Leandro Agudelo

Postdoctoral Associate

I work at the intersection between molecular metabolism and computational biology. My skill-sets are within the areas of molecular biology and computer science so I integrate them to understand the deep underlying order within complex biological systems. On the one hand, I apply machine and deep learning algorithms for pattern discovery in biological data, followed by experimental validation. On the other hand, I'm interested in finding ways to emulate strategies from evolutionary molecular adaptations to improve existing machine and deep learning systems. 
 

Projects

Geometric Deep Learning to Identify Biological Regulatory Networks

  • Geometric deep learning is on the rise. Its application to relational graphs is outstanding. Among the latter, complex biological networks represent one of the fields that could profit the most from this emerging technique
  • Developing methods to integrate systemic network approaches into single platforms. To this end, I am using concepts from the field of geometric deep learning. Briefly, in order to achieve combinatorial generalization of biological systems, I aim to systematically learn structured representations such as networks using relational collective behavior with deep learning. These methods support relational reasoning and generalization, which will help us understanding structured relations such as those observed in complex biological networks. The systematic combination of several principles of machine learning and network science will uncover relationships useful for drug-development and biomarker discovery.

Multi-omic Machine Learning in Metabolism and Disease

  • Use and develop hybrid deep learning models suitable to understand complex biological networks and study human disease. This approach will help us to infer comorbid molecular components across complex metabolic diseases such as type-2 diabetes and obesity.

  • Systemic genomic integration using multi-omic deep learning inference of regulatory networks governing metabolic adaptations. Currently focusing on immunometabolism.

Publications