News


Martin Kampmann, PhD
Michael Keiser, PhD

Yesterday, the Chan Zuckerberg Initiative (CZI) launched the CZI Neurodegeneration Challenge Network. This new network brings together scientists from diverse research fields to understand the underlying causes of neurodegenerative diseases. Following an international open call for applications, two IND faculty, Martin Kampmann and Michael Keiser, were among the 17 recipients of a Ben Barres Early Career Acceleration Award, which provides $2.5 million in research funding for each recipient.... Read more ...

In their Technical Comment on “Predicting reaction performance in C–N cross-coupling using machine learning (Reports, 13 April 2018),” Kangway Chuang, Postdoctoral Fellow in the Keiser Lab, and Mike Keiser, Assistant Professor In-Residence, report that Ahneman et al.'s “experimental design [was] insufficient to distinguish models trained on chemical features from those trained solely on random-valued features in retrospective and prospective test scenarios, thus failing classical controls in machine learning.”

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Kampmann Lab, July 2018

A major goal of the Kampmann Lab is the identification of new therapeutic targets for neurodegenerative diseases, such as Alzheimer’s disease. The lab has developed a CRISPR-based screening platform to identify genes and pathways controlling disease processes in neurons and glia from human patient-derived induced pluripotent stem cells. The Innovative Genomics Institute, directed by CRISPR pioneer Dr. Jennifer Doudna, named Dr. Martin Kampmann an IGI Investigator, and supports the Kampmann lab to facilitate the translation of research insights to therapeutic strategies. Kampmann lab members... Read more ...

Keiser Chuang ACS Chemical Biology Machine Learning AI UCSFIn a new “In Focus” article in ACS Chemical Biology, IND Assistant Professor In-Residence Mike Keiser and postdoc Kangway Chuang stress caution when applying machine learning to experimental research models. With new machine learning methods broadly accessible to the research community, Keiser and Chuang emphasize the continued importance of designing strong control experiments and argue that a “hypothesis-driven approach” to research design is “now more critical than ever”.

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