Browsing by Author "Islam, Md. Manowarul"

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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.
Mining and Predicting Protein-drug Interaction Network of Breast Cancer Risk Genes
(Gene Reports, Elsevier, 2020-06-15) Islam, Muhammad Nahidul; Shaolin, Shams Shah; Paul, Bikash Kumar; Bhuyian, M, Touhid; Ahmed, Kawsar; Touhid, Bhuyian; Islam, Md. Manowarul
Background and objective The increasing number of patients of Breast Cancer (BC) is a matter of concern all around the world. There isn't any specific reason behind this disease to be occurred in the human body. But there are such diseases which are counted as a risk factor of BC. Atypical Hyperplasia (AH) and Lobular Carcinoma in SITU (LCIS) are two of them. In this research we have collected the gene list of BC, AH and LCIS from the NCBI gene database and then our target was to find out the common genes among them by implementing an intersecting program. Method Then we used those intersected data to construct and analyze the Protein-Protein Interaction Network (PPIN), Protein-drug interaction network and so on. After the end of this we have got the most common fundamental genes associated with these diseases, the interrelated interaction networks of this 3 disease that will help us to better understand the common gene structure of them. Results We have got a drug signature suggestion for the hub proteins in the PDI network. From PPIN, 9 most responsible hub genes are obtained. Finally, following the PDI network, 4 of those hub genes were selected where several drug molecules from DRUGBANK were suggested for each hub gene. Conclusions In future by implementing further studies and analyzing those gene structures, it may be able to notify us to take precautionary steps to reduce the risk of BC.