Janine LaSalle is associate director of genomics at the University of California, Davis, Genome Center.
Janine LaSalle
Associate director of genomics
Genome Center, University of California, Davis
From this contributor
Chemical tags reveal interplay of genes, environment in autism
The landscape of chemical modifications on the DNA of people with autism could reveal clues to the condition and lead to treatments.
Chemical tags reveal interplay of genes, environment in autism
By
Janine LaSalle
20 June 2017 | 5 min read
Genes and environment are two-way street in autism risk
Genes and the environment each influence the role of the other in determining the risk of developing autism. Genetics can determine how susceptible one is to the environment, and environmental factors can influence gene expression and introduce mutations, says immunologist Janine LaSalle.
Genes and environment are two-way street in autism risk
By
Janine LaSalle
21 August 2012 | 6 min read
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.
By
Lauren Schenkman
20 May 2025 | 6 min read
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.
By
Jill Adams
20 May 2025 | 2 min read
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.
By
Alona Fyshe
19 May 2025 | 7 min read