Zobrazeno 1 - 10
of 344
pro vyhledávání: '"Wong Wing H"'
Autor:
Harris Lyndsay N, Leung Hon-chiu E, Xu Xiu-qin, Shi Qian, Lu Xin, Zhang Xuegong, Iglehart James D, Miron Alexander, Liu Jun S, Wong Wing H
Publikováno v:
BMC Bioinformatics, Vol 7, Iss 1, p 197 (2006)
Abstract Background Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classificati
Externí odkaz:
https://doaj.org/article/a22bdfe0e1ad4be18bb10a86ca938da6
Publikováno v:
BMC Bioinformatics, Vol 7, Iss 1, p 44 (2006)
Abstract Background Many statistical algorithms combine microarray expression data and genome sequence data to identify transcription factor binding motifs in the low eukaryotic genomes. Finding cis-regulatory elements in higher eukaryote genomes, ho
Externí odkaz:
https://doaj.org/article/c91137d14baa45c4a0140786f7e1a892
Autor:
Wong Wing H, Hong Pengyu
Publikováno v:
BMC Bioinformatics, Vol 6, Iss 1, p 20 (2005)
Abstract Background Collecting and managing information is a challenging task in a genome-wide profiling research project. Most databases and online computational tools require a direct human involvement. Information and computational results are pre
Externí odkaz:
https://doaj.org/article/25412abea2df4946991f9ad48a5b0c56
Traditional MCMC algorithms are computationally intensive and do not scale well to large data. In particular, the Metropolis-Hastings (MH) algorithm requires passing over the entire dataset to evaluate the likelihood ratio in each iteration. We propo
Externí odkaz:
http://arxiv.org/abs/1908.02910
Autor:
Gennert, David G., Lynn, Rachel C., Granja, Jeff M., Weber, Evan W., Mumbach, Maxwell R., Zhao, Yang, Duren, Zhana, Sotillo, Elena, Greenleaf, William J., Wong, Wing H., Satpathy, Ansuman T., Mackall, Crystal L., Chang, Howard Y.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Jul . 118(30), 1-12.
Externí odkaz:
https://www.jstor.org/stable/27052636
Autor:
Li, Dangna, Wong, Wing H
In this paper we propose a general framework of performing MCMC with only a mini-batch of data. We show by estimating the Metropolis-Hasting ratio with only a mini-batch of data, one is essentially sampling from the true posterior raised to a known t
Externí odkaz:
http://arxiv.org/abs/1707.09705
In our recent paper, we showed that in exponential family, contrastive divergence (CD) with fixed learning rate will give asymptotically consistent estimates \cite{wu2016convergence}. In this paper, we establish consistency and convergence rate of CD
Externí odkaz:
http://arxiv.org/abs/1605.06220
The Contrastive Divergence (CD) algorithm has achieved notable success in training energy-based models including Restricted Boltzmann Machines and played a key role in the emergence of deep learning. The idea of this algorithm is to approximate the i
Externí odkaz:
http://arxiv.org/abs/1603.05729
Approximate Bayesian Computation (ABC) methods are used to approximate posterior distributions in models with unknown or computationally intractable likelihoods. Both the accuracy and computational efficiency of ABC depend on the choice of summary st
Externí odkaz:
http://arxiv.org/abs/1510.02175
Autor:
Bo Zhou, Purmann, Carolin, Hanmin Guo, GiWon Shin, Yiling Huang, Pattni, Reenal, Qingxi Meng, Greer, Stephanie U., Roychowdhury, Tanmoy, Wood, Raegan N., Ho, Marcus, zu Dohna, Heinrich, Abyzov, Alexej, Hallmayer, Joachim F., Wong, Wing H., Ji, Hanlee P., Urban, Alexander E.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America; 7/30/2024, Vol. 121 Issue 31, p1-12, 20p