Learn More
Several randomized trials have demonstrated non-small cell lung cancer (NSCLC) patients with activating epidermal growth factor receptor (EGFR) mutations can achieve favorable clinical outcomes on treatment with EGFR tyrosine kinase inhibitors (TKIs). EGFR mutation is considered as a predictive marker for efficacy of EGFR-TKIs in NSCLC. Here we show(More)
Feature selection has attracted significant attention in data mining and machine learning in the past decades. Many existing feature selection methods eliminate redundancy by measuring pairwise inter-correlation of features, whereas the complementariness of features and higher inter-correlation among more than two features are ignored. In this study, a(More)
—Finding an efficient way to discover Markov blanket is one of the core issues in data mining. This paper first discusses the problems existed in IAMB algorithm which is a typical algorithm for discovering the Markov blanket of a target variable from the training data, and then proposes an improved algorithm λ-IAMB based on the improving approach which(More)
The aim of this study was to explore the role of long non-coding RNA UCA1 (urothelial cancer-associated 1) in acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in EGFR-mutant non-small cell lung cancer (NSCLC). In our study, UCA1 expression was significantly increased in lung cancer cells and patients with(More)
BACKGROUND POTEE (POTE ankyrin domain family, member E) is a newly identified cancer-testis antigen that has been found to be expressed in a wide variety of human cancers including cancers of the colon, prostate, lung, breast, ovary, and pancreas. AIM To measure the serum levels of POTEE in patients with non-small-cell lung cancer (NSCLC) and to explore(More)
The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the(More)
In this paper, a novel feature selection method is presented, which is based on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To better capture the relationship between features and the class, class labels are separated into individual variables and relevance and redundancy are explicitly handled on each class label. Super-efficiency(More)