LncRNA-protein interaction prediction with reweighted feature selection.

LncRNA-protein interactions are ubiquitous in organisms and play a crucial role in a variety of biological processes and complex diseases. Many computational methods have been reported for lncRNA-protein interaction prediction. However, the experimental techniques to detect lncRNA-protein interactions are laborious and time-consuming. Therefore, to address this challenge, this paper proposes a reweighting boosting feature selection (RBFS) method model to select key features. Specially, a reweighted apporach can adjust the contribution of each observational samples to learning model fitting; let higher weights are given more influence samples than those with lower weights. Feature selection with boosting can efficiently rank to iterate over important features to obtain the optimal feature subset. Besides, in the experiments, the RBFS method is applied to the prediction of lncRNA-protein interactions. The experimental results demonstrate that our method achieves higher accuracy and less redundancy with fewer features.

A flexible surface-enhanced Raman Spectroscopy chip integrated with microlens.

A novel flexible Surface-enhanced Raman Spectroscopy (SERS) chip integrated with microlens was proposed and designed, which consisted of PDMS film, planoconvex microlens, and silver nanoparticles (AgNPs) monolayer, and was of high signal collection efficiency. The flexible PDMS film integrated with microlens was designed by optical simulation, and fabricated by optimized micromachining process. AgNPs monolayer were uniformly assembled on the other side of the PDMS film through a liquid-liquid interface self-assembly method to form SERS chip. The prepared chip revealed excellent SERS performance with a Raman enhancement factor of about 107 and a signal variation of <11.5 %. The SERS chip was successfully utilized for in-situ detection of thiram residues on tomato skins, and its characteristic peaks could still be clearly distinguished when the concentration was down to 2.5 µM. It was shown that the proposed SERS chip was suitable for in-situ detection of a real sample on complex surface morphology and shown potential prospect in the fields of chemical and biomedical detections.