Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
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. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
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.
📋 Course Objectives
1. Put biotechnology techniques to work on real datasets and case studies.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A portfolio-grade biotechnology deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Microarray Data and Its Structure
• Affymetrix, Illumina and two-colour platforms and the differences that matter
• CEL files, probe-to-gene mapping and multi-probe genes
• Retrieving and reading experiments from GEO with GEOquery
Getting to an Expression Matrix
• Background correction, RMA normalisation and quantile normalisation in affy and oligo
• Array quality assessment and the defensible grounds for excluding an array
• Batch effect detection by PCA before any modelling is attempted
Differential Expression
• limma, the linear model framework and empirical Bayes moderation
• Design and contrast matrices for anything beyond a two-group comparison
• FDR control and why an unadjusted p-value list is not a result
Machine Learning on Expression Data
• Classification with caret or tidymodels when features vastly outnumber samples
• Feature selection inside the resampling loop, or the accuracy is inflated
• Clustering, heatmaps and the arbitrariness of the chosen distance metric
Interpretation and Validation
• Over-representation against GSEA and the correct background gene set
• Validation in an independent GEO dataset as the honest check
• Where microarray conclusions do and do not agree with RNA-Seq
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | XGBoost |
Frequently Asked Questions
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