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The Department of Computational Medicine has sponsored the Computational Genomics Summer Institute (CGSI) with funding from the NIH since 2016.
By Kevin McClanahan
Media Contact | David Sampson | DSampson@mednet.ucla.edu
The Department of Computational Medicine and especially the Biomathematics Ph.D. program wants to congratulate Rachel Mester and Apeksha Singh for finishing their Ph.D.
The Dissertation Year Award is intended to support doctoral students who are within one year of completing and filing their dissertation. This is the first time the Department of Computational Medicine has received two Dissertation Year Awards! The department is very proud of both Mariana and Xiangting and we look forward to the great work they will do during the upcoming academic year.
This fall, the Department of Computational Medicine at UCLA welcomes nine new students to its Biomathematics Ph.D. program. The incoming students have a diverse backgrounds in mathematics and biology, and they aspire to integrate different disciplines in their research.
Vivek Agarwal
By Kevin McClanahan
Researchers say a machine learning tool can identify many patients with rare, undiagnosed diseases years earlier, potentially improving outcomes and reducing cost and morbidity. The findings, led by researchers at UCLA Health, are described in Science Translational Medicine.
UCLA has received a $4.6 million grant from The Warren Alpert Foundation to establish th
More than four years after the world first learned about COVID that led to an unprecedented global health crisis in modern history and upended life as we knew it, UCLA researchers behind the SwabSeq COVID-19 PCR test came together November 13 in honor of SwabSeq’s third anniversary and its milestone of reaching 2 million processed tests.
Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or ‘fill-in’ missing phenotypes in population-scale biobank datasets.