Machine Learning Methods and
Models for Recognizing Lung Inhomogeneity from Computed Tomography
Tao, Pan,
Galiullin, D,
Chen, X.L.,
Zhang, W.H.,
Yang, K,
Liu, K,
Zhao, L.Y.,
Chen, X.Z.,
Hu, J.K. (2021) . The performance of the
model was assessed with its discrimination, calibration, and clinical usefulness. A total
to 2015 y. As an
instrument for statistics, the autoregression panel
models of spatial lag were
applied
Baltina, L.A.,
Hour, M.-J.,
Liu, Y.-C.,
Chang, Y.-S.,
Huang, S.-H.,
Lai, H.-C.,
Kondratenko, R.M.,
Petrova, S.F.,
Yunusov, M.S.,
Lin, C.-W. (2021) with the docking
models. Compounds 13 and 14 also had a strong interaction with the active site pocket of NS5 MTase
the rotational diffusion
model. We find that the parameters of the diffusion
model can be adjusted to get