Sentence examples similar to based methods for prediction from inspiring English sources

Computational quantum mechanics is leading to new, theoretically based methods for the prediction of thermodynamic properties and phase behavior of interest to engineers.

All of the above machine-learning based methods for drug target interaction prediction formulate the task as a binary classification problem, with the goal to classify a given drug target pair as binding or non-binding.

This is a profound and composite method with the essence of sequential, structural and evolutionary based methods for protein function prediction.

Moreover, we wish to add more tree estimation tools besides MrBayes [ 18, 19], e.g. RAxML [ 51], and add likelihood based methods for labeling, as well as add TFBS prediction modules to enable use without Genomatix or Mapper access, automated grouping by clustering of TFBS predictions, and import of experimental (e.g. ChIP on Chip [ 50]) data.

In this work, we present a Support Vector Machine SVMM) based method for the prediction of fungal adhesins and adhesin-like proteins.

To our knowledge, there are two sequence-based methods for prediction of protein RNA interactions: catRAPID (Bellucci et al., 2011) and RPISeq (Muppirala et al., 2011).

In this study, we assessed, using the same data set and accounting for measurement artefacts, (1) the predictive advantage of BIS compared to SFBIA; (2) the predictive power of four popular 50 kHz single frequency methods compared to; (3) the performance of the two most frequently used BIS-based methods for prediction of TBW as measured by the reference method of tritium dilution.

For Overload detection methods, there are two types of algorithms, a) Threshold based methods, and b) Prediction based method.

The IEDB database comprises a series of most up-to-date and evidence based methods specifically created for the prediction of MHC restricted T cell epitopes.

Adem and Vanapalli ([2013]) proposed the Modulus of Elasticity Based Method (MEBM) for deformation prediction in similar soils by using a semi-empirical estimation of the modulus of elasticity (Vanapalli and Oh [2010]) along with transient changes in soil suction (Wilson [1997]).

Therefore, we set forth to explore machine-learning based methods based on various protein features for the prediction of CDKI proteins.