Shandong University

About: Shandong University is a education organization based out in Jinan, Shandong, China. It is known for research contribution in the topics: Laser & Cancer. The organization has 99070 authors who have published 99160 publications receiving 1625094 citations. The organization is also known as: Shāndōng Dàxué.

Dealloying to nanoporous Au/Pt alloys and their structure sensitive electrocatalytic properties

Abstract: A simple and general dealloying method is employed to fabricate nanoporous Au/Pt alloys with pre-determined alloy compositions. Structural characterization by electron microscopes demonstrates that selective etching of Cu from Au/Pt/Cu alloy precursors results in the formation of three-dimensional bicontinuous porous network structures with uniform pores and ligaments less than 10 nm. X-Ray photoelectron spectroscopy and X-ray diffraction demonstrate that nanoporous Au/Pt alloys have a single-phase cubic structure with relatively uniform compositions across the samples. These high surface area alloy nanostructures show much enhanced specific activity and distinct surface reactivity toward the electrooxidation of some small organic molecules, such as methanol and formic acid, as the Au content varies within the structure, thus holding great potential for use in clean energy and environmental applications.

HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction

Abstract: // Xing Chen 1, * , Chenggang Clarence Yan 2, * , Xu Zhang 3 , Zhu-Hong You 4 , Yu-An Huang 5 , Gui-Ying Yan 6 1 School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China 2 Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, China 3 School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, China 4 School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China 5 Department of Computing, Hong Kong Polytechnic University, Hong Kong, China 6 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China * The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. Correspondence to: Xing Chen, email: xingchen@amss.ac.cn Gui-Ying Yan, email: yangy@amss.ac.cn Keywords: microRNA, disease, microRNA-disease association, heterogeneous network, similarity Received: May 12, 2016 Accepted: July 28, 2016 Published: August 12, 2016 ABSTRACT Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models.