Debora is a Senior Scientific Fellow at GenBio AI and a mathematician and computational biologist with a track record of developing novel algorithms and statistical methods to address fundamental, previously unsolved problems in biology. Her work focuses on interpreting genetic variation in ways that directly impact biomedical applications.
During her PhD, she quantified the potential pan-genomic scope of microRNA targeting and combinatorial regulation of protein expression and co-discovered the first microRNA encoded by a virus. As a postdoctoral researcher, she and her colleagues solved the long-standing problem of ab initio protein structure prediction by introducing a maximum entropy probabilistic model for evolutionary sequence data.
She has since expanded this approach to infer functional interactions and biomolecular structures, including the three-dimensional structures of RNA and RNA–protein complexes, as well as the conformational ensembles of intrinsically disordered proteins. Her research interests also include developing deep learning methods to predict the effects of genetic variation and to enable sequence design for biosynthetic and therapeutic applications.
Biography
Debora is a Senior Scientific Fellow at GenBio AI and a mathematician and computational biologist with a track record of developing novel algorithms and statistical methods to address fundamental, previously unsolved problems in biology. Her work focuses on interpreting genetic variation in ways that directly impact biomedical applications.
During her PhD, she quantified the potential pan-genomic scope of microRNA targeting and combinatorial regulation of protein expression and co-discovered the first microRNA encoded by a virus. As a postdoctoral researcher, she and her colleagues solved the long-standing problem of ab initio protein structure prediction by introducing a maximum entropy probabilistic model for evolutionary sequence data.
She has since expanded this approach to infer functional interactions and biomolecular structures, including the three-dimensional structures of RNA and RNA–protein complexes, as well as the conformational ensembles of intrinsically disordered proteins. Her research interests also include developing deep learning methods to predict the effects of genetic variation and to enable sequence design for biosynthetic and therapeutic applications.