Dr. Saed Sayad
The advent of multi-omics technologies (e.g., genomics, transcriptomics, proteomics, and metabolomics) has brought the hope of discovering novel biomarkers that can be used to diagnosis, prognosis, and treatment of diseases. Data science has an important role in identifying biomarkers (biological markers) using data from Microarray and RNA-Seq experiments. In this hands-on tutorial, you will learn how to use data science and transcriptomic data to discover biomarkers for diagnosis, prognosis, response to treatment, monitoring and risk assessment.
November 2, 2022 at 3:00 PM UTC
1- Presentation (30 minutes) We are going to have a short presentation on the important role of data science 6-step process flow for biomarker discovery
2- Lab (60 minutes) There will be a hands-on project using omics data to find biomarkers and build PRS (Polygenic Risk Score) models. Please make sure you have R (https://www.r-project.org/) and RStudio (https://www.rstudio.com/) installed on your computer.
3- Demo (30 minutes) Writing and mainting R or Python codes is a time consuming task. I demonstrate a new amchine learning platform for the omics data analysis which saves your time and resources significantly.
Please make sure you have installed the following R libraries:
library(data.table)
library(formattable)
library(plotrix)
library(limma)
library(dplyr)
library(Rtsne)
library(MASS)
library(xgboost)
library(rafalib)
library(factoextra)
library(caTools)
library(pROC)
library(caret)
library(gains)
library(lift)- If you like it, leave your star in this project 🌟
- If you would like to complain/suggest/contribute to this project, feel free to open a issue 💟
- Please follow our FIXME - contributing guidelines.
If you use any of the materials of this lesson, please cite as:
This work is licensed under a Creative Commons Attribution 4.0 International License.
