guest lecture at U of Arkansas bioinformatics seriees

https://ualr.edu/bioinformatics/education-series/ Friday, October 16 at 2:00 pm CST Recorded Session available at https://youtu.be/6q9xHV5VznM Speaker Topic Dr. Hong Qin is an Associate Professor in the Department of Computer Science and Engineering at the University of Tennessee – Chattanooga who uses computational and mathematical approaches to investigate biomedical and biological questions. One focus is to develop probabilistic gene network models to infer network changes during cellular aging. We build gene network models from heterogeneous genomics data sets, including protein interactions, gene expression data sets, RNAseq data sets, protein mass-spec data sets, high-throughput phenotypic screens, and gene annotations. We are developing machine-learning methods to automatically estimate cellular lifespan from time-lapsed images. We are also applying engineering principles to study molecular, biological, and ecological networks. We are developing deep-learning methods for better classification and prediction using heterogeneous biomedical and biological large data sets. Dr. Hong Qin is a recipient of a NSF CAREER award 2015-2020. Qin’s expertise: Graph reliability modeling; Bioinformatics; Computational genomics; Mathematical modeling; Systems Biology; Cellular aging; Gene network analysis and modeling Dr. Qin will present how to use R to analyze COVID 19 data. R (along with bio-python and bio-perl) is one of the top choices for analyzing life science data. R is open source, runs on multiple platforms (Windows, Linux, MacOS, and cloud), has excellent packages for analyzing genomic data (e.g., Bioconductor), and has several nice interfaces for developing and running code (R Studio and Jupyter Notebook). The R code designed for this demo is available from https://github.com/hongqin/Use-R-in-CoLab/blob/master/Learn_R_UALR_CoLab.ipynb. This tutorial runs in Google’s CoLab cloud so no local installation is needed.