Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Hengshi Yu"'
Autor:
Hengshi Yu, Joshua D. Welch
Publikováno v:
Genome Biology, Vol 22, Iss 1, Pp 1-26 (2021)
Abstract Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on
Externí odkaz:
https://doaj.org/article/fa386cea7a6c4de2901834c11ed128d8
Publikováno v:
Contemporary Clinical Trials Communications, Vol 19, Iss , Pp 100605- (2020)
Cluster randomized trials (CRTs) usually randomize groups of individuals to interventions, and outcomes are typically measured at the individual level. Marginal intervention effects are frequently of interest in CRTs due to their population-averaged
Externí odkaz:
https://doaj.org/article/705a0322a4944e6498855d7235c96d39
Autor:
Hengshi Yu, Joshua D. Welch
Small molecule treatment and gene knockout or overexpression induce complex changes in the molecular states of cells, and the space of possible perturbations is too large to measure exhaustively. We present PerturbNet, a deep generative model for pre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a36cda9715afda6dbb0f8eb0f44aca10
https://doi.org/10.1101/2022.07.20.500854
https://doi.org/10.1101/2022.07.20.500854
Publikováno v:
Biostatistics. 23:772-788
Stepped wedge cluster randomized trials (SW-CRTs) with binary outcomes are increasingly used in prevention and implementation studies. Marginal models represent a flexible tool for analyzing SW-CRTs with population-averaged interpretations, but the j
Publikováno v:
The Biochemist. 41:34-38
Beginner's Guides each cover a key technique and offer the scientifically literate but not necessarily expert audience a background briefing on the underlying science of a technique that is (or will be) widely used in molecular bioscience. The series
Autor:
Joshua D. Welch, Hengshi Yu
Publikováno v:
Genome Biology
Genome Biology, Vol 22, Iss 1, Pp 1-26 (2021)
Genome Biology, Vol 22, Iss 1, Pp 1-26 (2021)
Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d3dd5d2093bbefa3693085e414e6f7c
Autor:
Gallis, John A.1 john.gallis@duke.edu, Hengshi Yu2 hengshi@umich.edu, Fan Li3 frank.li@duke.edu, Turner, Elizabeth L.1 liz.turner@duke.edu
Publikováno v:
Stata Journal. 2018, Vol. 18 Issue 2, p357-378. 22p.
Sampling from Disentangled Representations of Single-Cell Data Using Generative Adversarial Networks
Autor:
Hengshi Yu, Joshua D. Welch
Deep generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), have achieved remarkable successes in generating and manipulating highdimensional images. VAEs excel at learning disentangled image represen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c7849b741c6841187f93e7dd3a61e086
https://doi.org/10.1101/2021.01.15.426872
https://doi.org/10.1101/2021.01.15.426872
Autor:
Hengshi Yu, Welch, Joshua D.
Additional file 1 Tables S1 and Figures S1–S15
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21b8f172e24e2e56857ca7ff5734aa24
Autor:
Hengshi Yu, Welch, Joshua D.
Additional file 2 Review history
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::480d7a40e4aa4ab2fb647f0f67e8064f