Sentence examples for more biologically interpretable from inspiring English sources

To infer more biologically interpretable results, Metascape applies a mature complex identification algorithm called MCODE33 to automatically extract protein complexes embedded in the large network.

We anticipate that a large amount of data will help us to extract more biologically interpretable sources that are beneficial in revealing structural networks related to the complex mechanisms of intelligence and cognition.

The iBAG model can help us eliminate these genes and obtain results that are more biologically interpretable.

We assume the joint genotype of a given position can be mapped onto the more biologically interpretable set of marginal genotypes according to Table 1.

By organizing genes into co-regulated clusters prior to statistical testing, the problem becomes more computationally feasible and the resulting output more biologically interpretable.

The advantage of the iBAG unified model is that it can identify the genes with effects modulated by methylation, and thus the results are more biologically interpretable.

Those 11 bioclimatic layers were winnowed from a larger set of 19 variables using correlations to estimate redundancy between variables and retaining the more biologically meaningful and interpretable variables (e.g., annual mean temperature, mean temperature of the wettest quarter, and precipitation of the wettest quarter).

These values are more biologically informative and interpretable than simple differences or ratios because they are no longer confounded by the effects of initial DNA methylation values and CpG density.

Compared to existing graph methods, Markov cluster (MCL) algorithm [ 20] and affinity propagation (AP) [ 21], Triple-Link can decompose the SCCM more efficiently and results in biologically interpretable TF clusters.

However, the predictive models of most previous methods are not biologically interpretable, which makes it difficult to interpret biological features of drug-target interactions or compound-protein interactions.

The data mining and functional interpretation of datasets to access biologically interpretable results pose many analytical challenges, which have been recently reviewed by Kumar and Mann (2009).