Fast adaptive neighbors clustering via embedded clustering
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.02.087
Abstract: Abstract Recently, spectral clustering (SC) has been gaining more and more attention due to its excellent performance in unsupervised learning. However, the computational complexity of the SC is high. Also, the adjacency graph matrix of… read more here.
Keywords: graph embedded; embedded data; fast adaptive; raw data ... See more keywords
Graph embedded ensemble deep randomized network for diagnosis of Alzheimer's disease.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/ACM transactions on computational biology and bioinformatics"
DOI: 10.1109/tcbb.2022.3202707
Abstract: Randomized shallow/deep neural networks with closed form solution avoid the shortcomings that exist in the back propagation (BP) based trained neural networks. Ensemble deep random vector functional link (edRVFL) network utilize the strength of two… read more here.
Keywords: graph embedded; alzheimer disease; ensemble deep; diagnosis alzheimer ... See more keywords
Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype–phenotype interactions
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Genetics"
DOI: 10.3389/fgene.2022.921775
Abstract: Motivation: A central goal of current biology is to establish a complete functional link between the genotype and phenotype, known as the so-called genotype–phenotype map. With the continuous development of high-throughput technology and the decline… read more here.
Keywords: multi omics; expression quantitative; graph embedded; graph ... See more keywords