Seyed Amir Malekpour

Seyed Amir Malekpour develops computational and statistical methods for analyzing genomic and gene regulatory data, with a particular focus on inferring gene regulatory networks, chromatin states, and copy number variation using logic-based and machine learning models. His work also touches on explainable AI methods for predictive modeling and applications to disease genetics such as Alzheimer's disease and viral molecular evolution. Students interested in bioinformatics, statistical genomics, or interpretable machine learning applied to biological data may find relevant methods and tools in his publications.

Publication output has been irregular with gaps in some years but shows a marked increase in recent years, especially a sharp rise in 2026.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026