Twin support vector regression (TSVR) generates two nonparallel hyperplanes by solving a pair of smaller-sized problems instead of a single larger-sized problem in the standard SVR. Due to its efficiency, TSVR is frequently applied in various areas. In this paper, we propose a totally new version of TSVR named Linear Twin Quadratic Surface Support Vector Regression (LTQSSVR), which directly uses two quadratic surfaces in the original space for regression. It is worth noting that our new approach not only avoids the notoriously difficult and time-consuming task for searching a suitable kernel function and its corresponding parameters in the traditional SVR-based method but also achieves a better generalization performance. Besides, in order to make further improvement on the efficiency and robustness of the model, we introduce the 1-norm to measure the error. The linear programming structure of the new model skips the matrix inverse operation and makes it solvable for those huge-sized problems. As we know, the capability of handling large-sized problem is very important in this big data era. In addition, to verify the effectiveness and efficiency of our model, we compare it with some well-known methods. The numerical experiments on 2 artificial data sets and 12 benchmark data sets demonstrate the validity and applicability of our proposed method.
Background
The DEAD-box RNA-binding protein (RBP) DDX17 has been found to be involved in the tumorigenesis of many types of cancers. However, the role of DDX17 in lung adenocarcinoma (LUAD) remains unclear.
Methods
We silenced DDX17 expression in A549 LUAD cells by small interfering RNA (siRNA). Cell proliferation and apoptosis assays were performed to explore the functions of DDX17. Knockdown of DDX17 by siRNA significantly inhibited proliferation and induced apoptosis in A549 cells. We used high-throughput RNA sequencing (RNA-seq) to identify differentially expressed genes (DEGs) and alternative splicing (AS) events in DDX17 knockdown LUAD cells.
Results
DDX17 knockdown increased the expression levels of proapoptotic genes and decreased those of proproliferative genes. Moreover, the DDX17-regulated AS events in A549 cells revealed by computational analysis using ABLas software were strongly validated by quantitative reverse transcription–polymerase chain reaction (RT–qPCR) and were also validated by analysis of The Cancer Genome Atlas (TCGA)-LUAD dataset. These findings suggest that DDX17 may function as an oncogene by regulating both the expression and AS of proliferation- and apoptosis-associated genes in LUAD cells. Our findings may offer new insights into understanding the molecular mechanisms of LUAD and provide a new therapeutic direction for LUAD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.