Sentence examples for been explored for predicting from inspiring English sources

Recently, machine learning methods such as neural networks and support vector machines have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic and ADMET property.

Many machine learning models haven been explored for predicting PPIs and most of them are based on traditional classifiers.

Several other machine learning (ML) methods have been explored for predicting the functional classes of proteins and peptides.

Machine learning methods have been explored for predicting protein function from amino acid sequence derived structural and physicochemical properties (des Jardins et al. 1997; Jensen et al. 2002; Karchin et al. 2002; Jensen et al. 2003; Cai et al. 2003; Cai and Lin, 2003; Cai et al. 2004b; Bhasin and Raghava, 2004a; Han et al. 2004b; Cai and Chou, 2005; Guo et al. 2006).

Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins.

In addition, digital soil mapping and modelling techniques (e.g. McBratney et al. 2000) are being explored for predicting the preliminary suitability for teak plantations, in combination with some previous point observations and some broad-scale variables, such as those derived from Landsat images, Digital Elevation Models (DEM) and their derivatives.

Biot's model of wave propagation in porous isotropic materials is explored for predicting ground vibration from rail vehicles on vertically heterogeneous isotropic saturated soil and rock using seismic reflectivity methods combined with a multi-degree-of-freedom model of a transit vehicle bogie.

Active sites of the members of each class share common structural and physicochemical properties to support the common functionality, which can be explored for predicting the function of proteins from amino acid sequence derived structural and physicochemical descriptors independent of sequence homology.

Having only been explored for confirming predicted miRNA targets [ 24, 25] or extracting tumor-classifying molecular signatures [ 26], these parallel expression datasets have far more potential to be exploited.

Microarray expression data sets were explored for the predicted AtLRR-RLK genes using the Arabidopsis Affymetrix GeneChip® average data available on the GENEVESTIGATOR analysis tool site (http://www.genevestigator.ethz.ch) [ 20].

As more psychrophile genomes are sequenced and published, they can be explored for additional alkane catabolism pathways predicted from environmental evidence but missing in the current set of analyzed genomes.