International Workshop on Machine Learning Applications for R&D in Biotechnology
About Course
Held on 6th September to 12th September 2023 (7 to 8 pm)
Module 1: Introduction to Data Science in Biotechnology
- Understanding Data Science in the Biotech Context
- Overview of Prediction Algorithms in Biotechnology
- Importance of Data Quality and Preprocessing
- Hands-on: Data Preprocessing for Biotech Datasets
Module 2: Fundamentals of Machine Learning
- Supervised vs. Unsupervised Learning in Biotech Applications
- Key Machine Learning Concepts for Biotechnologists
- Database Handling for Biotech Data: Challenges and Solutions
- Introduction to File Formats and Conversions in Biotech Data
Module 3: Machine Learning Algorithms for Biotech Predictions
- Regression Algorithms for Continuous Data Prediction
- Classification Algorithms for Categorical Data Prediction
- Feature Selection and Dimensionality Reduction in Biotech Data
- Practical: Building a Predictive Model for a Biotech Case Study
Module 4: Applying Machine Learning in Cancer Genomics
- Cancer Genomics: Introduction and Significance
- Sequence Alignment Analysis in Cancer Genomics
- Introduction to Machine Learning Tools for Sequence Analysis
- Hands-on: Utilizing Machine Learning Software for Cancer Sequence Analysis
Module 5: Machine Learning in Cancer Genomics and Proteomics
- Naive Bayes Algorithm: Theory and Application in Biotech
- Naive Bayes for Cancer Genomics: Case Study and Examples
- Naive Bayes in Proteomics: Analyzing Protein Data
- Workshop Project: Implementing Naive Bayes for Biotech Data Analysis