Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Youhan Fang"'
Autor:
Yuan Qi, Youhan Fang, David R Sinclair, Shangqin Guo, Meritxell Alberich-Jorda, Jun Lu, Daniel G Tenen, Michael G Kharas, Saumyadipta Pyne
Publikováno v:
PLoS ONE, Vol 15, Iss 2, p e0228651 (2020)
A new computational framework for FLow cytometric Analysis of Rare Events (FLARE) has been developed specifically for fast and automatic identification of rare cell populations in very large samples generated by platforms like multi-parametric flow c
Externí odkaz:
https://doaj.org/article/84362fa455664ae0a5ea0a7fde02aee9
Autor:
Skeel, Robert D.1 rskeel@purdue.edu, Youhan Fang1 yfang@purdue.edu
Publikováno v:
Entropy. Oct2017, Vol. 19 Issue 10, p561. 16p.
Autor:
Robert D. Skeel, Youhan Fang
Publikováno v:
Entropy, Vol 19, Iss 10, p 561 (2017)
Markov chain Monte Carlo sampling propagators, including numerical integrators for stochastic dynamics, are central to the calculation of thermodynamic quantities and determination of structure for molecular systems. Efficiency is paramount, and to a
Externí odkaz:
https://doaj.org/article/cf66bfe585e94b97bea05e055ecd50da
Publikováno v:
Journal of Chemical Physics; 5/7/2014, Vol. 140 Issue 17, p174108-1-174108-10, 10p, 2 Charts, 2 Graphs
Publikováno v:
The Journal of Chemical Physics. 144:029901
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a3b253b50134e6e1b51cc076231711b
https://doi.org/10.1063/1.4940219
https://doi.org/10.1063/1.4940219
Publikováno v:
The FASEB Journal. 24
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6708b2261d5878ddca327deebf1e258b
https://doi.org/10.1096/fasebj.24.1_supplement.212.8
https://doi.org/10.1096/fasebj.24.1_supplement.212.8
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642152931
WABI
WABI
Multi-channel, high throughput experimental methodologies for flow cytometry are transforming clinical immunology and hematology, and require the development of algorithms to analyze the high-dimensional, large-scale data. We describe the development
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::640a435ac44ea178bb234c51087b195a
https://doi.org/10.1007/978-3-642-15294-8_14
https://doi.org/10.1007/978-3-642-15294-8_14
Publikováno v:
ICPR
Scopus-Elsevier
Scopus-Elsevier
In this paper, we propose a non-parametric discriminant analysis method (no assumption on the distributions of classes), called Parzen discriminant analysis (PDA). Through a deep investigation on the non-parametric density estimation, we find that mi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::933a96ce8e623175f19e3d0cbf172b3c
https://doi.org/10.1109/icpr.2008.4761903
https://doi.org/10.1109/icpr.2008.4761903
Publikováno v:
The Journal of Chemical Physics. 140:174108
One of the most demanding calculations is to generate random samples from a specified probability distribution (usually with an unknown normalizing prefactor) in a high-dimensional configuration space. One often has to resort to using a Markov chain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::093bcc1af186a1fc026b06f9611144d7
https://doi.org/10.1063/1.4874000
https://doi.org/10.1063/1.4874000
Autor:
Qi, Yuan1,2 (AUTHOR) alanqi@cs.purdue.edu, Fang, Youhan1 (AUTHOR), Sinclair, David R.3,4,5 (AUTHOR), Guo, Shangqin6 (AUTHOR), Alberich-Jorda, Meritxell7 (AUTHOR), Lu, Jun8,9 (AUTHOR), Tenen, Daniel G.10,11,12 (AUTHOR), Kharas, Michael G.13 (AUTHOR), Pyne, Saumyadipta4,14 (AUTHOR) alanqi@cs.purdue.edu
Publikováno v:
PLoS ONE. 2/11/2020, Vol. 15 Issue 2, p1-18. 18p.