The Taiwan International Graduate Program has been established to attract highly qualified young researchers both from home and abroad in order to help jump-start the development of several frontier areas that are crucial to the future development in science and technology. Specific graduate programs have been developed to enhance the innovative potential and academic standards of research on these and related fields. Within this context, the graduate program on “Bioinformatics” is designed to offer specific training and research opportunities to Ph.D. students interested in working on this particular area.
The TIGP Program on Bioinformatics (BP) is a joint-degree program sponsored by Academia Sinica (Institute of Information Science, Institute of Statistical Science, and Institute of Biomedical Sciences), National Tsing Hua University, National Chiao Tung University and National Yang Ming University. Additional teaching support will be available from other major research universities in Taiwan. Unlike most Bioinformatics programs offered in other universities that adopt existing courses in various departments, our courses are specifically designed for BP students and taught by active and experienced researchers in bioinformatics. The program provides interdisciplinary training and research opportunities that seamlessly integrate the related areas so that students can be well-prepared for independent research in this new, fascinating areas of bioinformatics. We will focus on genetics and proteomics study and emphasize data transfer, data analysis, biological information and biological feature extraction, knowledge management using advanced computation methodologies and computer science technology.
Our research areas include but not limited to the following:
- Computational biology: This area focuses on the design of various algorithms for sequence analysis, gene prediction, disease gene mapping, motif finding, and gene networks.
- Biological knowledge management: This area focuses on the integration of various heterogeneous databases, biological knowledge representation, automation of pipeline experiments, and the construction of various annotation databases. In addition, biological literature search is also a crucial component.
- Bioinformatics applications: This area focuses on using existing tools to analyze biological sequences, microarray data, proteomic data, etc. Statistical analysis and data mining techniques will be used to reach the goal of "information-driven biomedical research."
- Computational structural biology: This area focuses on protein structure prediction and classification, automated biomolecule docking, and molecular dynamics.
Core courses I (Required)
B1. Basic molecular biology for bioinformatics (3 credit units)
● Molecules, Cells, and Evolution
● Chemical Foundations
● Protein Structure and Function
● Basic Molecular Genetic Mechanisms
● Molecular Genetic Techniques
● Genes, Genomics, and Chromosomes
● Transcriptional Control of Gene Expression
● Post-Transcriptional Gene Control
● Cellular Energetics
● Signal Transduction and G Protein–Coupled Receptors
● Signaling Pathways That Control Gene Expression
● The Eukaryotic Cell Cycle
● Cancer
C1. Biological computing I (3 credit units)
This course is aimed at students without prior knowledge of computer science who have desire to apply computational approaches to biological problem solving.
The goal is to provide students with a brief introduction to several topics related to basic computer science and biological computing. Students are expected to make productive use of computational techniques after they take this course. We cover the following topics in this course:
● Data Structure
● Algorithmic techniques
● Analysis of algorithms
● Computational algorithms
● Bioinformatics algorithms
S1. Fundamental Statistical Methods in Bioinformatics (3 credit units) [Previously C2]
● Descriptive statistics
● Probability
● Discrete distribution
● Continuous distribution
● Parameter estimation, confidence interval
● Hypothesis testing
● Comparative study
● Analysis of categorical data
● Correlation, regression, and analysis of variance
● Non-parametric analysis
● Clustering
● Classification
● Survival data analysis
P1. Programming Language - Python (2 credit units)
● Basic Elements of Python
● Basic statements I: branching programs and inputs
● Modules, Files, and Structured Types
● Exception handling
● Introduction to Biopython
● Object-oriented programming: classes
● Comparative study
● Data analysis toolbox
● Web programming
Core Courses II (Mandatory)
B2. Big Data in Bioinformatics - From Data-Driven Analysis to Knowledge (3 credit units)
● Genome sequence acquisition & analysis
● The human genome project
● Genomic variations
● Genomics Databases & Bioinformatics Applications (I)
● Genomics Databases & Bioinformatics Applications (II)
● Introduction to statistical genetics
● Introduction to evolutionary genomics
● DNA Microarrays: principles and applications (I)
● DNA Microarrays: principles and applications (II)
● Transcriptome - related bioinformatics databases & applications
● Protein informatics
● Structural proteomics & drug design
● Protein-protein interaction network and databases
● Databases of biochemical pathways
C2. Advanced Algorithms in Computational Biology (3 credit units)
In this course we cover the following but not limit to:
● Sequence analysis algorithms
● Machine Learning
● High-throughput Data Analysis
S2. Fundamental Statistical Methods in Bioinformatics (3 credit units)
● Advanced analysis of omics data
● Advanced analysis of sequencing data
● Maximum likelihood estimates and EM algorithm
● Bayesian methods with Monte Carlo Markov Chains
● Advanced regression and dimension reduction
● Resampling procedures and permutation tests
● Advanced clustering, classification and data visualization
● Biomedical image analysis
● Statistics in human genetics
● Biosystem network analysis
Elective Course
Dynamics in Systems
The vast advancement in technology and accumulation of information nowadays has enabled us to study biology with great details in time and space resolution, and in the molecular level. Understanding of biology at the systems level has become possible in many cases.
The dynamical aspect of these studies often include a mathematical model to describe and to predict the behavior of the system. The construction, evolution and prediction of these biological models are closely related to a branch in mathematics – nonlinear dynamics. In this course we cover the following topics:
● Central dogma in molecular biology
● Michaelis-Menten kinetics
● Appendices A and B (Alon)
● Bifurcation analysis
● Two-dimensional flows
● Oscillations in Biology
● Noises in biology-introduction
Requirements
- Master's or Bachelor's degree in biology, computer science, statistics or other related areas. (Must have the ORIGINAL copy of diplomat by registration)
- Fluency in English:
● TOEFL score of 550pbt/213cbt/79ibt.
● This can be waived for those who have obtained bachelor or master degrees from English
speaking countries. - GRE score from the general exam. However, an applicant may submit one of the following material in place of a GRE general test score:
● Any evidence of research ability such as papers published in international conferences or
journals.
● Satisfactory performance in any course or project work related to the design of algorithms
or probability such as discrete mathematics, algorithms, computational complexity, data
structure, probability, computer architecture, compiler, and computer programming. - (Required) Basic programming skills
- A Statement of Purpose that includes a research plan
- Official transcripts from academic institutions attended after senior high school
- Three letters of recommendation
Scholarships
- Once admitted, each TIGP student will receive a monthly stipend of NT$34,000 (around USD1133) for the first year.
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