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Professor Ken Lange honored at annual symposium

UCLA’s departments of Computational Medicine and Human Genetics, with support from NIH, held the fourth Lange Symposium on January 26th, 2024. The topic for this year’s symposium was Computational and Statistical Genomics. This annual event celebrates the impact of Dr. Lange’s research, mentorship, and teaching throughout an illustrious career spanning more than four decades. It featured scientific talks by some of Dr. Lange’s esteemed colleagues and former trainees:

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. 

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