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The proposed models demonstrated a high correlation among the variables and responses.
Studies using genetically engineered mouse models demonstrated a central role of JAK2 V617F in the pathogenesis of MPNs.
In vivo studies using rat hip and rabbit calvarial defect models demonstrated a high osseointegration potential of the TiCaPCON/PTFE implants.
Notably, the ligand-guided ALiBERO-based mSUCNR1 models demonstrated a dramatic improvement in retrospective virtual screening performance of the developed compounds compared with the initial homology models and proved successful in separating the majority of active from inactive ligands in docking screens (Fig. 3a, Supplementary Fig. S2).
Konnov (2015) and ELTE (Varga et al., 2016) models demonstrated a uniform trend at all conditions tested: the second mechanism predicts lower burning velocities which are in better agreement with the heat flux measurements from different groups.
The models demonstrated a strong fit with published data and experimental results (R2=0.75 to 0.99) and predicted the temporal total construct mass with reasonable accuracy (30% RMS error).
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Compared to statistical regression models, RBFN models demonstrated an improvement of more than 20%.
The models demonstrated an excellent fitting to the measured data with coefficient of determination varied between 0.987 and 0.999.
The regression models demonstrated an impact of the observed income-related dimensions on these rates.
The GA-SVR models demonstrate a better performance compared to the traditional bed load formulas.
The 3D-QSAR models demonstrate a good ability to predict the activity of the designed compounds (r2 = 0.967, q2 = 0.756).
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