
Frontiers in Computational Biosciences Seminar Series: Justin Sonnenburg, PhD, Alex and Susie Algard Endowed Professor, Microbiology & Immunology – Stanford University
Boyer Hall 159TITLE: "The Predicament of the Industrialized Gut Microbiome"
TITLE: "The Predicament of the Industrialized Gut Microbiome"
TITLE: "Fair AI for Health: Reducing Bias in Predictive Models"
The analysis of imaging datasets is both exciting and challenging. New and increasingly powerful techniques try to maximize the information derived from multi-dimensional imaging datasets. Yet, every dataset can be a unique analysis challenge, and packages may not always work out-of-the-box. In this workshop, we will explore some popular computational tools to extract quantitative information […]
Fueled by technological innovations, online databases and algorithmic developments, biomedical research is rapidly becoming a data science. In this webinar, three of the most innovative UCLA faculty will share their perspectives on how biological big data are enabling their research. We will hear about robotic instrumentation that generate big data about drugs and genes, how diverse […]
This workshop will introduce the basic principles of DNA methylation and briefly describe available methods for DNA methylation assessment, focusing on bisulfite sequencing (BS-Seq). We will cover the principles of BSseq preprocessing, alignment and methylation calling, differential methylation analysis, annotation and visualization. Attendees will have the opportunity to work on real DNA methylation data provided […]
TITLE: "Spatial Single-Cell Mapping of Transcriptional Differences Across Genetic Backgrounds in Mouse Brains"
TITLE: "Chromosome-scale drift under stabilizing selection, with evolutionary implications"
Mergeomics is a computational tool designed to elucidate the underlying pathways, networks, and key regulators of complex diseases by integrating multi-omics data (genomics, transcriptomics, epigenomics, proteomics, etc). With use of integrative omics concepts and network modeling methods, this tool can uncover disease-associated gene sets and biological pathways across omics layers and can identify key drivers […]