All News
Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries
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. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods.
Biomedical Data Science for Precision Health Equity Trainees Attend National Conference
Three PhD students supported by the Biomedical Data Science for Precision Health Equity training program, along with PI Professor Bogdan Pasaniuc (Computational Medicine) and Professor Alex Bui (Radiological Sciences, Bioengineering), attended the NLM T15 Training Conference, held at Stanford in June. All T-15 institutions from across the country participated in the three-day meeting, which featured a keynote address from Dr.
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Single-cell RNA sequencing (scRNA-seq) technologies have revolutionized biomedical sciences by enabling genome-wide profiling of gene expression levels at an unprecedented single-cell resolution. A distinct characteristic of scRNA-seq data is the vast proportion of zeros unseen in bulk RNA-seq data. Researchers view these zeros differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as false signals or missing data to be corrected.
Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus
Single-cell sequencing is transforming our understanding of complex tissues but their application to large population cohorts has been limited. Large sample sizes are particularly important for studying complex autoimmune diseases such as lupus where patients present a variety of symptoms and may respond very differently to current treatments. By using genetic information encoded in each single cell, we’ve previously developed a method called mux-seq to enable single-cell profiling of large populations.
Dr. Valerie Arboleda inducted into ASCI
Professor Valerie Arboleda was inducted into the American Society for Clinical Investigation (ASCI) on April 8, 2022 during the AAP/ASCI/APSA Joint Meeting in Chicago. She is one of 95 new members this year. The new members come from 46 different institutions and represent excellence across the breadth of academic medicine.
UCLA and Amazon Announce Inaugural Recipients of Research Gifts and Amazon Fellowships
The UCLA Science Hub for Humanity and Artificial Intelligence, a collaboration with Amazon, announced today its first cohort of 12 Amazon fellowships and six gift-funded research projects.
Bruins-in-Genomics: Evaluation of the impact of a UCLA undergraduate summer program in computational biology on participating students
Stacey Beggs | Computational Medicine Department
Introducing the Biomedical Data Science Training Program for Precision Health Equity
- Read more about Introducing the Biomedical Data Science Training Program for Precision Health Equity
Stacey Beggs | Computational Medicine
UCLA Scientists Develop New Algorithms to Study Genomic Data
UCLA Samueli Newsroom
UCLA computer scientists and genetics specialists in collaboration with their colleagues from several other institutions have developed a new genomic data computational method. Their improved algorithms can analyze genomic data up to 1,800 times faster than previous techniques, making it possible to analyze the genetic information of 1 million individuals in just one day.
UCLA SwabSeq funded by BARDA to develop “agnostic” virus test
Biomedical Advanced Research and Development Authority (BARDA)’s Division of Research, Innovation and Ventures (DRIVe) is collaborating with multiple industries and academic partners, including the University of California, Los Angeles, to advance the ability to quickly respond to public health emergencies with a new diagnostic capability that covers all existing and new respiratory RNA viruses in a single test.