Maral Aminpour

Area of Study / Keywords

Precision Health Design

About

Maral Aminpour is currently appointed in the Department of Biomedical Engineering at University of Alberta. 

Research

Our computational Lab is located in the Biomedical Engineering Department at the University of Alberta in Edmonton. We specialize in developing computer models to aid in drug discovery and the understanding of biological systems and their interactions. Currently, our research is focused on repurposing drugs for COVID-19, optimizing anti-tubulin cancer drugs, and creating new software that utilizes a hybrid method of artificial intelligence and physics-based methods to study protein-protein interactions.

Please check my lab's website: https://www.maralaminpour.ca

BME 420 - Rational Drug Design and Discovery: Principles and Applications

An introduction to topics and tools in structure-based and ligand-based in silico drug design such as molecular dynamics, virtual screening, receptor-drug interactions, and pharmacophore modeling. Holistic view of drug discovery, including topics such as pharmacokinetics and systems biology, role of artificial intelligence in drug discovery, and an introduction to precision medicine. Credit cannot be obtained for both BME 420 and BME 620.

BME 477 - Machine Learning for Biomedical Applications

Applications of machine learning tools to real-world problems in biomedical engineering including diagnostic and prognostic applications. An introduction to machine learning. Machine learning tools: regression and classification; manifold learning and dimensional reduction; decision trees and ensemble learning; unsupervised learning and clustering; feature selection and feature extraction; neural networks and deep learning. Biomedical applications: cancer, cardiovascular disease, diabetes, neurological diseases and infectious diseases. Credit cannot be obtained for both BME 477 and BME 677.

BME 620 - Rational Drug Design and Discovery: Principles and Applications

An introduction to topics and tools in structure-based and ligand-based in silico drug design such as molecular dynamics, virtual screening, receptor-drug interactions, and pharmacophore modeling. Holistic view of drug discovery including topics such as pharmacokinetics and systems biology, Artificial Intelligence's role in drug discovery and an introduction to precision medicine. Credit cannot be obtained for both BME 420 and BME 620.

BME 677 - Machine Learning for Biomedical Applications

Applications of machine learning tools to real-world problems in biomedical engineering including diagnostic and prognostic applications. An introduction to machine learning. Machine learning tools: regression and classification; manifold learning and dimensional reduction; decision trees and ensemble learning; unsupervised learning and clustering; feature selection and feature extraction; neural networks and deep learning. Biomedical applications: cancer, cardiovascular disease, diabetes, neurological diseases and infectious diseases. Credit cannot be obtained for both BME 477 and BME 677.

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