Date: 1/18/24
Adit Radhakrishnan
Title: How do neural networks learn features from data?
1:30-3:00 pm in MSOB x303
More info here.
Treatment of metastatic disease is responsible for nearly one-third of the decrease in annual deaths from breast cancer from 1975 to 2019, according to a Stanford Medicine-led study.
“Jennifer Caswell-Jin and former research assistant Liyang Sun are co-first authors of the study, which was published Jan. 16 in the Journal of the American Medical Association. Sylvia Plevritis, PhD, professor and chair of biomedical data science, and Allison Kurian, MD, MSc, professor of medicine and of epidemiology and population health, are co-senior authors.”
To read the whole story, click here: https://med.stanford.edu/news/all-news/2024/01/breast-cancer-deaths.html
We are thrilled to announce that James Zou has been promoted to DBDS Associate Professor with tenure. A hearty congratulations and thanks for his endless hard work, revolutionary research, and constant contributions to DBDS.
Go, James!
Julia Salzman’s new paper on Cell today:
Today’s genomics workflows typically require alignment to a reference sequence, which limits discovery. We introduce a unifying paradigm, SPLASH (Statistically Primary aLignment Agnostic Sequence Homing), which directly analyzes raw sequencing data, using a statistical test to detect a signature of regulation: sample-specific sequence variation. SPLASH detects many types of variation and can be efficiently run at scale. We show that SPLASH identifies complex mutation patterns in SARS-CoV-2, discovers regulated RNA isoforms at the single-cell level, detects the vast sequence diversity of adaptive immune receptors, and uncovers biology in non-model organisms undocumented in their reference genomes: geographic and seasonal variation and diatom association in eelgrass, an oceanic plant impacted by climate change, and tissue-specific transcripts in octopus. SPLASH is a unifying approach to genomic analysis that enables expansive discovery without metadata or references.