表面增強雷射脫附遊離/飛行時間質譜(SELDI-TOF-MS)是種屬於高維度的蛋白質質譜儀資料，主要是用來偵測蛋白質分子的表現。由於SELDI技術的限制，導致掃描出來的質譜儀資料往往存在誤差與雜訊，因此在分析前通常會先針對原始資料進行低階的事前處理，步驟包括去除基線、正規化、峰偵測(peak detection)與峰調準(peak alignment)。本文中所探討前列腺癌資料，可分成正常、良性腫瘤、癌症初期與癌症末期四種類別。我們分析及比較兩筆事前處理的蛋白質質譜資料，包括我們自行處理的以及Adam等人所處理的資料。為了解決SELDI在偵測分子質量時常出現的位移誤差以及同位素的問題，我們提出以”質荷比段落”當作新的特徵變數的想法來進行分析。本文利用「ROC曲線下面積」(AUC)當作選取的準則來挑選出重要的質荷比段落，而分類方法則採用支援向量機(SVM)。在四分類的分類結果中，我們自行處理的事前處理資可以得到訓練資料89%及測試資料63 %的正確率。而Adam等人所處理的事前處理資料，則得到訓練資料94%及測試資料86 %的正確率。本研究結果指出不同事前處理的方法對分類結果確實有影響，同時也驗證了利用”特徵變數段落”的方法來進行分析的可行性。 / The surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a technique for presenting the expression of molecular masses. It is obvious that every spectrum has a huge dimension of features. In order to analyze these types of spectra samples, preprocessing steps are necessary. The steps of preprocessing include baseline subtraction, normalization, peak detection, and alignment. In our study, we use a prostate cancer data for demonstration. This prostate cancer data can be classified into four categories, namely, healthy men, benign prostate hyperplasia, early stage prostate cancer, and late stage prostate cancer. We analyzed both the preprocessed data processed by ourselves and the preprocessed data done by Adam et al.. In this thesis, we use segmentations of features as “new features” in attempt to solve problems due to location shifts and isotopes. The selection of important segmentations was based on the values of AUC and the SVM was applied for classification. For four-class classification, 94 % and 86 % of accuracy were obtained for training samples and validation samples, respectively, by using Dr. Adam et al.’s preprocessed data, and 89% for training samples, and 63% for validation samples by using our preprocessed data. This study suggested that the preprocessed method does have effect on classification result and a reasonable classification result can be obtained by using segmentations of features.

藉由「早期發現，早期治療」的方式，我們可以降低癌症的死亡率。因此找出與癌症病變有關的生物標記以期及早發現與治療是一項重要的工作。本研究分析了包含正常人以及攝護腺癌症病人實際的蛋白質質譜資料，而這些蛋白質質譜資料是來自於表面強化雷射解吸電離飛行質譜技術（SELDI-TOF MS）的蛋白質晶片實驗。表面增強雷射脫附遊離飛行時間質譜技術可有效地留存生物樣本的蛋白質特徵。如果沒有經過適當的事前處理步驟以消除實驗雜訊，ㄧ 個質譜中可能包含多於數百或數千的特徵變數。為了加速對於可能的蛋白質生物標記的搜尋，我們只考慮可以區分癌症病人與正常人的特徵變數。 基因演算法是一種類似生物基因演化的總體最佳化搜尋機制，它可以有效地在高維度空間中去尋找可能的最佳解。本研究中，我們利用仿基因演算法(GAL)進行蛋白質的特徵選取以區分癌症病人與正常人。另外，我們提出兩種兩階段仿基因演算法(TSGAL)，以嘗試改善仿基因演算法的缺點。 / Early detection and diagnosis can effectively reduce the mortality of cancer. The discovery of biomarkers for the early detection and diagnosis of cancer is thus an important task. In this study, a real proteomic spectra data set of prostate cancer patients and normal patients was analyzed. The data were collected from a Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (SELDI-TOF MS) experiment. The SELDI-TOF MS technology captures protein features in a biological sample. Without suitable pre-processing steps to remove experimental noise, a mass spectrum could consists of more than hundreds or thousands of peaks. To narrow down the search for possible protein biomarkers, only those features that can distinguish between cancer and normal patients are selected. Genetic Algorithm (GA) is a global optimization procedure that uses an analogy of the genetic evolution of biological organisms. It’s shown that GA is effective in searching complex high-dimensional space. In this study, we consider GA-Like algorithm (GAL) for feature selection on proteomic spectra data in classifying prostate cancer patients from normal patients. In addition, we propose two types of Two-Stage GAL algorithm (TSGAL) to improve the GAL.

科学研究費補助金 研究種目:一般研究(C) 課題番号:62550093 研究代表者:森 敏彦 研究期間:1987-1988年度

新創企業在創業之初，往往資源不足、訂單未到、客戶關係未建立好、技術及產品未成熟、管理尚未到位，市場不確定等不利因素。所以不管從台灣或大陸的環境來看新創企業，創業之初的三到五年是新創企業最為艱困且風險最高的時段。 因此本研究主要在探討，材料表面處理技術的創業規劃及經營，新創企業如何在創業醞釀階段，就做好創業活動規劃；在草創存活階段，做好存活策略推動，使企業得以存活；在經營維持階段，如何推進經營利潤，並讓企業持續發展下去。使新創企業處於不同階段下，能有效分析、規劃及做好該階段的行動任務，而讓新創企業渡過各個階段的考驗，為本論文的研究目的。 研究結果顯示，在創業醞釀階段，要有創業活動的形成，需要在創業活動前做好分析及規劃。除要有創業的「環境價值機會」出現外，要有能提供顧客價值之「價格」、「品質」、「速度」、「需求滿足」誘因，及具備「技術、產品或服務」、「人或團隊」、「資金」、「客戶關係」等創業關鍵組成。 在草創存活階段，企業需推動存活策略三構面，使企業能順利存活下來，其中與其構面變項有利的連接方向，證明朝向：產品/服務經營模式「訂定得當」、控制資源/開銷「消耗少」、創造利益體/網絡「連結多」，將最有利於新創企業存活的方向。此外，新創企業在草創階段，如所從事的產品或服務之營銷活動，所在的區域、客戶、及市場，在創業之初有很高的不確定性，選擇性或階段性的「集中」（Focus）策略才是正確的戰略。但如從存活策略三構面上做分析，所得到的結果皆為不利的情況，而資源又已先行投入情況下，如擬存活下來，對該項目最好的經營策略就是採取「退縮」（Withdrawal）策略。 而在新創企業在剛經歷草創存活階段後，為能持續性存活下來，需有「經營利潤推進」產生，才得以能維持經營。本論文從經營利潤推進的角度，提出經營維持的四構面做法，包括在創業經營風險構面及成本構面的降低活動；價值構面及收入構面的提升活動。做為在經營維持階段，主要的經營指標。因此，具體落實經營利潤推進的四構面，企業才得以有「利潤」的產生，也唯有利潤的產生，新創企業才得以有機會永續。 關鍵字：新創企業、創業規劃、存活策略、利潤推進、表面處理 創業管理 / A start-up at the beginning often encounters the following challengers: resources are insufficient; the order has not been big enough; customer's relation has not been set up well; the technology and the product is immature; the management has not been put in place yet; the market is uncertain, and so on. No matter the new company is operating in Taiwan or the mainland china, it will face high risk, particularly in the period of 3rd to 5th year after its establishment. Therefore, this research aims to study the entrepreneurial plans and managements for the material surface treatment technology, including how to make a business activity plan well in the entrepreneurial fermentation stage? how to do a better survival strategy promotion in the starting survival stage? and how to become profitable in the management arrangement stage. The purpose to let the new enterprise pass each stage sufferings by effectively analyzing, planning and completing action tasks under the different periods. The findings showed that the company needs to complete the analysis and the plan prior to the activity of entrepreneurship in the entrepreneurial fermentation stage. Besides providing with the opportunities for value added, the start-up needs to provide with “the customer value”, such as “price”, “quality”, “speed”, and “the satisfied demand” to provide, and to possess “the entrepreneurial key element” including “the technology, the product or the service”, “the human or the team”, “the fund”, and “the customer relations”. In the starting survival stage, the enterprise could implement the three key actions of survival strategy to survive smoothly. It is proven that a better survival orientation for a newly created enterprise is to have an appropriate position in product/service or business model, to utilize fewer resources/expenses, and to link with a wider network. In addition, the right strategy for a newly created enterprise in the starting survival stage is the selective or gradual focus strategy, if the marketing activities of product/service have the very high uncertainty at the beginning of the undertaking in the region or the market. The analysis from three key elements of survival strategy obtains result all for disadvantageous situation, but resources already in advance investment situation. The best management strategy is adapted to the strategy of withdrawal. A newly creation enterprise has gone through the starting survival stage, the advancement of operating profit should be produced to last for long enduring survival. The present paper proposed four key elements of management maintenance as a main management target from the advancement's angle of operating profit, which including to reduces the operational risk and cost elements and to increase the operational value and income elements. Therefore, the new enterprise could carry out the four key elements of operating profit advancement to get “the profit”, which would let enterprise have the opportunity to last forever. Keywords：Newly Created Enterprise, Entrepreneurial Plan, Survival Strategy, Profit Advancement, Surface Treatment, Entrepreneurial Management,

傳統的腫瘤指標篩檢方法，往往靈敏度、普及度及特異性有限，無法得到正確、即時的診斷結果。現今癌症的研究，則透過蛋白質體學經由光譜及影像觀察癌症不同時期的蛋白質表現變化，期望未來得以發展較佳之診斷工具。本研究中主要針對兩組攝護腺癌症病人之蛋白質質譜資料，此資料應用蛋白質晶片與表面強化雷射解吸電離飛行質譜技術（SELDI-TOF-MS）收集而來。我們的研究目的在於從大量的蛋白質特徵中篩選出一群有助於分類的蛋白質特徵變數。我們提出以最小分錯率特徵選取法與最小p值（ 檢定、Kruskal-Wallis檢定）特徵選取法進行初步特徵辨識度排序以及選取，並進一步發展出k-mean萃取法、最大相關係數萃取法與判定係數萃取法以改善變數間嚴重的共線性問題。我們利用支援向量機（Support Vector Machine）方法進行分類並評估分類效果，在不同的分類目的下萃取有助於辨識的蛋白質特徵，以決定最佳特徵集合。研究發現運用最小分錯率特徵選取法與最小p值分錯率特徵選取法，輔以判定係數萃取法，在各分類目的下皆有良好表現，為較佳的特徵選取方式。

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第15343号 / 工博第3222号 / 新制||工||1485(附属図書館) / 27821 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 宮川 豊章, 教授 朝倉 俊弘, 教授 河野 広隆 / 学位規則第4条第1項該当

Kyoto University (京都大学) / 0048 / 新制・論文博士 / 博士(工学) / 乙第12460号 / 論工博第4042号 / 新制||工||1497(附属図書館) / 28070 / (主査)教授 岩田 博夫, 教授 伊藤 紳三郎, 教授 辻井 敬亘 / 学位規則第4条第2項該当

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第22261号 / 理博第4575号 / 新制||理||1657(附属図書館) / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)准教授 吉川 裕, 教授 秋友 和典, 准教授 石岡 圭一 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

京都大学 / 0048 / 新制・論文博士 / 博士(工学) / 乙第12857号 / 論工博第4104号 / 新制||工||1608(附属図書館) / 31537 / (主査)教授 宮川 豊章, 教授 河野 広隆, 准教授 山本 貴士 / 学位規則第4条第2項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18813号 / 理博第4071号 / 新制||理||1585(附属図書館) / 31764 / 京都大学大学院理学研究科化学専攻 / (主査)准教授 奥山 弘, 教授 松本 吉泰, 教授 倉田 博基 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM