[SUB EN/FR/RU] Practical session for omics data analysis by machine learning | AI4Omics

Описание: Github Repository: https://github.com/epimed/ai4omics-pr...

This video presents a practical session of the Master Course Unit “AI for Omics Data Integration” of the University of Grenoble Alps. It is a part of the Master Program “AI4OneHealth”.

The objective of this session is to develop practical skills in omics RNA-seq data analysis using machine learning techniques. We will use Python programming language and the machine learning framework scikit-learn. The exercises will be present in a format of Jupyter notebooks.

The practical session is composed of two main tasks. The first task aims to introduce you to a basic machine learning pipeline and to show how to create a simple classifier. In the second task, you will be invited to create a logistic regression classification model for cancer diagnosis using omics data. The solutions for all the exercises will be available.

The video includes comprehensive instructions to install the software for the practical session: Python, packages, Jupyter notebook, session materials on Github.

Presented by: Ekaterina Flin, EpiMed group, Institute for Advanced Biosciences (IAB), University of Grenoble Alps (UGA)

#machine_learning #scikit_learn #omics

Timeline
00:00 Introduction
00:40 Objective of the practical session
01:10 Main tasks
02:07 Transcriptome data RNA-seq
02:49 Expression patterns in normal cells and in cancer
04:03 Data of colon cancer
04:26 Goal: recognize colon cancer
04:57 Github repository
05:28 Python installation
05:43 Installation of packages
06:22 Download of session materials
07:19 Jupyter Notebook
08:25 You are ready to start


Links
Master Program AI4OneHealth: https://www.masterai4onehealth.eu
Course Unit "Main challenges of machine learning for omics":    • 1. Main challenges of machine learning for...  
Github Repository of the practical session: https://github.com/epimed/ai4omics-pr...
Task 1 solution (video):    • [SUB EN/FR] Basic machine learning pipelin...  
Task 2 solution (video):    • [SUB EN/FR] Logistic regression with sciki...