Master Program


Master & PhD Programs in Department of  Bioinformatics and Medical Engineering 

Course Description - Bioinformatics and Medical Engineering

Course Title

Course description

Credits

Artificial Neural Networks

Basic concepts in neural computing; functional equivalence and convergence properties of neural network models; associative memory models; associative, competitive and adaptive resonance models of adaptation and learning; selective applications of neural networks to vision, speech, motor control, and planning; neural network modeling environments. Students will understand the basic concepts, principles, mathematical models, and applications of some classical neural network models. Students will gain experience in applying neural networks to problem-solving using MATLAB. Students will understand practical applications and have interests by reading articles.

3

Bioinformatics

 

This course covers the following topics: sequence alignment, dynamics programming, NCBI database, gene annotation, gene prediction, molecular phylogenetics, protein structure, and RNA structure. 

3

Biomaterials

Biomaterials are materials that are applied in medical devices or in contact with biological systems. Biomaterials as a field has seen steady growth over its approximate half-century of existence and use ideas from materials science, chemistry, biology, medicine, and engineering. This course provides students with a perfect introduction to the world of biomaterials, linking the basic characteristics of metals, polymers, ceramics and natural biomaterials to the unique advantages and limitations of their biomedical applications. The clinical issues such as sterilization, surface modification, cell-biomaterial interactions, drug delivery systems, and tissue engineering have been discussed in detail so that students have a practical understanding of the real world challenges associated with biomaterial engineering. The purpose of the course is to provide students with the special meaning of the term biological material, as well as the rapid and exciting evolution and expansion of biomaterial science and its application in medicine. At the end of the semester, all students should identify and understand the main terms used primarily in the biomaterials, the basic properties of the various biological materials, the correct association of the terms with the process/phenomenon, and the ability to correlate the relevant events.

3

Bioinformatics Algorithms

 

This course highlights how a biological problem can be transformed into a computational problem in a number of ways that feature different levels of accuracy and complexity. Highly accurate models often result in intractable computational problems while less accurate models may produce meaningless results. The main goal is to maintain an acceptable level of accuracy keeping the computational problem effectively solvable.

3

Biologically Inspired Computing

Biological organisms cope with the demands of their environments using solutions quite unlike the traditional mathematical approaches to problem solving. Biological systems tend to be adaptive, reactive, and distributed. Bio-inspired computing is a field devoted to tackling complex problems using computational methods modeled after design principles encountered in nature. The goal is to produce informatics tools with enhanced robustness, scalability, flexibility and which can interface more effectively with humans. Students will be introduced to fundamental topics in bio-inspired computing, which gives students a chance to observe and compare different natural behaviors that can be utilized for computation. Students will build up their proficiency in the application of various algorithms in real-world problems. Through the study of animal behaviors, students will be interested in the beauty of natural phenomena, which are in essence highly excellent optimizers.

3

Bio-optics

This course is designed for students who do not have basic knowledge of bio-optics. This course will provide an introduction to make students have a basic knowledge of optics and understand the biomedical applications of optical techniques. In this course, students can learn how to get images and data through optical instruments. To arise the learning interests of students, some novel optical biomedical sensing technologies will be lectured in this course. Students need to read some review papers of optical techniques and give presentations in the weeks of the midterm and final exams. At the end of this course, students should know the principles of optical techniques, the applications of optical techniques, the development of bio­-optics, and the advantages and disadvantages of these optical techniques.

3

Biotechnology

This course is designed for non-biology students, such as from computer science, engineering, mathematics and others. This course will provide an introduction to the current trends in Biotechnology researches and their application. At the end of this course, students will gain basic level knowledge in Biotechnology and applying these to their research.

3

Biomedical Image Processing

This course is designed for students who have basic programming skill and interest in the digital image processing technology. This course will provide an introduction to educating students to have a broad understanding of image processing technology. In this course, students need to learn the program writing skill with the Matlab language and use Matlab program to carry out image processing assignments. The course will combine examples and exercises in teaching, so that students can learn the theory and application of technology at the same time. Students need to complete several homework assignments and also have to submit the midterm and final reports. At the end of this course, students should learn the basic principles of medical images, the use of Matlab programming language, basic skills of analyzing images and the reconstruction of a 3D object from images.

3

Bio-Sensing Technology

This course developed to present a basic understanding of bio-sensor principles and applications. The research and development in the bio-sensors is an extremely dynamic area of current science and technology. This course is divided into two main categories—(1) theoretical understanding of various physical and chemical phenomena behind the operation of different types of sensors and micro-systems, (2) designing of sensors with appropriate electronic interface as a complete system. To enable students to understand the biomedical sensing technologies currently used in the biological medicine, and have basic concepts for these theoretical foundations. To teach students to understand how to get these sensing data via the introduction of this course, and have complete and correct concepts for these technologies. To teach many kinds of medical sensing technologies for arousing students learning interest in biomedical detection technology.

3

Basic Theories of Biomedical Images and instruments

This course is designed for the students who interest in the imaging systems used in the hospital. This course introduces basic medical image processing concepts and imaging techniques. Principals of X-ray, computerized tomography, magnetic resonance imaging ultrasound imaging, optical coherence tomography and so on will be lectured in this course. This course also introduces some commonly used image processing methods for biomedical images. Students need to read some review papers of imaging technologies and give presentations in the weeks of the midterm and final exams. In the end of this course, students should know the principles of imaging techniques, instruments, and the basic concepts for medical image processing.

3

Cell Biology

This course is designed for non-biology students, such as from computer science, engineering, mathematics and others. This course will provide an introduction to the structure and biology of the eukaryotic cell, with a special focus on the cell/cell and cell/ECM interaction, endomembrane trafficking, cytoskeleton and signal transduction pathways. At the end of this course, students will gain basic level knowledge in cell biology and applying these to their research.

3

Data Mining

Data mining, or called knowledge discovery, is the processes of analyzing data from different perspectives, deriving useful information, and finally acquiring knowledge. The applications of data mining are now prevalent in varied domains, such as stock prediction, customer behavior analysis, and social network. In this course, we will learn what data mining can do and how to do it. Some important concepts and techniques, including association rules, clustering, classification, and artificial intelligence, will be discussed.

3

Medical Device Development and Regulation

This course introduces the design and application of medical devices, and include the relevant laws and regulations. Therefore, students can understand the requirement to comply with these norms, and they can design products to meet the regulatory requirements in the future. The lectures in this course include the basic concepts of medical instrumentation, principles of basic sensors, amplifiers and signal processing, the origin of biopotential and so on. After learning this course, the students should obtain the foundations necessary to build a strong understanding of the development of medical devices.

3

Molecular Biology

This course is designed for non-biology students, such as from computer science, engineering, mathematics and others. This course will provide an introduction to the molecular basis of life and inheritance, with a detail in the structure and function of biological macromolecules and the biochemical mechanisms that control the maintenance and expression of the genome in prokaryotes and eukaryotes. Students will work on several assignments and pass two examinations. At the end of this course, students will gain basic level knowledge in molecular biology and applying these to their research.

3

Molecular Medicine

The goal of this course is to discuss the aspects of molecular medicines from the causes, development and diagnosis through to the treatment of diseases, with special focus on the overview of the molecular mechanisms incorporating modules from immunology to signaling, from virology to gene therapy, and the latest development in personalized medicine introduction into the molecular basis of diseases and the novel treatment options that have become available. This includes an analysis of cellular structures and organelles, protein structures and functions, nucleic acid biochemistry, replication and repair of DNA, the processes of transcription and translation, regulation of gene expression, modern molecular techniques used for diagnosis and research, proteins, purines and pyrimidines, and human genetics. After having completed this course, the students shall have obtained a basic understanding of various human diseases and the underlying molecular, genetic or biochemical basis for the pathogenesis of the clinical disorders and their possible treatments.

3

Omics

This course is designed for students who major in engineering and science. Omics is a new area of study in molecular biology that examines the feature of a large family of biological molecules, such as DNA, mRNA, proteins, metabolites, lipids and carbohydrates (saccharides). This course is designed to give students a general understanding of the genomes, transcriptomes, proteomes and their integration, i.e. omic. Genomes, transcriptomes, proteomes are the large-scale study of genes, transcripts, and proteins. In addition, this course will also cover the following topics: regulatory elements, epigenetic mechanism (DNA methylation, chromatin modeling), and non-coding RNA biology. Networks of interactions are fundamental to all biological processes. In the last ten years, we began to see much progress in analyzing biological networks using the random graph approach. Network motifs are patterns that occur more often than their randomized parts. A complex network can be characterized by certain topological measurements. Students will learn those techniques in the course.

3

Robotic Computing

Robotic Computing addresses computing technologies, and their synergetic interactions, that enable and are enabled by human-like robots. The scope of this course includes, but is not limited to, perception, semantic content understanding and delivery, reasoning, planning, problem-solving, learning, human-robot interaction and domain-specific applications in the home, healthcare, business, entertainment, business, education, industry, etc. The recent success of AlphaGo and advances in artificial intelligence, cloud computing, mobile computing, cognitive computing, semantic computing and other related areas have demonstrated that the era of robots is emerging. The course will give an introduction to the relevant technologies so that students have the required concepts to study further. Practical demonstrations and applications will trigger students’ learning motivation.

3

Seminar (I)(II) (III)

 

This course will help graduate students to develop their independent research ability. Students will have to present their papers related to the major topics in either bioinformatics or biomedical research.

3

Systems Biology

 

Networks of interactions are fundamental to all biological processes; for example, the cell can be described as a complex network of chemicals connected by chemical reactions. Cellular processes are controlled by various types of biochemical networks: (i) metabolic networks; (ii) protein-protein interaction networks (PIN), and (iii) gene regulatory networks. Complex networks are found throughout biology. Many biological networks seem to have underlying modularity structure. Each module is expected to perform a specific function, separable from the functions of other modules.

This course will cover the following topics, including network biology, random graph theory, network perturbations, network motifs structures, network complexity, and network enrichment analysis. Students will learn how to analyze biological systems from a system level perspective.

3

 

Faculty Members

Instructor’s title

Instructor’s name

Contact Information

President/Professor

Dr. Jeffrey J.P. Tsai

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Chairman / Professor

Dr. Rouh-Mei Hu

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Professor

Dr. Phillip, C-Y. Sheu

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Professor

Dr. Ka-Lok Ng

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Associate Professor

Dr. Wen-Pin Hu

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Associate Professor

Dr. Yu-Ching Chen

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Assistant Professor

Dr. Wen-Ling Chan

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Assistant Professor

Dr. Wen-Yu Su

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Assistant Professor

Dr. Che-Nan Kuo

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Assistant Professor

Dr. Yu-Lin Song

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Assistant Professor

Dr. Yu-Fang Shen

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Assistant Professor

(Dept. of

Computer Science &

Information Engineering)

Dr. Wei-Fu Lu

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