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Data Science for Biomarkers Discovery

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

Workshop Outline:

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)

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Citation

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License

This work is licensed under a Creative Commons Attribution 4.0 International License.

CC BY 4.0