Eric Schadt is Mount Sinai Professor in Predictive Health and Computational Biology at the Icahn School of Medicine in New York City. He is also founder and chief executive officer of the predictive health company Sema4.
Eric Schadt
Professor
Icahn School of Medicine at Mount Sinai
From this contributor
Counseling can ease shock of unexpected genetic results
The best way to deliver surprises from genetic findings is to provide adequate information and counseling alongside the results.
Counseling can ease shock of unexpected genetic results
By
Eric Schadt
23 October 2018 | 6 min read
Explore more from The Transmitter
What a bird’s-eye view of half a million papers reveals about neuroscience
New research uses artificial-intellligence-driven bibliometrics to map the structural organization of neuroscience across 25 years. The field it reveals is at once thriving and theoretically adrift.
What a bird’s-eye view of half a million papers reveals about neuroscience
New research uses artificial-intellligence-driven bibliometrics to map the structural organization of neuroscience across 25 years. The field it reveals is at once thriving and theoretically adrift.
By
Mac Shine
6 April 2026 | 3 min read
Newly identified barrier cells seal off choroid plexus from CSF, rest of brain
A long-overlooked layer of fibroblasts exists inside the choroid plexus of mice and humans, adding complexity to the area’s compartmentalization.
Newly identified barrier cells seal off choroid plexus from CSF, rest of brain
A long-overlooked layer of fibroblasts exists inside the choroid plexus of mice and humans, adding complexity to the area’s compartmentalization.
By
Claudia López Lloreda
3 April 2026 | 4 min read
‘Digital sphinx’ raises questions about connectome models
The sphinx, with a worm’s brain and a fly’s body, illustrates the potential pitfalls of using deep-learning techniques to model biological processes.
‘Digital sphinx’ raises questions about connectome models
The sphinx, with a worm’s brain and a fly’s body, illustrates the potential pitfalls of using deep-learning techniques to model biological processes.
By
Natalia Mesa
2 April 2026 | 5 min read