Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Peiying Ruan"'
Autor:
Jiahui Guan, Krishna Juluru, Yothin Rakvongthai, Benjamin S. Glicksberg, Watsamon Jantarabenjakul, Li-Chen Fu, Mike Fralick, Anthony Costa, Quanzheng Li, Andrew Feng, Eric K. Oermann, Joshua D. Kaggie, Xihong Lin, Pedro Mário Cruz e Silva, Deepeksha Bhatia, Byung Seok Kim, Hitoshi Mori, Pablo F. Damasceno, Peiying Ruan, Yuhong Wen, Hao-Hsin Shin, Amilcare Gentili, Weichung Wang, Chiu-Ling Lai, Jason C. Crane, Andrew N. Priest, Soo-Young Park, Peerapon Vateekul, Matheus Ribeiro Furtado de Mendonça, Gustavo César de Antônio Corradi, Griffin Lacey, Meena AbdelMaseeh, Yu Rim Lee, Tatsuya Kodama, Pierre Elnajjar, Krishna Nand Keshava Murthy, Xiang Li, Evan Leibovitz, Vitor Lavor, Christopher P. Hess, Colin B. Compas, Stefan Gräf, Masoom A. Haider, Daguang Xu, Nicola Rieke, Thanyawee Puthanakit, Sarah E Hickman, Hui Ren, Marcio Aloisio Bezerra Cavalcanti Rockenbach, Jung Gil Park, Jesse Tetreault, Hisashi Sasaki, Min Kyu Kang, Won Young Tak, Chun-Nan Hsu, Fiona J. Gilbert, Chin Lin, Varun Buch, Felipe Kitamura, Tony Mazzulli, Eddie Huang, Abood Quraini, Shelley McLeod, Young Joon Kwon, Gustavo Nino, Dufan Wu, Chien-Sung Tsai, Mona Flores, Baris Turkbey, Sira Sriswasdi, Pochuan Wang, Mohammad Adil, Aoxiao Zhong, Chih-Hung Wang, Sheng Xu, C. K. Lee, Isaac Yang, Marius George Linguraru, Holger R. Roth, Chia-Jung Hsu, Anas Z. Abidin, Thomas M. Grist, Hirofumi Obinata, Sheridan Reed, Andrew Liu, Ahmed Harouni, Natalie Gangai, Ittai Dayan, Kristopher Kersten, Stephanie Harmon, Jae Ho Sohn, John Garrett, Bradford J. Wood, Sharmila Majumdar, Bernardo Bizzo, Shuichi Kawano, Keith J. Dreyer, Carlos Tor-Díez, Chia-Cheng Lee
Publikováno v:
Nature Medicine. 27:1735-1743
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe
It is known that many driver nodes are required to control complex biological networks. Previous studies imply that O(N) driver nodes are required in both linear complex network and Boolean network models with N nodes if an arbitrary state is specifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c027f45fdc2125189e791e231ed28768
Publikováno v:
Journal of theoretical biology. 463
It is known that many driver nodes are required to control complex biological networks. Previous studies imply that O(N) driver nodes are required in both linear complex network and Boolean network models with N nodes if an arbitrary state is specifi
Publikováno v:
BMC Bioinformatics
BMC Bioinformatics, 2018, 19 (S1)
BMC Bioinformatics, BioMed Central, 2018, 19 (S1)
BMC Bioinformatics, Vol 19, Iss S1, Pp 73-84 (2018)
BMC Bioinformatics, 2018, 19 (S1)
BMC Bioinformatics, BioMed Central, 2018, 19 (S1)
BMC Bioinformatics, Vol 19, Iss S1, Pp 73-84 (2018)
Background Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3548a1fbb712fdcdb81d37ad94a29467
https://hal.science/hal-01985434
https://hal.science/hal-01985434
Publikováno v:
Integrated Computer-Aided Engineering. 19:23-38
Grammar-based compression is to find a small grammar that generates a given data and has been well-studied in text compression. In this paper, we apply this methodology to compression of rectangular image data. We first define a context-free rectangu
Publikováno v:
BMC Bioinformatics
Background Modeling high-dimensional data involving thousands of variables is particularly important for gene expression profiling experiments, nevertheless,it remains a challenging task. One of the challenges is to implement an effective method for
Publikováno v:
PLoS ONE, Vol 8, Iss 6, p e65265 (2013)
PLoS ONE
PLoS ONE
Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexe
Publikováno v:
Future Generation Information Technology ISBN: 9783642175688
FGIT
FGIT
Grammar-based compression is to find a small grammar that generates a given data and has been well-studied in text compression. In this paper, we apply this methodology to compression of rectangular image data. We first define a context-free rectangu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8175404489c6f3d0a3d845e4b99e62ea
https://doi.org/10.1007/978-3-642-17569-5_24
https://doi.org/10.1007/978-3-642-17569-5_24