Analysis of Microarray Data using Machine Learning/AI in R

Decoding Genetic Data: AI-Powered Microarray Analysis in R.

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MODE
Virtual (Google Meet)

TYPE
Mentor Based

LEVEL
Moderate

DURATION
3 Days (1.5 hours per day)

Start Date
9 -January -2025

Time
8:00 PM IST

About

Microarrays are one of the most common tools to understand biological spectacle by large-scale dimensions of biological samples, typically DNA, RNA, or proteins. The technique has been used for a variety of purposes in life science research, ranging from gene expression profiling to SNP or other biomarker identification, and further, to understand relations between genes and their activities on a large scale. Artificial intelligence (AI) and machine learning (ML) techniques can be used to analyse microarray data to gain insights into biological processes. Machine learning tools can automate the analysis of microarray data to identify patterns of gene expression. These patterns can be used to compare gene expression between different conditions, such as healthy and diseased cells. There are no curated machine learning/AI-ready datasets that meet the requirements for machine learning analyses within public functional genomics repositories at the moment.
The R language supports identifying gene expression through Bioconductor packages to show all differential gene expressions by generating the volcano map, Euclidean distances to perform clustering, Venn diagram, and heatmap.

Aim

The aim of microarray data analyses is the identification of genes showing significant differences in expression levels between two or more groups.

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Workshop Objectives

Master the fundamentals of microarray technology and its applications.
Utilize R to preprocess, analyze, and interpret microarray data.
Apply AI and machine learning techniques to uncover patterns in complex genetic datasets.
Develop skills in statistical analysis and biomarker identification.
Understand the ethical implications of genomic data handling and reporting.

Workshop Structure

DAY 1: Functional genomics repositories

General Overview of public genomics repositories
Overview of GEO2R, procedure to use, Edit options and features, Limitations and caveats, Summary Statistics.

DAY 2: R studio and Bioconductor packages

R and Bioconductor packages installation
GEOquery and limma Bioconductor package

DAY 3: Microarray Data Analysis

Gene expression analysis with GEOquery and limma for Microarray data analysis

Participant’s Eligibility

Undergraduate degree in Bioinformatics, Genomics, Computational Biology, or related fields.
Professionals in biotechnological, pharmaceutical sectors, or clinical research.
Individuals with foundational knowledge of molecular biology and an enthusiasm for computational data analysis.

Important Dates

Registration Ends

2025-01-09
Indian Standard Timing 07:00 PM

Workshop Dates

2025-01-09 to 2025-01-11
Indian Standard Timing 8:00 PM

Workshop Outcomes

Proficiency in analyzing microarray data using advanced AI and machine learning techniques
Capable of contributing to genetic research, diagnostics, and therapeutic developments

Mentor Profile

Fee Structure

Student

INR. 1399
USD. 50

Ph.D. Scholar / Researcher

INR. 1699
USD. 55

Academician / Faculty

INR. 2199
USD. 60

Industry Professional

INR. 2699
USD. 85

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