校際選修

115-1 選課時程

進行中

  • 初選第一階段 6/15/2026
  • 初選第二階段 6/22/2026
  • 校際選修 8/24/2026
  • 初選第三階段 8/31/2026
  • 開學後加退選 9/7/2026
  • 逾期加退選 9/21/2026
選課資源

進階生物資訊統計學

Advanced Statistical Methods in Bioinformatics

學期
114-2
學分
3 學分
當期課號
143201
永久課號
MDBI30072
開課單位
生物資訊學程
授課教師
國際研究生學程
類別
必修
上課時間表
週四
2
09:00–09:50
進階生物資訊統計學
3 節連堂
3
10:10–11:00
4
11:10–12:00

* 根據陽明交大上課時間表所列

概述

Outline: Introduction to useful and advanced statistical methods in computational biology. The topics include: Analysis of next generation sequencing (NGS) Data (e.g., RNA-Seq and ChIP-Seq), maximum likelihood estimation, the EM algorithm, Bayesian inference, Monte Carlo methods, Resampling (Bootstrap & permutation test), Human Genetics, clustering and classification, dimension-reduction and missing data.

先修科目

The course details may change (time, location, topic, etc.), please refer to the class syllabus on the TIGP Bioinformatics Program before each class: https://idv.sinica.edu.tw/tigpbio/index.html 【For Non-BP student】 For Non-BP student to register/sit-in any BP course, it is required to gain course chair's permission: (1) Basic Enrollment Information form (https://forms.gle/oK7vJzzrx9EvybbT9) (2) TIGP-BP Course Registration Consent Form (https://idv.sinica.edu.tw/tigpbio/index/TIGP%20Bioinformatics_Class%20Registration%20Consent%20Form.docx) ※ Deadline: the 4th week of each semester. ※ Signature of the course chair should be collected before submission. Incomplete form will not be accepted. ※Course grade will NOT be given (even class enrollment is completed at school) if fail to follow the above procedures.

教學方式

TA: N/A (Please refer to the lectures respectively shall you have any questions for each class).

評分方式

Grades: Midterm exam 50%. Final exam 50%.

週次計畫
週次主題
第 1 週*May be rescheduled, please refer to the BP website (https://idv.sinica.edu.tw/tigpbio/index.html) for the latest info.* Analysis of NGS data I
第 2 週*May be rescheduled, please refer to the BP website (https://idv.sinica.edu.tw/tigpbio/index.html) for the latest info.* Analysis of NGS data II
第 3 週Maximum likelihood estimates and the EM algorithm I
第 4 週Maximum likelihood estimates and the EM algorithm II
第 5 週Bayesian Statistics
第 6 週Resampling methods
第 7 週Monte Carlo Markov Chains
第 8 週Midterm Exam
第 9 週Prediction of Drug Response
第 10 週Cluster Analysis
第 11 週Classification and Its Assessment
第 12 週Statistics in Human Genetics/Genomics I
第 13 週Statistics in Human Genetics/Genomics II
第 14 週Advanced regression and dimension reduction I
第 15 週Advanced regression and dimension reduction II
第 16 週Final Exam
教科書

N/A