gene Network Model of Thyroid Disorder and Associated Diseases
(Informatics in Medicine Unlocked, Science Direct, 2020) Kawsar, Md; Taz, Tasnimul Alam; Paul, Bikash Kumar; Mahmud, Shahin; Islam, Md Manowarul; Bhuyian, Touhid; Ahmed, Kawsar
Chronic Kidney Disease (CKD), High Blood Pressure (HBP), and Thyroid Disorder (TD) diseases are interrelated. When human patients are affected by one of them, then the possibility of affectness by the other two diseases is increased. Background studies indicate that there are large numbers of similar biological and genetic features among HBP, CKD, and TD. For this reason, the common gene network models among these three diseases are explored. The gene number is reduced through preprocessing and filtering. Then the common genes among the selected diseases and the most significant genes are explored. After completing this process, ten common genes among HBP, CKD, and TD are recognized. This analysis identifies the most significant hub proteins based on biological, biochemical, and genetic relationships between common genes. Following these relationships, the Protein-Protein Interactions network, Co-Expression network, Enrichment Analysis, Topological properties analysis, Gene regulatory network, and Physical Interaction network are exhibited. This analysis helps us to identify similar biological and genetic features among HBP, CKD, and TD. Interaction of proteins with drug molecules enables an efficient drug design for this research. These drugs can be considered for further verification by chemical experiments.
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.
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.