Artificial Intelligence in Bioinformatics

Taipei Medical University via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

6 weeks, 2 hours a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Overview

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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.

Syllabus

Basic concepts of AI in bioinformatics data

This module offers a comprehensive overview of Bioinformatics, an interdisciplinary field that integrates computer science, statistics, mathematics, and biology. This module will introduce the field's four core branches: sequence analysis, structural bioinformatics, gene and protein expression, and network and systems biology. Additionally, the course will guide you through various public bioinformatics databases and teach you how to effectively collect and apply different types of biological data. Through this module, you will build a solid foundation and gain a deep understanding of how computational technologies and public resources drive major discoveries in modern genomics and biomedicine.

Artificial Intelligence: fundamentals and applications

This module introduces the fundamentals and practical applications of artificial intelligence in a more accessible way. It begins by explaining what AI is in scientific terms and outlines its main branches, including deep learning, natural language processing, and robotics. You will then be guided through the practical workflow of machine learning, with a focus on how data is prepared and used in tools such as Weka and Python. The module also covers key methods for evaluating models, including cross-validation, confusion matrices, precision, and recall, helping you understand how to measure model performance in a meaningful way. Finally, it explores several widely used machine learning algorithms, such as K-nearest neighbors, random forests, and support vector machines, with an emphasis on how they work in practice. Overall, this module aims to build a clear and practical understanding of data-driven decision-making.

Bioinformatics feature engineering

This module introduces feature learning principles within a bioinformatics framework, focusing on the prediction of electron transport proteins. It delineates a structured four-stage workflow comprising data collection, feature extraction using metrics like Amino Acid Composition, feature learning via machine learning algorithms, and final model evaluation. Additionally, the curriculum provides practical training in Weka software, demonstrating how to save and load predictive models, perform hyperparameter tuning through controlled experiments in Weka Experimenter, and utilize ArffViewer for converting CSV data into the ARFF format.

Feature Learning

This module introduces feature learning principles within a bioinformatics framework, focusing on the prediction of electron transport proteins—key mediators of cellular respiration. It delineates a structured four-stage workflow comprising data collection, feature extraction using metrics such as Amino Acid Composition, Dipeptide Composition, and Position-Specific Scoring Matrix, feature learning via machine learning algorithms including K-Nearest Neighbors and Random Forest, and final model evaluation through cross-validation and independent test sets. Additionally, the curriculum provides practical training in Weka software, demonstrating how to save and load predictive models for reuse without retraining, perform hyperparameter tuning through controlled experiments and paired t-tests in Weka Experimenter, and utilize ArffViewer for converting CSV data into the ARFF format required for classification tasks.

Deep Learning

This module transitions from traditional machine learning frameworks to the advanced domain of deep learning and sequence analysis. This week, we will investigate how multi-layered artificial neural networks perform autonomous representation learning, bypassing manual feature engineering to extract intricate biological patterns from large-scale datasets. Our curriculum provides an objective evaluation of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Natural Language Processing (NLP) workflows, demonstrating their functional application to genomics and proteomics. To ground these scientific concepts, you will complete hands-on technical implementations using the WekaDeeplearning4j software suite and specialized Python packages, mastering network configuration and localized hyperparameter tuning. Please thoroughly review the assigned video lectures and documentation before starting your practical exercises.

Writing Bioinformatics Papers

This module focuses on scientific communication in bioinformatics, guiding learners through the process of transforming bioinformatics analyses into a publishable research paper. You will explore the standard bioinformatics workflow—from data collection and feature extraction to model development and prediction—and learn how these steps are presented in a research paper. The module also covers dataset construction, feature representation, data visualization, and scientific reporting, using a published bioinformatics paper as a practical example.