||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.|
||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.