Utilizing Public Data
All datasets are publicly archived — every analysis can be independently verified and extended.
Thousands of samples across tissues, diseases, and cohorts — far beyond single-lab capacity.
FAIR data principles enable cross-study meta-analyses and collaborative discovery.
Multi-cohort validation increases confidence that findings are not dataset-specific artefacts.
Three Data Modalities
Research Workflows
Bulk RNA-Seq Meta-Analysis
Large-scale transcriptomic discovery using harmonised public datasets from NCBI GEO & SRA
Single-Cell Harmonised Framework
Cross-cohort single-cell atlas construction and comparative cell-state analysis
ML / DL in Genomics
Machine and deep learning for biomarker discovery, disease classification, and precision medicine
Mentoring
I supervise students in reproducible bioinformatics — each mentee works through a full analysis, from public data retrieval to interpretation, with weekly one-to-one review.
Collaborators
Ongoing research partnerships across microbiology, plant biotechnology, and computational oncology.
Dr. Md. Salequl IslamProfessorDepartment of Microbiology, Jahangirnagar UniversityBangladeshScholar →
Dr. Iqbal MahmudSenior Research ScientistDepartment of Bioinformatics and Computational Biology, MD AndersonUSAScholar →
Dr. Yeon Ju KimAssociate ProfessorDepartment of Convergent Biotechnology & Advanced Materials Science, Kyung Hee UniversitySouth KoreaScholar →
If you have a dataset, a clinical question, or a study that would benefit from bulk / single-cell / spatial re-analysis or ML modelling, get in touch.
Start a conversation →