Sentence examples for implemented for prediction from inspiring English sources

Candidate crystallographic slip-based parameters are implemented for prediction of fatigue crack initiation.

Yu et al. propose an approach based on probabilistic latent semantic analysis (PLSA) to extract sentiment factors, and two autoregressive models are then implemented for prediction [7].

Based on these important features of molecules, together with skeletal branching pattern, both the electrotopological state (E-state) and molecular connectivity (Chi indices) structure descriptors were successfully implemented for prediction of genotoxicity and carcinogenicity [18, 43, 44].

Moreover, a meta-analysis suggests that mutations in KRAS exons 3 and 4, NRAS, BRAF, PIK3CA, and nonfunctional PTEN predict resistance to anti-EGFR therapies [ 70] and concluded that these biomarkers should be implemented for prediction of clinical benefit from anti-EGFR antibodies in mCRC.

Amongst many machine learning methods, neural network has been widely implemented for predictive modeling and shows excellent prediction performance compared to logistic regression [ 28- 31].

The end-to-end horizontally scalable application is designed and implemented for route prediction.

In the outlook, it is concluded that more complicated cyclic hardening models should be implemented for better prediction of distortion and residual stresses after heat treatment or welding.

In this paper, a hybrid genetic programming (GP) with multiple genes is implemented for developing prediction models of spectral energy demands.

A new scheme was developed for assortment of the element risk factor (defined as the ratio of the strain energy density to the yield strain energy for each element) and implemented for the prediction of the failure strength.

Furthering our earlier work of indirect degradation estimation via a mixed time-/condition-based approach, a stage-based Gamma process is designed and implemented for the prediction of degradation PDF where a recursive Maximum Likelihood Estimation (MLE) algorithm deduced from the conventional MLE algorithm is able to update the modeling parameters based on each aperiodic monitoring interval.

To the best of our knowledge, this concept has never been implemented for AMPs prediction and also for the other type of bioinformatics applications.