We show that very different structure and ligand based methods for predicting drug-target interactions can be combined effectively using data fusion, outperforming any single method in ranking of actives.
We believe that many of the existing k-mer based methods for predicting TF-DNA interactions, which often involve filtering, normalization, and transformation of the binding data, can be combined with the key idea proposed in this paper to obtain a more accurate model.
Here we evaluate the performance of a molecular dynamics based method for predicting the binding affinities of different types of human thrombin inhibitors.
Considering limited available information on uncertainties in structural - acoustic coupled systems, two methods namely the vertex method and the Legendre orthogonal polynomial based method for predicting their dynamic behavior are developed based on the Statistical Energy Analysis SEAapproachapproach
In this paper, we first propose GM-SMCC, an effective and novel semi-supervised multi-label collective classification based method for predicting functional properties of proteins.
In a recent review [12], R Sharan et al described two main classes of the network-based methods for predicting protein functions: direct methods such as neighborhood counting based method [18],Graph theoretic methods [19], [20]; module-assisted methods such as hierarchical clustering-based methods [21], [22], graph clustering methods [23], [24].
The primary focus of this article is on the statistical mechanics-based methods for predicting RNA 3D structures and folding energy landscapes, and the related quantitative insights into RNA functions.
In contrast with sequence-based methods, comparatively little work has been done to explore structure-based methods for predicting peptide binding affinities to class II MHC.
This method can thus be explored as a potential tool to complement alignment-based, clustering-based, and structure-based methods for predicting protein function.
For example, Weib and Menzel (2008) compared the Priestley–Taylor (PT) method, two methods based on the Penman Monteith (PM) equation and the Hargreaves method, a temperature-based method for predicting PET in a global-scale hydrologic method.
For instance, as illustrated in Figure 4D, NORSnet, predicts the protein to be entirely lacking unstructured loops and PROFsec, a profile neural network based method predicting secondary structure, predicts the disordered region to be mostly helical.
