Sentence examples for software inference from inspiring English sources

A software inference algorithm that uses primary care Big Data can accurately classify the content of clinical consultations.

The software inference algorithm (Fig.  2) is a more detailed implementation of the clinical algorithm (Fig.  1) which divides clinical decisions into distinct data level operations.

This study developed and tested a natural language processing software inference algorithm to classify the content of clinical consultations using primary care Big Data.

A natural language processing software inference algorithm that analyses the content of clinical consultation records, diagnostic classifications and prescription information, is able to classify child-GP consultations related to respiratory conditions with similar accuracy to clinical experts.

To develop a natural language processing software inference algorithm to classify the content of primary care consultations using electronic health record Big Data and subsequently test the algorithm's ability to estimate the prevalence and burden of childhood respiratory illness in primary care.

Phylogenetic relationships were analyzed using the Philip 3.67 software (Phylogeny Inference Package, Version 3.67) and the neighbor joining program with 1000 bootstrap replicates.

The consensus tree of the bootstrap in the ML method was integrated using Phylip software (Phylogeny Inference Package v3.695, http://evolution.genetics.washington.edu/phylip.html).edu/phylip.html

Although +TIPs mark only phases of MT growth, the plusTipTracker software allows inference of additional MT dynamics, including phases of pause and shrinkage, by linking collinear, sequential growth tracks.

We present a new method and software for inference of haplotype phase and missing data that can accurately phase data from whole-genome association studies, and we present the first comparison of haplotype-inference methods for real and simulated data sets with thousands of genotyped individuals.

Gelman [9], [10] instead uses WinBUGS [28], [29], [30], one of the family of BUGS software (Bayesian inference Using Gibbs Sampling).

The proposed Bayesian method is implemented in WinBUGS software and inferences are compared to those drawn from a naive analysis, which ignores measurement errors in the exposures.