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Machine Learning in Bioinformatics: From Zero to Hero
Machine learning is essential for bioinformatics, enabling advanced data analysis and innovation in modern research
6
12+
10
2
When and How
Start Date
24th of January 2025
Duration
1.5 – 2 hours per week
Online
via zoom
Overview
The “Machine Learning in Bioinformatics: From Zero to Hero” workshop is an intensive, hands-on program designed to equip participants with essential skills to leverage machine learning (ML) in bioinformatics research. Spanning foundational Python programming to advanced ML techniques for omics data analysis, this course is tailored for computational biologists, data scientists, and bioinformaticians eager to apply ML in real-world scenarios.
Why Machine Learning in Bioinformatics Matters?
Machine learning empowers bioinformaticians to analyze complex biological data, uncover patterns, and accelerate discoveries in areas like genomics and personalized medicine. It enhances data interpretation, automates tasks, and drives innovation, making it essential for modern biological research.
After completing this workshop, you will be able to:
- Understand basic Python syntax and utilize libraries like Numpy and Pandas for data analytics.
- Determine whether ML is suitable for addressing specific research questions and datasets.
- Identify appropriate ML algorithms for analyzing various omics data types.
- Detect and mitigate issues like overfitting and underfitting in ML models.
- Explore strategies to enhance ML model performance and interpret results effectively.
- Develop practical ML classifiers for tasks like phenotype classification, cancer staging, and drug response prediction.
Content
1. Introduction to Python (2 Sessions)
- Learn Python syntax, data structures, and numerical operations using Numpy.
- Master data manipulation, cleaning, and handling missing values with Pandas.
2. Machine Learning Fundamentals (2 Sessions)
- Explore the principles of selecting suitable ML models for bioinformatics data.
- Understand the limitations of ML and when to avoid its application.
- Build foundational ML pipelines for data normalization, cleaning, training, and evaluation.
- Practical Projects:
- Classify colorectal cancer using a 16S rRNA taxonomy table.
- Predict cancer stages from SNP data.
3. Enhancing Machine Learning for Sequences (2 Sessions)
- Implement techniques like feature selection, hyperparameter tuning, and data augmentation to boost model performance.
- Dive into DNA sequence analysis with Biopython, exploring feature extraction and sequence augmentation.
- Gain insights into deep learning approaches for DNA sequence data.
- Practical Projects:
- Predict drug response using expression data.
- Classify RNA sequences taxonomically with Biopython.
This workshop is ideal for:
- Computational biologists and bioinformaticians aiming to develop ML-based tools for multi-omics data.
- Data scientists looking to expand their expertise in applying ML to biological datasets.
No matter your level, this workshop will empower you with essential tools and insights.
- Sessions are recorded, so you can revisit the content anytime.
- Interactive Q&A after each session ensures personalized support.
- Flexible timing to accommodate participants from different time zones
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