Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data
- Jovan Tanevski
- Thin Nguyen
- et al.
- Life Science Alliance
Pablo is manager of the Biomedical Analytics and Modeling group at the IBM Center for Computational Health. He joined IBM research in 2010 and received his Undergraduate degree in Physics from the University of Mexico UNAM (2000) and a Masters degree from the University of Paris VII/XI his Ph.D. in Genetics from the Rockefeller University (2005). He was awarded a Helen Hay Whitney fellowship as a posdoctoral fellow in Columbia university.
I am overall interested in applying algorithms and models to biological/healthcare data and specifically how events determine higher order phenomena/behaviour/disease from regulation of bacteria, mitochondrial fractional control of cell death (using our DEPICTIVE algorithm), circadian bevahiors in flies and prediction of human olfactory responses in disease (see full text of recent paper in Science)
I have been lately interested in Natural Language Processing and embeddings to understand olfactory perception (see our paper Nature comms) and its applications to diagnosis of disease (see a recent NYT article on our COVID predictions through smell analysis).
I am also a director of DREAM challenges, where we look for algorithmic and Artificial Inteligence solutions in Systems Biology via crowdsourced competitions, I recently organized the Single cell transcriptomics DREAM challenge, the DREAM of Malaria challenge and the Allen institute cell lineage DREAM reconstruction challenge.
You can check my TEDx talk on DREAM challenges, 'Breaking the Piñata of Science'.