Publications


Journals:

  1. 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. IF=6.698

  2. 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. IF=8.665

  3. 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. IF=8.139

  4. 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.

  5. 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. IF=3.702

  6. 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. IF=3.702

  7. 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:

  1. 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).

  2. 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:

  1. 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.

  2. 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:

  1. 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.