Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

GeoChip: a comprehensive microarray for investigating biogeochemical, ecological and environmental processes

Abstract: Owing to their vast diversity and as-yet uncultivated status, detection, characterization and quantification of microorganisms in natural settings are very challenging, and linking microbial diversity to ecosystem processes and functions is even more difficult. Microarray-based genomic technology for detecting functional genes and processes has a great promise of overcoming such obstacles. Here, a novel comprehensive microarray, termed GeoChip, has been developed, containing 24 243 oligonucleotide (50 mer) probes and covering 410 000 genes in 4150 functional groups involved in nitrogen, carbon, sulfur and phosphorus cycling, metal reduction and resistance, and organic contaminant degradation. The developed GeoChip was successfully used for tracking the dynamics of metal-reducing bacteria and associated communities for an in situ bioremediation study. This is the first comprehensive microarray currently available for studying biogeochemical processes and functional activities of microbial communities important to human health, agriculture, energy, global climate change, ecosystem management, and environmental cleanup and restoration. It is particularly useful for providing direct linkages of microbial genes/populations to ecosystem processes and functions.

Robust multi-view spectral clustering via low-rank and sparse decomposition

Abstract: Multi-view clustering, which seeks a partition of the data in multiple views that often provide complementary information to each other, has received considerable attention in recent years. In real life clustering problems, the data in each view may have considerable noise. However, existing clustering methods blindly combine the information from multi-view data with possibly considerable noise, which often degrades their performance. In this paper, we propose a novel Markov chain method for Robust Multi-view Spectral Clustering (RMSC). Our method has a flavor of lowrank and sparse decomposition, where we firstly construct a transition probability matrix from each single view, and then use these matrices to recover a shared low-rank transition probability matrix as a crucial input to the standard Markov chain method for clustering. The optimization problem of RMSC has a low-rank constraint on the transition probability matrix, and simultaneously a probabilistic simplex constraint on each of its rows. To solve this challenging optimization problem, we propose an optimization procedure based on the Augmented Lagrangian Multiplier scheme. Experimental results on various real world datasets show that the proposed method has superior performance over several state-of-the-art methods for multi-view clustering.