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Several factors can contribute to the improved prediction performance of our approach in predicting RD values from sequences alone.
The present model is shown to yield significant improvement over a homogeneous modeling approach in predicting chemical species mole fractions, as well as accurate predictions of temperature and pressure.
These results indicate usefulness and applicability of the proposed approach in predicting the LCC.
In order to evaluate suitability of our approach in predicting coauthorships in the Lattes dataset, several experiments were run.
We propose a two-step approach in predicting Total Organic Carbon (TOC) content from well log data.
Results demonstrate the strength of our approach in predicting delays, elapsed times and other system performance measures.
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Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs.
The adequacy of these simplified approaches in predicting laminated glass window responses to blast loads is discussed.
The performance of the model is compared with several state-of-the-art approaches, in predicting human fixations using three different eye-tracking datasets.
Our previous work [28] illustrates the usefulness of a combination of chemical similarity and machine-learning approaches in predicting the activity of the Tox21 dataset with high accuracy for a majority of the targets considered in the challenge [29].
Results shows that the proposed method remains accurate and robust in parameter estimation under different settings of synthetic networks and outperforms other selected approaches in predicting the status of network formation.
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