Sentence examples for effective inference from inspiring English sources

We leveraged a technique called the network representation learning to embed a gene network into a compact and robust feature space, and then designed a hierarchical statistical model to integrate features of multiple gene networks with GWAS data for the effective inference of genes associated with a disease of interest.

The linear programming approach we have developed can serve as an effective inference tool in its own right, and can be an efficient first step in a more-intensive phylogenomic analysis.

As estimation is most important when the therapeutic regimen has been considered as effective, inference may be more common when the phase II trial proceeded to the second stage as compared to cases where it was stopped for futility at the first stage.

The case-control design typically oversamples cases for more cost-effective and statistically efficient inference.

We propose a cost-effective outcome-dependent sampling design for the failure time data and develop an efficient inference procedure for data collected with this design.

We outline the types of automation that would be enabled by effective lightweight inference capabilities and survey some promising approaches to realizing the needed capabilities.

Any effective GRN inference method has to cope well with the large number of genes and the small number of time points that characterize microarray datasets.

We repeated this analysis with the PANDA network and found similar results (Fig.  4b), showing that the combined score is beneficial for any effective network inference algorithm.

To achieve this objective, traditional statistical methods, such as principal component analysis (PCA) [ 2- 8], the focus of this article, are being retrofitted to provide effective statistical inference in this challenging context of microarray data analysis.

The k-NN algorithm is the simplest among those used in machine learning and can determine the attribute of a query point by taking the weighted average of the k-NN to the point, and as such is a highly effective inductive inference method [ 39].

This kind of information leads to high-dimensional and low-sample size (HDLSS) data sets (i.e., p ≫ n where p and n are the number of covariates and patients) which pose tremendous challenges to effective statistical inference especially for the time-to-event due to the presence of censoring and the use of much more complicated models.