Chia-Li Han2
Jau-Song Yu1
2Chia-Li Han
1Jau-Song Yu
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We developed a multiplexed label-free quantification strategy, which integrates an efficient gel-assisted digestion protocol, high-performance liquid chromatography tandem MS analysis, and a bioinformatics alignment method to determine personalized proteomic profiles for membrane proteins in human tissues. This strategy provided accurate (6% error) and(More)
Cancer marker discovery is an emerging topic in high-throughput quantitative proteomics. However, the omics technology usually generates a long list of marker candidates that requires a labor-intensive filtering process in order to screen for potentially useful markers. Specifically, various parameters, such as the level of overexpression of the marker in(More)
Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell(More)
Unlike most cancers, thyroid cancer has an ever-increasing incidence rate over recent years. In order to better understand its molecular mechanisms, we acquired gene expression data from The Cancer Genome Atlas (TCGA), leveraged supervised machine learning methods to predict stages and outcomes, and utilized unsupervised machine learning methods to gain(More)
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