Browsing by Author "Kawsar, Md"

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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.
Analysis of Gene Network Model of Thyroid Disorder and Associated Diseases
(Daffodil International University, 2021) Kawsar, Md
Chronic Kidney Disease (CKD), High Blood Pressure (HBP) and Thyroid Disorders (TD) are three relational diseases, when human affected by one of them, increases the possibility affected those people by other two diseases. Found large numbers of similarly biological and genetic features among HBP, CKD, and TD. In this investigation, we will find out behind the reason for 3 diseases are related to each other. Identify the common genes among HBP, CKD, and TD and finding the most significant genes. First step of this investigation by reducing the number of reactive genes intersection is obtained using RStudio. After completing this process, we have identified ten genes that are shared by HBP, CKD, and TD. Based on the biological, biochemical, and genetic relationships between common genes, this analysis identifies the most significant hub proteins. We designed Protein-Protein Interactions network, Co-Expression network, Enrichment Analysis, Topological properties analysis, Gene regulatory network, and Physical Interaction network based on our understanding of biological, biochemical, and genetic relationships. This analysis allows us to identify biological and genetic similarities between HBP, CKD, and TD. Finally, we've arrived at our targeted hub proteins.
Characterizing Topological Properties and Network Pathway Model among Vector Borne Diseases
(Informatics in Medicine Unlocked, Elsevier, 2020) Taz, Tasnimul Alam; Kawsar, Md; Paul, Bikash Kumar; Ahmed, Kawsar; Bhuyian, Touhid
Dengue, Malaria, and Chikungunya are vector-borne diseases, and are some of the most destructive diseases in the world. They cause millions of deaths per year. Our study, described herein, attempts to develop a significant solution in designing drugs for specified genes by analyzing the Protein-Protein Interactions (PPIs) network, topological properties, and pathway analysis. The steps of our paradigm include gene collection from the database, filtering, and finding the linkage of genes for these diseases. The initial step assists in the analysis by decreasing the total number of genes. Preprocessing, filtering, and linkage of genes were obtained by using the R language. After these analyses, we found four common genes. The topological properties for the related genes were identified. On the basis of the topological properties, Co-Expression and Physical Interaction networks were obtained. For systematic understanding of molecular mechanisms, we designed three types of gene regulatory networks. Based on our analyses, a drug is designed for the targeted genes.
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.