In recent years, proteomics research has become a popular method for characterizing the functional proteins driving the transformation of malignancy, tracing the large-scale protein alterations induced by anti-cancer drug, as well as discovering the innovative targets and first-in-class drugs for oncologic disorders. Proteomics became popular due to its ability to provide quantitative and dynamic information on tumor genesis and development by directly profiling protein expression. Label-free Quantification (LFQ) is an important method for quantifying protein expression in cancer proteomics. However, the main challenge in using this method for discovering anti-cancer targets and drugs is the low precision, poor reproducibility, and inaccuracy of the LFQ of proteomics data.
This article by Dr. Feng Zhu et al. is published in Current Pharmaceutical Design, 2018. To obtain the article, please visit: http://www.eurekaselect.com/166976
Bentham Science 
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