Machine Learning in Gene Expression Data

BDG LifeSciences is a distinguished bioinformatics company established in 2010 and operates globally. Headquartered in India the company specializes in facilitating workshops, training programs, novel & innovative research projects, and online courses in technologies of bioinformatics & life science. The company is registered under the Ministry of MSME (Micro, Small, and Medium Enterprises), Government of India, with the registration number UDYAM-UP-01-0019151. Recently, BDG Lifesciences, India has authorized BBR Group Pty Ltd., Australia (ACN 608 550 849), to operate its programs in Australia & New Zealand.
With a focus on practical application of technology, where participants work on their own computer/laptop on software/servers,  BDG LifeSciences has been a leader in the sector for the last 14 years. Since its inception, BDG LifeSciences has successfully educated a diverse range of participants globally, including students, scientists, faculty members, professors, and corporate executives.

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Module 1: Introduction to Jupyter Notebook(Day 1, 60-90min)
•    How to install Jupyter notebook?
•    How to install Python packages in Jupyter notebook?

Practical:
•    Use Jupyter notebook.

Module 2: Introduction to Gene Expression Data(Day 1, 60-90min)
•    What is Gene Expression?
•    Techniques for Measuring Gene Expression: Microarrays, RNA-Seq
•    Sources of Gene Expression Data: GEO

Practical:
•    Download and explore gene expression datasets from the GEO database.

Module 3: Introduction to Machine Learning(Day 2, 60-90min)
•    Supervised and Unsupervised learning

Module 4: Feature Selection for Drug Target Identification(Day 2, 60-90min)
•    Introduction to Feature Selection: Why is it needed in gene expression data?
•    Techniques: Recursive Feature Elimination (RFE) using Python
•    Biological Relevance of Features

Practical:
•    Apply feature selection techniques to identify important genes from a dataset.

Module 5: Machine Learning for Drug Target Prediction (Day 3, 60-90min)
•    Predicting Drug Targets: Identifying genes with therapeutic potential
•    Classification Algorithms:SVM, Random Forests
•    Evaluating Models: Accuracy, AUC-ROC, Precision-Recall
•    Biological Interpretation of Results

Practical:
•    Train and evaluate a classifier to identify potential drug targets from gene expression data using Python.

Learning Outcomes:
•    Preprocess and analyze gene expression data.
•    Apply machine learning algorithms to identify important genes and potential drug targets.

Tools:
•    Databases: NCBI GEO
•    Python Programming: Jupyter Notebook
•    Machine learning tool: Python

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• In this Online Workshop, there will be a LIVE trainer who will solve queries along with training.
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