資料採礦小組

【回到實驗室首頁】

資料採礦相關網站 :

生物資訊相關網站 :

教育部暑期生物資訊學課程 :

2003台大暑期『生物科技教育』---生物資訊課程

生物資訊書籍介紹 :

生物資訊電腦技術 (OReilly出版，中文翻譯，可試讀第一章)
生物資訊入門
請注意 :
中文譯作由藝軒圖書出版，譯者為陳進和等。原文書名為"Attwood/Parry-Smith: Introduction to Bioinformatics"。本書目前在Amazon網站上的評價為四顆星，是一本介紹生物資訊入門的好書。

生物資訊研討會 :

Data Mining and Text Mining for Bioinformatics Workshop at the ECML / PKDD 2003 in Dubrovnik-Cavtat, Croatia; 22. September, 2003.
請注意 :
若要下載研討會整本論文集，請在點進去之後，在Proceedings 這個項目下，看到 Get the workshop proceedings as one PDF file 這行字，直接在上面按滑鼠右鍵並選擇另存目標，就可以把本研討會所製作完成的整本論文集 PDF 檔案下載到自己電腦上了。

生物資訊文獻探討 :

韓嘉威教授發表在 Information Systems Vol: 28, Issue: 4, June, 2003 (pp. 243-268)的論文，
題目名稱為"Cancer Classification using gene expression data"(利用基因序列來解決癌症分類問題)。
請注意：上面的連結需有圖書館網頁密碼方可順利開啟。

其摘要如下：
        The classification of different tumor types is of great importance
        in cancer diagnosis and drug discovery. However, most previous cancer
        classification studies are clinical based and have limited diagnostic
        ability. Cancer classification using gene expression data is known to
        contain the keys for addressing the fundamental problems relating to 
        cancer diagnosis and drug discovery. The recent advent of DNA 
        microarray technique has made simultaneous monitoring of thousands of
        gene expressions possible. With this abundance of gene expression 
        data, researchers have started to explore the possibilities of cancer
        classification using gene expression data. Quite a number of methods 
        have been proposed in recent years with promising results. But there 
        are still a lot of issues which need to be addressed and understood.

        In order to gain a deep insight into the cancer classification 
        problem, it is necessary to take a closer look at the problem, the 
        proposed solutions and the related issues all together. In this 
        survey paper, we present a comprehensive overview of various proposed 
        cancer classification methods and evaluate them based on their 
        computation time, classification accuracy and ability to reveal 
        biologically meaningful gene information. We also introduce and 
        evaluate various proposed gene selection methods which we believe 
        should be an integral preprocessing step for cancer classification. 
        In order to obtain a full picture of cancer classification, we also 
        discuss several issues related to cancer classification, including 
        the biological significance vs. statistical significance of a cancer 
        classifier, the asymmetrical classification errors for cancer 
        classifiers, and the gene contamination problem.

Google Answer網站上面有人發問的一些重要問題 :

Google Answer 網站上面的一個人問的關於 Categorization 的問題，他想知道在少部份文章被分類時，如何知道該用哪種方法分類，且為何要使用這個方法來分類。他出價100美元請 Google Answer 回答，而也獲得了詳盡的解答，有興趣的人請點選這裡過去看看。

機器學習文獻探討 :

美國麻省理工學院出版的期刊Journal of Machine Learning Research(JMLR)，在2003年介紹了許多關於 Text Mining 的論文。
網上論壇 comp.ai: JMLR: Special Issue on Learning Text and Images.