Computational Biology: The Application of Artificial Intelligence algorithms like Machine Learning in biomedicine

Course 15: Computational Biology: The Application of Artificial Intelligence algorithms like Machine Learning in biomedicine

I. Course Description

With the rapid development of medical technology, intelligent medical technology is becoming the engine of medical innovation, injecting unprecedented intelligence and data-driven into the traditional medical mode. Using advanced technologies such as machine learning and deep learning, intelligent medical systems can quickly analyze large medical data sets and provide more accurate diagnosis and treatment solutions. In addition, the continuous upgrading of smart medical devices, such as convolutional (CNN) network based on medical imaging and recurrent neural network (RNN), make clinical decisions more scientific and accurate. By integrating the Internet of Things and cloud computing technologies, real-time monitoring and sharing of medical data will be possible, prompting healthcare workers to make decisions more quickly and improve medical efficiency.

The course focuses on learning applications in biomedical science and bioengineering, bridging theoretical learning and practical applications, and providing students with basic knowledge of machine learning, including its principles, operations, and execution of algorithms. Students will learn math and coding to develop and implement machine learning solutions. Starting with the basic knowledge, we will move into case studies in biomedical science and bioengineering, showing how machine learning can solve complex problems in these fields. The goal of the course is to master students in machine learning to understand it, discuss it and apply it. At the end of the course, students will be able to examine issues in the biomedical and bioengineering fields and determine which machine learning tools can create effective solutions.

II. Professor Introduction

James Choi – Tenured Professor in Bioengineering, Imperial College London

Professor James J. Choi is a tenured professor in the Department of Bioengineering at Imperial College London and is also the head of the PhD Training Program in Intelligent Medical Imaging at Imperial College London. As empire institute of noninvasive surgery and biological testing laboratory founder, he led the team to develop minimally invasive surgery equipment and methods, solve the neurodegenerative diseases and brain tumors, for the treatment of Alzheimers disease, glioblastoma and diffuse medulloblastoma disease provides a new method, and won a number of international well-known awards.

His laboratory conducts research in hardware, algorithms, physics, biology and translation, and provides a solid foundation for transforming scientific research results into practical applications through interdisciplinary cooperation. Professor James J. Choi has published many academic papers such as “Passive Cavitation Detection with needle Hydrophone Array” in IEEE and other international top conference journals. His current research interests focus on biomedical engineering-non-invasive device noninvasive microscopy, brain drug supply, and live tissue and pathology diagnosis.

III. Syllabus

1. Introduction to machine learning
2. supervised learning
3. unsupervised learning
4. Foundation of deep learning
5. Advanced deep learning
6. Integration of machine learning and biomedicine
7. Biomedical imaging and machine learning
8. Biomedical signal processing
9. Medical device design and machine learning
10. Genes, proteins, and drugs