亞洲大學生物資訊與醫學工程學系

Structural Biology

結構生物學

This course is designed to give students a general understanding of the structural biology. Structural biology is a branch of molecular biology, biochemistry, and biophysics concerned with the molecular structure of biological macromolecules, specially proteins and nucleic acids, how they acquire the structures they have, and how alterations in their structures affect their function. This subject is of great interest to biologists because macromolecules carry out most of the functions of cells, and because it is only by coiling into specific three-dimensional shapes that they are able to perform these functions. This architecture, the "tertiary structure" of molecules, depends in a complicated way on the molecules' basic composition, or "primary structures."

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Data Mining

資料探勘

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.

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Machine Learning

機器學習

This course is designed to give students an introduction to the methodologies, technologies, mathematics, and algorithms currently needed by people who do researches in learning with data. At the end of this course, students will possess the essential abilities to read academic papers and do their researches. Basic knowledge in calculus, linear algebra, statistics, and probability is helpful in this course.

3

Image Processing

影像處理

Digital image processing involves the acquisition, enhancement, display, and understanding of digital images. This course is designed to give students a general understanding of the fundamentals of digital image processing and to provide them with hands-on experience in designing and programming image processing algorithms.

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Pattern Recognition

圖形識別

Pattern recognition aims to classify data (patterns) based either on a priori knowledge or on statistical information extracted from the patterns. This course is designed to teach students the basic techniques of pattern recognition and to show the students how to use such techniques for classifying real dataset, the assignment of a physical object or event to one of several prescribed categories.

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Artificial Intelligence

人工智慧

This course covers general knowledge representation techniques and problem solving strategies. Topics will include search, intelligent agents, game playing, rule-based systems, logic programming, semantic networks, planning, and uncertain reasoning. The aim of this course is to introduce the current range of AI-informed techniques for solving problems in computer science and biomedical research.

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Molecular Biology

分子生物學

The aim of this course is to provide students with a basic understanding of modern molecular biology. Topics in replication, dynamics of chromosome structure, regulation of gene expression will be discussed. Students will be able to use the knowledge they have acquired to develop projects in bioinformatics at the end of the course.

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Bioinformatics

生物資訊學

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

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Biomedical Signals and

Detection

生醫訊號與感測

This course covers the following concepts: medical data acquisition, conversion, analysis and processing, elementary digital signal processing theory, and various applications in biomedical area.

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Bioinformatics Algorithms

生物資訊演算法

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.

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Microarray Data

Analysis

微陣列資料分析

The course offers an in-depth understanding of microarray technology, perform statistical analysis (t-test, chi-square test, F-test, test of hypothesis) of data, helping students get familiar with the internet resources.

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Genomics

基因體學

This course studies the genomes of organisms. The field includes intensive efforts to determine the entire DNA sequence of organisms and fine-scale genetic mapping efforts. The field also includes studies of intragenomic phenomena such as heterosis, epistasis, pleiotropy and other interactions between loci and alleles within the genome. Research of single genes does not fall into the definition of genomics unless the aim of this genetic, pathway, and functional information analysis is to elucidate its effect on, place in, and response to the entire genome's networks.

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Proteomics

蛋白質體學

This course is designed to give students a general understanding of the proteomics. Proteomics is the large-scale study of proteins, particularly their structures and functions. Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. The proteome is the entire complement of proteins, including the modifications made to a particular set of proteins, produced by an organism or system. This will vary with time and distinct requirements, or stresses, that a cell or organism undergoes.

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Seminar (I)(II) (III)

論文研討(一)(二) (三)

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.

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Systems Biology

系統生物學

This course is designed to give students a general understanding of the systems biology. Systems biology is a term used to describe a number of trends in bioscience research, and a movement which draws on those trends. Proponents describe systems biology as a biology-based inter-disciplinary study field that focuses on complex interactions in biological systems, claiming that it uses a new perspective (holism instead of reduction). Particularly from year 2000 onwards, the term is used widely in the biosciences, and in a variety of contexts. An often stated ambition of systems biology is the modeling and discovery of emergent properties, properties of a system whose theoretical description is only possible using techniques which fall under the remit of systems biology

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Time Series Analysis

時間序列分析

This course is designed to introduce a variety of statistical models for time series and cover the main methods for analysing these models. The following topics will be cover; probability models for time series, stationarity, moving average (MA), autoregressive (AR), and ARIMA models. 

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