Evan Eichler
Professor of Genome Sciences
University of Washington
From this contributor
Autism and the complete human genome: Q&A with Evan Eichler
Scientists have at last filled in the missing gaps — an advance likely to inform every aspect of autism genetics research, Eichler says.
Autism and the complete human genome: Q&A with Evan Eichler
Remembering Steve Warren (1953-2021): A giant in the field of genetics
Steve Warren co-discovered the genetic mechanism that underpins fragile X syndrome and was a generous, inspiring mentor to many.
Remembering Steve Warren (1953-2021): A giant in the field of genetics
Questions for Evan Eichler: An evolving theory of autism
A gene that raises the risk of autism in some people may also give humans an evolutionary boost.
Questions for Evan Eichler: An evolving theory of autism
Explore more from The Transmitter
Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.