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本頁翻譯進度

燈號說明

審定:無
翻譯:吳克銘(簡介並寄信)
編輯:劉秋枝(簡介並寄信)

計畫背景
Project Background

寫好的計畫依文章或專題計畫的格式,加上展示用的圖片(用Powerpoint® 或 Word軟體),全部必須在第十二節課當天交出。我們建議你在第七節課結束前,開始挑選題材與小組成員。
The full written project in the form of an article or grant proposal as well as the figures ready for (Microsoft® Powerpoint® or Microsoft® Word) presentation is due the day of lecture 12. We recommended that you start choosing a topic and team before the end of lecture 7.

每個計畫小組的口頭報告每個人限定六分鐘,含最後兩分鐘(每個人)的問題發問。除非有特殊請求,報告檔案將會依照下列清單順序安裝在電腦中。
The oral presentation will be limited to 6 minutes per person on each project team. This will give us 2 minutes (per person) for questions at the end. The presentations will be loaded on the computer in order of the schedule below, unless special requests are made.

每個小組必須至少有一個計算「結果」。這可以簡單到檢查已刊出文章中的表格,或是複雜到提出新的計算生物學演算法及相關圖形。
Each team must have at least one computational "result". This can be as simple as checking a table in a published article or as complex as a new computational-biology algorithm and associated graphics.

至少應該有一篇以上先前相關文章的關鍵性評價。
There should be critical assessment of at least one previous relevant article.

請註明並連結到pubmed或其他任何可用的參考網址。
Please cite and link pubmed or web references wherever possible.

每個成員在小組中所扮演的角色應該清楚地記載在寫好的版本裡。每個小組成員應該口頭報告一個實際的貢獻,而不僅僅只介紹最後的報告者。
The role that each member played in the team should be clearly stated in the written version. Each team member should present a substantial contribution orally, not merely introduce the final speaker(s).

全部的課程成績計算為:每個問題集佔12%,以及報告計畫佔28%。
The overall course grade will be 12% per problem set and 28% for the project.

遲交的對策是,第十二節課中午截止期限後,每遲一天扣(100%中的)5%。(假如你是第一組,你應該將你的投影片電郵給我們,並在第十二節課結束前,確認在我們手中運行正常。)
The late policy is 5% (of 100%) off per day after the deadline of lecture 12 at noon. (If you are in the first group, you should get your slides emailed to us and confirm functioning in our hands by the end of lecture 12.)



評分規則
Grading Rubric

這規則儘可能設計地愈清楚愈好,以確保所有學生能以一貫性的標準來評分。報告計畫的每一部分將會依照1到5分的尺度來評分。對每一部分明確的定義了這尺度,但粗略的區分如下:1 = 不良,2 = 需要改進, 3 = 良好,4 = 優良,5 =傑出。下載完整規則檔案(PDF)
This rubric is designed to be as explicit as possible to ensure that all students are graded consistently. Each component of the project will be graded on a scale from 1 to 5. The scale is explicitly defined for each component but is roughly as follows: 1 = poor, 2 = needs improvement, 3 = good, 4 = excellent, 5 = outstanding. Download complete rubric file. (PDF)


2002年計畫標題構想
2002 Project Topic Ideas

  1. 蛋白質與蛋白質間的交互作用:網絡結構。
    Protein-Protein Interactions: Network Structures.

  2. 找出微陣列資料與特定轉錄因子的促進子共識序列之間的相互關聯性。
    To correlate microarray data with the promoter site consensus sequence for a specific transcription factor.

  3. 人類寄生性病原體的基因體分析,特別是瘧原蟲與利什曼原蟲。
    Genomic analysis of parasitic human pathogens, particularly Plasmodium falciparum, and Leishmania major.

  4. 使用perl程式語言來模擬抗體基因的重組,以預測抗體變異區域的胺基酸序列。
    Simulation of the recombination of antibody genes by using perl to predict the amino acid sequences of the variable region of the antibody.

  5. Th2趨化激素受體與結合子之核酸與蛋白質序列的動態規劃分析。
    Dynamic Programming analysis of Th2 chemokine receptors and ligands nucleotide and protein sequences.

  6. 找出概括性的微變選擇條件以用來發現人類小片斷干擾RNA。
    The Determination of a General Set of Fine-Grained Selection Criteria for the Discovery of siRNA in Humans.

  7. 我們如何管理這些各種不同資訊來源所提供的矛盾、否定或「不確定」之功能預測的事例,以用來建立網絡模型?
    How to we manage cases in which conflicting, contradicting or "speculative" functional predictions are contributed by the various information sources used to build a network model.

  8. 發展一個工具(或程式)能根據鄰近基因背景來預測蛋白質的功能(非同源性的解決方法)。
    Develop an engine (or program) to predict protein function on context basis (non-homologous approach).

  9. 腫瘤壞死因子接受器的生物探勘。
    TNF Receptor Biomining.

  10. 根據邏輯迴歸方法來對微陣列歸類模型做各種不同選擇方法的比較。
    Comparing Variable Selection Methods for Microarray Classification Models Based on Logistic Regression.

  11. 使用偶合指數方法來定出開放讀碼框架。
    Using the Index of Coincidence to identify Open Reading Frames.

  12. 藉由清除基因調控區域上的短序列以進行轉錄控制。
    Transcriptional control mediated by cleansing of short sequences from gene regulatory regions.

  13. 為核甘酸突變提供視覺介面的軟體解決方案。
    Software solution that provides a visual interface to nucleotide mutations.

  14. 比較河魨與人類基因體序列來定出可能的轉錄調節要素。
    Identification of Potential Transcriptional Regulatory Elements by Comparison of Human and Pufferfish Genomic Sequences.

  15. 重疊比較從主要組成分析來的叢集結果,與從自我組織圖方法來的叢集結果。
    Overlaying Clustering Results from PCA with Clustering Results from Self-Organizing Maps.



Microsoft® 及PowerPoint® 是微軟公司在美國及/或其他國家的商標或註冊商標
Microsoft® and PowerPoint® are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries.




 
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