Sentence examples for predictions generated with from inspiring English sources

Or, more accurately, Predict the ORIT four days in advance by averaging the predictions generated with PDDs of 4, 5, and 6 (with overall mean |prediction error| of 0.00223).

For each datapoint in either the test set or in the hold-out fold in the case of cross-validation, we calculated the standard deviation of the predictions generated with each model conforming the model ensemble.

Model extensibility to different emulsion compositions and homogenizer pressures was investigated by comparing model predictions generated with the base case parameters to drop volume distributions measured under different conditions.

Thus, ChIP-Seq-defined regions are used as filters to refine the computational predictions generated with PSSMs.

The results of the aforementioned SIDER search were combined to 3'UTR predictions generated with PRED-A-TERM, a Leishmania-specific mRNA extremity prediction algorithm [ 35], in order to ascertain the relative genomic dissemination of SIDER elements (Table 1).

We used curated factors associated with Prostatic Neoplasms (MeSH ID: D011471), Lung Neoplasms (D008175), and Breast Neoplasms (D001943), to validate our predictions generated with the prostate cancer, lung cancer, and breast cancer datasets respectively.

This observation corresponded with predictions generated in silico with a kinetic model of yeast glycolysis, when the catalytic capacities of all glycolytic enzymes were set to share the same normalized temperature dependency.

These methods used a combination of predictions generated from FGENESH with Medicago truncatula parameters [ 22], Genscan with Arabidopsis thaliana parameters and GeneMark with Medicago truncatula [ 23] parameters.

We also compare biotransformation predictions generated by PROXIMAL with those generated by METEOR and Metaprint2D-react, two other prediction tools.

Thus, by coupling the functional predictions generated by FATHMM with domain-based ontological associations, as opposed to protein level annotations, we have developed a tool, which is capable of providing useful insights into the underlying mechanisms disrupted by AASs without any prior/background information on the protein itself.

In order to evaluate dynamic forecasts from VAR, we compared the performance of VAR forecasts with predictions generated by other procedures.