Sentence examples for based on training sets from inspiring English sources

Supervised and unsupervised classifications rely on classical set theory in assigning pixels into discrete classes based on training sets and some statistically determined criteria.

Previous studies have reported the development of in silico QSAR models based on training sets of relatively small and mostly similar compounds; this model covers a broader range of melanin-binding affinities than what has been previously published and identified several physiochemical descriptors to be considered in the design of compounds where melanin-binding modulation is desired.

Two models, SE and ME, were applied in both methods based on training sets obtained from WP and AP.

They use state-of-the-art classifier to predict interactions based on training sets and identify thousands of new potential interactions.

Wood chemical compositions (holocellulose, α-cellulose, and lignin contents) were determined using near-infrared reflectance spectroscopy (NIRS) according to Schimleck et al. (2004) based on training sets (models) derived from wet chemistry analyses techniques described in Tian et al. (2012).

However, it should be noted that the fitted model forming the basis of the Veridex signature was calculated on an entirely independent cohort of patients, in contrast to our models which were based on training sets from the TRANSBIG data.

We started the development of ligand-based pharmacophore models based on training set compounds with high activity (IC50 <100 nM) and several-fold selectivity for IKK-β over IKK-α.

Marker panels were generated for MTBC and NTM ('phylo groups') and for individual species within those groups, using the same method as for staphylococci based on training set consisting of data sets MTBC_A1 (338 MTBC clinical isolates, Supplementary Data 6) and Myco_SRA (380 Mycobacterial samples downloaded from the SRA, Supplementary Data 11).

Statistically valid 3D-QSAR models with good correlation and predictive power were obtained with CoMFA steric and electrostatic fields (r2 = 0.797, q2 = 0.589) and CoMSIA with combined steric, electrostatic, hydrophobic and hydrogen bond acceptor fields (r2 = 0.867, q2 = 0.570) based on training set of 33 molecules with predictive r2 of 0.808 and 0.890 for CoMFA and CoMSIA respectively.

The Cooper statistics based on training set indicated an accuracy of 91%, high value of sensitivity (96%) and specificity (86%).

Models were generated based on training set compounds using the Genetic Function Approximation (GFA) as implemented in the Discovery Studio [57] and the best model (based on the lack-of-fit criterion) was validated with the test set.