Publications
Publications
Journals (5 SCI + 2 Scopus):
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2021). UICPC: Centrality-based Clustering for scRNA-seq Data Analysis without User Input. Computers in Biology and Medicine, 104820.
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2021). UIFDBC: Effective density based clustering to find clusters of arbitrary shapes without user input. Expert Systems with Applications, 115746.
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2022). UIPBC: An Effective Clustering for scRNA-seq Data Analysis without User Input. Accepted in Knowledge-based Systems.
Chowdhury, H. A., Barah, P., Bhattacharyya, D. K., & Kalita, J. K. (2021). Identification of potential Parkinson’s disease biomarkers using computational biology approaches. Network Modeling Analysis in Health Informatics and Bioinformatics, 10(1), 1-16.
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2020a). (Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(4), 1154–1173.
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2020b). Differential Expression Analysis of RNA-seq Reads: Overview, Taxonomy, and Tools. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(2), 566–586.
Sahu, A., Chowdhury, H. A. et al. (2020). Integrative Network Analysis Identifies Differential Regulation of Neuroimmune System in Schizophrenia and Bipolar Disorder. Brain, Behavior, & Immunity-Health, 2, 100023.
Conferences:
Chowdhury, H. A. (2021, May). Effective Clustering of scRNA-seq Data to Identify Biomarkers without User Input. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 18, pp. 15710-15711).
Chowdhury, H. A., & Bhattacharyya, D. K. (2017). mRMR+: An Effective Feature Selection Algorithm for Classification. In International Conference on Pattern Recognition and Machine Intelligence (pp. 424–430). Springer.
Book Chapters:
Chowdhury, H. A., Ahmed, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2020). NCBI: A Novel Correlation Based Imputing Technique Using Biclustering. In Computational Intelligence in Pattern Recognition (pp. 509–519). Springer.
Chowdhury, H. A., & Bhattacharyya, D. K. (2016). Plagiarism: Taxonomy, tools and detection techniques. In Proceedings of the 19th National Convention on Knowledge, Library and Information Networking (NACLIN’16).
Poster:
Chowdhury, H. A. (2021). A Clustering Method for scRNA-seq Data without User Input. In the 35th AAAI Conference on Artificial Intelligence.
Work Done during Ph.D.:
First, I obtained a thorough understanding of how next-generation sequencing data is generated, as well as the numerous computational methods for effectively analysing count data. Then, using computational biology approaches, I analysed two RNA-seq neurodegenerative disease datasets and discovered a few potential biomarkers. Next, I concentrated on scRNA-seq data, first developed a pipeline for effective scRNA-seq count data preprocessing, then evaluated the effectiveness of 20 state-of-the-art clustering methods on scRNAseq data and developed two scRNA-seq-specific clustering approaches to identify cell groups. Finally, I developed a density-based clustering method that can discover arbitrary-shaped clusters. All these clustering methods don't require any input from the user and are available as a R package. I have published my research work in reputed journals and Conferences such as ‘AAAI proceedings, IEEE/ACM TCBB, Expert Systems with Application, Knowledge-based Systems, and many more.