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審定:無
翻譯:鄭宇(簡介並寄信)
編輯:朱學(簡介並寄信)

最後四節課期間是學生們展示那些課內完成的、有意義的研究專題結果的機會。我們鼓勵你們組隊(一個有兩至三個學生的團隊)去開展一個專案的工作,還推薦你們組成有不同興趣,技巧和方法的團隊。下面是一些最終專題的範例,其中,有的是以前的最終專題。
The last four class sessions will be an opportunity for students to present the results of significant research projects done for the class. We encourage you to team up (in teams of two or three students) to work on projects, and teams that span students with different interests, skills and approaches are encouraged. Below is a set of example projects for the final project. Some of these were prior final projects.



  1. 計算在pubmed上的文檔上(查看http://www.ncbi.nlm.nih.gov)那些最接近病人問題列表的情況。你可以使用來自臨床工作站資料庫上的問題列表(一個課內可用的臨床資料庫)

    Calculate which documents at Pubmed (see http://www.ncbi.nlm.nih.gov) are the closest to any patient's context based on a problem list. You can use the problem list from the Clinician's Workstation database (an available clinical database for the class).

  2. 用基因實體命名法來匯總微陣列實驗中的基因結果,實驗是根據相近基因文獻中指證的方式。

    Use the Gene Ontology nomenclature to cluster gene results in a microarray experiment based on how close two genes are in the literature citing them.

  3. 將CWS(或者其他的臨床資料庫)相關資料庫轉化成一個XML流,並且重建一個較好的規範化資料模型上的數據。與含更多不同表格的模型相比,在”較好”的資料模型是一個普通模型的時候,要比較你的影響和結果。

    Translate the CWS (or other clinical database) relational database into an XML stream and re-constitute the data in a better normalized data model. Compare your effort and results when the "better" data model is a generic model vs. one containing many distinct tables.

  4. 從功能角色方面進入CWS(或者其他的臨床資料庫)。進行密碼驗證。如果你是一個醫學上的高手,請在你的系統中闡述你的方法的弱點和你是如何揭開秘密的。

    Implement role-based access to the CWS (or other clinical database). Implement cryptographic authentication. Describe the weaknesses of your approach and how you would compromise privacy in your system if you were a medical knave.

  5. 病人將怎樣理解他們剛剛被診斷的多態性/突變的意思呢?建立一個web上使用的、一個地點鏈結的ID的腳本,可得到所有的基因多態、和那些基因相關的OMIM引文。隨後自動搜尋整個web和pubmed,找出適合用戶的(如普通的英語)資訊解釋,這些資訊可幫助病人理解每一個獨特基因的多態性/突變的意思。

    How will patients understand the meaning of a polymorphism/mutation that they were just "diagnosed" with? Create a web-enabled script that given a locus link ID returns all the gene polymorphisms, OMIM citations associated with that gene. Then automatically scour the entire web and pubmed to find consumer-oriented (i.e. plain English) explanations of information that would help a patient understand the meaning of a polymorphism/mutation of a particular gene.

  6. 給出一個微陣列的數據組,標示出測定中的錯誤的所有來源,並在特定資料組中找出它們。這些錯誤將如何影響論文的結果?你能評估錯誤穿過微陣列表面的一個概率分佈麼?找出分佈中異常的資料組的微陣列。用二維表圖結構/誤差表闡明這些分析。

    Given a microarray data set, articulate all the sources of error in measurement and find them in the particular data set. How will these errors affect the conclusions of the paper based on these results? Can you estimate a probability distribution of errors across the microarray surface? Find microarrays in a data set which are outliers in this distribution. Illustrate these analyses with two-dimensional surface plots/error surfaces.

  7. 計算用於微陣列的基因表達譜、和基因分型研究技術平臺使用的、低聚核˙覺敦w裝置的溶解溫度分佈。確定在一個或多個資料組的基因表達變化和探針裝置內部溶解溫度變化之間的關係。你能夠識別一個減少探針裝置變化的探針亞裝置嗎?使用那些僅減少變化的探針裝置會怎樣影響那個資料組被最初使用過的分類任務?

    Calculate the distibution of the melting temperature of the oligonucleotide probe sets that Affymetrix uses for its microarrays. Determine the relationship between the measured variability of gene expression in one or more data sets and the variation of the melting temperature within a probe set. Can you identify a subset of probes which reduce variability for a probe set? How does using ONLY those reduced variability probe sets affect the classification task for which the data set was originally used?

  8. 開發一套工具,採用識別患者姓名、昵稱、不同的身份號碼、地址、電話號碼等方法,以查找和替換資料的方法來識別機密的醫療記錄。

    Develop a set of tools to de-identify sensitive medical records by finding and replacing data that appear to identify the patient, such as names, nicknames, various identification numbers, addresses, phone numbers, etc.

  9. 用簡單的自然語言技術在雜亂的文本中摘錄出醫學紀錄埵魚鴘熙Q編碼的內容。例如,找出所有提到的藥物、劑量、給藥途徑,等。依靠文本分析,可以試用其他範例來確定是否這個病人的描述可能和某些公共健康關注的特殊疾病相符(從腦膜炎到炭疽熱)

    Use simple natural language techniques to extract interesting coded aspects of the medical record from unstructured text. For example, find all mention of medications, dosages, routes of administration, etc. Another example would be to try to determine by textual analysis whether the description of a patient might be consistent with some specific disease of public health interest (ranging from meningitis to anthrax).


這個列表是僅僅是一個提示。歡迎任何較為複雜的、合理的專題。我們要求你在第十七課之前提交專題,提議的專題要有足夠的細節來描述,這樣,我們才能評論它。同樣,需要確定在專案中工作的組員(我們建議二~三人)。

This list is meant only to be suggestive. Any reasonable project of roughly the above level of sophistication will be welcome. We ask you to submit a proposal by Lecture 17 that describes the proposed project in enough detail that we can critique it. It should also identify the group of people (we suggest 2-3) who will work together on the project.




 
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