Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Huai-bei Zhou"'
Autor:
Huai-bei Zhou
Publikováno v:
Planetary and Space Science. 44:603-610
The dynamic response of ionospheric plasmas is modeled for current sources induced by a pulsed tether. A new method was developed which combines analytic and numerical techniques to study the dynamic response of a 2-D magnetoplasma to a time-dependen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb60d01bed1c52488d7cbad4010f4d4e
https://doi.org/10.1016/0032-0633(96)00007-4
https://doi.org/10.1016/0032-0633(96)00007-4
Autor:
Lu Wang, † Huai-bei Zhou
Publikováno v:
The Journal of Physical Chemistry. 100:8101-8105
An analysis of molecular dynamics simulation of a model α-helix indicates that the motion of the helix system is chaotic. This system's behavior is due to an intrinsic sensitivity to initial conditions, which makes initially similar conformations of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::499d4b0ab58c591c58e73eb42ac8abcf
https://doi.org/10.1021/jp953409x
https://doi.org/10.1021/jp953409x
Confronting the problem of interconnected structural changes in the comparative modeling of proteins
Publikováno v:
Proteins: Structure, Function, and Genetics. 23:327-336
Comparative models of three proteins have been built using a variety of computational methods, heavily supplemented by visual inspection. We consider the accuracy obtained to be worse than expected. A careful analysis of the models shows that a major
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fcdcc3310c3e08742289562baff97eed
https://doi.org/10.1002/prot.340230307
https://doi.org/10.1002/prot.340230307
Publikováno v:
Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).
Nowadays, the best methods for protein secondary structure prediction are based on neural network and support vector machine, and both of them incorporate the information from multiple sequences alignment. However the two methods were executed on dif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22d069112dca81970dab0008b0c1f9f0
https://doi.org/10.1109/icmlc.2004.1378319
https://doi.org/10.1109/icmlc.2004.1378319
Publikováno v:
Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).
In this paper, we present an improved KNN (k-nearest neighbor) classification method called KNN+. In order to estimate its performance, we compare it with the original KNN method in the area of gene expression based tumor diagnosis, by the aid of two
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::be41cfaab44b20a95e82cea27749dbc3
https://doi.org/10.1109/icmlc.2004.1378515
https://doi.org/10.1109/icmlc.2004.1378515
Autor:
Juan Liu, Huai-Bei Zhou
Publikováno v:
Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).
Gene expression microarray data can be used to classify tumor types. We proposed a new procedure to classify human tumor samples based on microarray gene expressions by using a hybrid supervised learning method called MOEA/WV (multi-objective evoluti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6cd08f37ac737b3728e2f2bd5a13a553
https://doi.org/10.1109/icmlc.2003.1259886
https://doi.org/10.1109/icmlc.2003.1259886
Publikováno v:
Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).
This paper presents the application of SVMs to gene expression data based tumor diagnosis. Since there are large amount of genes and small number of samples in gene data and too many genes can harm the performance of the discrimination and increase t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::99d6865b54c78590d07a68cbd0efd12d
https://doi.org/10.1109/icmlc.2003.1259682
https://doi.org/10.1109/icmlc.2003.1259682
Publikováno v:
Wuhan University Journal of Natural Sciences; Nov2004, Vol. 9 Issue 6, p962-966, 5p
Publikováno v:
Wuhan University Journal of Natural Sciences; May2004, Vol. 9 Issue 3, p323-326, 4p
Publikováno v:
Wuhan University Journal of Natural Sciences; Sep2003, Vol. 8 Issue 3, p765-768, 4p