放射大數據體學資料庫分析與應用
Big Data Analysis and Application for Omics and Radiation
| 節 | 週二 |
|---|---|
5 13:20–14:10 | 放射大數據體學資料庫分析與應用 YB134（陽明） 2 節連堂 |
6 14:20–15:10 |
* 根據陽明交大上課時間表所列
大數據已經是時勢所趨且在部分研究中蘊含豐富資源供研究及使用, 本課程將簡介原理及基礎分析/運算大數據相關技術. 本課程包括基因體學/轉錄體學/蛋白質體學以及若干相關資料搜尋, 分析以及網站工具使用。旨在讓學生獲得獨立分析目標分子對於疾病與癌症進程所扮演的角色及功能，藉以提升自身研究主題的深度與廣度。 Big data has become a prevailing trend, offering a wealth of resources for research and application in various fields. This course introduces the underlying principles and fundamental technologies for big data analysis and computation. It covers genomics, transcriptomics, and proteomics, alongside relevant data retrieval, analysis methods, and the use of web-based tools. The course aims to enable students to independently analyze the roles and functions of target molecules in disease and cancer progression, thereby enhancing the depth and breadth of their own research.
對於omics 定義/具備所需文件以及分析能力尤佳, 不具備者也可修習本科目. 請準備平板/筆電對於因應課程的即時性分析與教學法. Although prior knowledge is not a prerequisite for taking this course, familiarity with 'omics' concepts and the necessary data analysis skills would be highly advantageous. To facilitate real-time analysis and interactive learning activities during class, please bring a tablet or laptop.
無備註
通過講解，讓學生學習大數據/資料庫的基礎知識，為後續的實驗設計提供數據並建立基礎，適合碩士/博士以及執行大專生計畫之學生應用於自身實驗，精益求精；並期待可激發學生對相關領域的興趣，希望能吸引更多學生未來進入相關領域一同探索新知。 本課程分為兩階段, 第一階段為理論與設計介紹, 第二階段為實質技術應用, 網站操作以及獨立選取題目作為期末考報告的演示主題. 本課程需要準備平板/筆電在課堂上即時操作並且呈現分析結果, 不具備程式語言/生物資訊背景者可以修習, 沒有限制. Through a series of lectures, students will acquire the foundational knowledge of big data and databases needed for future experimental design and data preparation. The course is ideal for Master's and PhD students looking to optimise their experimental workflows, as well as undergraduates participating in research projects. The course also aims to spark students' interest in the field and encourage them to pursue further exploration and discovery. The course comprises two phases: the first covers theory and design concepts, while the second focuses on practical technical applications and the use of web-based tools. Students will then give a final presentation on a topic of their choice. Students are required to bring a tablet or laptop to class for hands-on exercises and to present their analysis results. There are no prerequisites, and students with no prior background in programming or bioinformatics are welcome to enrol.
快問快答 20%, 期末口頭報告50%, 期末書面報告30%. Lightning round 20%, Final oral presentation 50%, Final paper work 30%
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction 2026-09-08(二) 時數：[2026-09-08]張御展(2.00) |
| 第 2 週 | Biochips/microarray/NGS 2026-09-15(二) 時數：[2026-09-15]張御展(2.00) |
| 第 3 週 | Collection and normalization of omics profiles 2026-09-22(二) 時數：[2026-09-22]張御展(2.00) |
| 第 4 週 | Prediction tools (Ingenuity Pathway Analysis, GSEA, DAVID) 2026-09-29(二) 時數：[2026-09-29]張御展(2.00) |
| 第 5 週 | Survival curves-1 2026-10-06(二) 時數：[2026-10-06]張御展(2.00) |
| 第 6 週 | Survival curves-2 2026-10-13(二) 時數：[2026-10-13]張御展(2.00) |
| 第 7 週 | Drug screening platform and combination interpretation 2026-10-20(二) 時數：[2026-10-20]張御展(2.00) |
| 第 8 週 | RNA-seq 2026-10-27(二) 時數：[2026-10-27]張御展(2.00) |
| 第 9 週 | Various bioinformatics tools 2026-11-03(二) 時數：[2026-11-03]張御展(2.00) |
| 第 10 週 | Cancer cell line encyclopedia (CCLE) 2026-11-10(二) 時數：[2026-11-10]張御展(2.00) |
| 第 11 週 | Drug resistance 2026-11-17(二) 時數：[2026-11-17]張御展(2.00) |
| 第 12 週 | Example sharing 2026-11-24(二) 時數：[2026-11-24]張御展(2.00) |
| 第 13 週 | Expert Lectures and Sharing 2026-12-01(二) 時數：[2026-12-01]張御展(2.00) |
| 第 14 週 | Lightning round 2026-12-08(二) 時數：[2026-12-08]張御展(2.00) |
| 第 15 週 | Final Oral presentation_1 2026-12-15(二) 時數：[2026-12-15]張御展(2.00) |
| 第 16 週 | Final Oral presentation-2 2026-12-22(二) 時數：[2026-12-22]張御展(2.00) |
1. Big Data in Omics and Imaging: Association Analysis (Chapman & Hall/CRC Computational Biology Series) 2. Integrating Omics Data, Cambridge University Press, 2015
- 地點
- B308
- 時間
- Fri (五) 78
- 聯絡方式
- yuchanchang@nycu.edu.tw