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研究生:鄭鼎翰
研究生(外文):Tin-Han Cheng
論文名稱:基質輔助雷射脫附飛行時間式串聯質譜儀 微生物抗藥性資料視覺化工具
論文名稱(外文):A microorganism resistance data visualized tool using Matrix-Assisted Laser Desorption Ionization-Time of Flight
指導教授:洪炯宗
指導教授(外文):Jorng-Tzong Horng
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系在職專班
學門:工程學門
學類:電資工程學類
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:38
中文關鍵詞:值譜儀資料視覺化
外文關鍵詞:MALDITOFdata visualize
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近年來,隨著質譜儀在各醫療機構的普遍應用,在大型的醫療機構都已經累積了數量可觀的資料,但是目前能使用質譜儀資料的軟體皆來自於質譜儀的廠商,除了高額的軟體授權費之外,也只能使用廠商提供的方式來使用資料。因此,在本研究中,我們與長庚醫療財團法人合作,建立了一個資料視覺化的工具,能讀入現有的大量歷史資料或是新產生的資料,並產生視覺化的資料圖表,也能與不同的分類模型結合,訓練分類模型來預測抗藥性結果,並將結果資料整合顯示於圖表之中,使臨床或是研究人員能更容易觀察大量的資料。
In recent years, as matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) widely used in the field of biomedical, most of the medical care institutions has accumulated quite amount of data, but for now, the only way to access data of MALDI-TOF is through the software packaged by MALDI-TOF company. Except the high license fee charged, we can only use the data in limited way that the company implement in the software. Based on the fact we mention above, we cooperate with Chang Gung Memorial Hospital, and develop a program that can present visualized resistance data from MALDI-TOF and is able to combine with different feature selection algorithms to predict resistance result, let our users easily compare between different data and different features. With our own designed program, we can adjust our user interface, set parameters or extend functions to fit our own user. And as our closely cooperation with our users, we can handle our user’s feedback more efficiently than the original software.
摘要 i
Abstract ii
Table of Contents iii
List of Figures iv
List of Tables v
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 MALDI-TOF 1
1.3 Related Works 1
1.4 Motivation & Goal 2
Chapter 2 Materials and Methods 3
2.1 Dataset 3
2.2 Tool Design 4
2.3 Data Process 5
2.4 Diagram Presentation 7
2.5 Classification Model 8
2.6 Adaptation to Python 9
Chapter 3 Results 10
3.1 Data Visualize 10
3.2 Classification Model 19
3.3 Comparison 23
Chapter 4 Conclusions 26
4.1 Conclusions 26
4.2 Future Works 26
References 27
1. Wang, H.-Y., et al., Application of a MALDI-TOF analysis platform (ClinProTools) for rapid and preliminary report of MRSA sequence types in Taiwan. PeerJ, 2018. 6: p. e5784.
2. Wang, H.-Y., et al., A new scheme for strain typing of methicillin-resistant Staphylococcus aureus on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using machine learning approach. PLOS ONE, 2018. 13(3): p. e0194289.
3. Lévesque, S., et al., A Side by Side Comparison of Bruker Biotyper and VITEK MS: Utility of MALDI-TOF MS Technology for Microorganism Identification in a Public Health Reference Laboratory. PLOS ONE, 2015. 10(12): p. e0144878.
4. Deak, E., et al., Comparison of the Vitek MS and Bruker Microflex LT MALDI-TOF MS platforms for routine identification of commonly isolated bacteria and yeast in the clinical microbiology laboratory. Diagnostic Microbiology and Infectious Disease, 2015. 81(1): p. 27-33.
5. Jamal, W., M. John Albert, and V. O Rotimi, Real-time comparative evaluation of bioMerieux VITEK MS versus Bruker Microflex MS, two matrix-assisted laser desorption-ionization time-of-flight mass spectrometry systems, for identification of clinically significant bacteria. Vol. 14. 2014. 289.
6. Pedregosa, F., et al., Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res., 2011. 12: p. 2825-2830.
7. Stephens, D. and M. Diesing, A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data. PLOS ONE, 2014. 9(4): p. e93950.
8. Akar, Ö. and O. Gungor, Classification of Multispectral Images Using Random Forest Algorithm. Vol. 1. 2012. 105-112.
9. Bijalwan, V., et al., KNN based Machine Learning Approach for Text and Document Mining. Vol. 7. 2014.
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