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Centrality in the proximity network is also found to be a significant descriptor with moderate importance.
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Selection of informative descriptors was found to be a crucial step in the development of scoring models, and three factors that affected scoring model performance were therefore analyzed in detail, i.e. the number of descriptors selected (quantity), discriminative power of descriptors (quality) and correlation between descriptors.
This may also explain why sKIT was the most significant descriptor of tumor size when biomarker response was linked to longitudinal tumor data but was not found to be a good predictor of overall survival.
Similar to the observation made in our previous studies (Zhu et al. 2008), the Jurkat cell line was found to be the most significant biological descriptor for predicting in vivo toxicity, followed by the SK-N-SH cell line.
Five of the six descriptors were found to be statistically significant after sensitivity analyses.
The physico-chemical descriptors such as polarizabilityAHP, kappa2, XcompDipole, +vePotentialSurfaceArea, XKMostHydrophilic were found to show a significant correlation with biological activity in benzimidazole derivatives.
ELUMO was also a significant descriptor, and increasing ELUMO of the PCB leads to decreasing KOA.
The physicochemical descriptor SssCH2count and alignment-independent descriptors T_N_O_6, T_2_Cl_6 and Chi5 were found to show significant correlation with biologic activity in imidazolinones.
Most relevant descriptors were found to encode size and hydrophobic interactions.
In the case of Fungi, the pure effect of plant communities was the most significant descriptor.
In this case, the α was the most significant descriptor.
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