Artificial Intelligence in Bioinformatics

О курсе

This course provides a comprehensive introduction to the application of artificial intelligence in bioinformatics, bridging computational methods with biological data analysis. Using Weka, a widely adopted machine learning software suite, learners will gain hands-on, practice-oriented experience alongside a solid theoretical foundation. Learners will explore the four core branches of bioinformatics—sequence analysis, structural bioinformatics, gene and protein expression, and network and systems biology—while gaining experience with widely used public bioinformatics databases. The course then covers the fundamentals of machine learning and deep learning, including data preparation, feature extraction, model evaluation, and key algorithms such as K-Nearest Neighbors, Random Forest, and Support Vector Machines. Through practical exercises in Weka and WekaDeeplearning4j, learners will build, tune, and evaluate predictive models for real-world bioinformatics problems, including protein function prediction and electron transport protein classification, using techniques such as Convolutional Neural Networks and Recurrent Neural Networks. By the end of this course, learners will be equipped with both the theoretical foundation and practical skills needed to apply AI-driven approaches to genomics and proteomics research, and to communicate their findings through effective scientific writing.

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Nguyen Quoc Khanh Le

Преподаватель курса