title: Selecting protein families for environmental features based on manifold regularization.
abstract: Recently, statistics and machine learning have been developed to identify functional or taxonomic features of environmental features or physiological status. Important proteins (or other functional and taxonomic entities) to environmental features can be potentially used as biosensors. A major challenge is how the distribution of protein and gene functions embodies the adaption of microbial communities across environments and host habitats. In this paper, we propose a novel regularization method for linear regression to adapt the challenge. The approach is inspired by local linear embedding (LLE) and we call it a manifold-constrained regularization for linear regression (McRe). The novel regularization procedure also has potential to be used in solving other linear systems. We demonstrate the efficiency and the performance of the approach in both simulation and real data.
authors.forename: Xingpeng
authors.initials: X
authors.lastname: Jiang
authors.forename: Weiwei
authors.initials: W
authors.lastname: Xu
authors.forename: E K
authors.initials: EK
authors.lastname: Park
authors.forename: Guangrong
authors.initials: G
authors.lastname: Li
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D016014:Linear Models; D008565:Membrane Proteins / Q000235:genetics*; D064307:Microbiota; D054712:Salinity
chemical_list: D008565:Membrane Proteins