Sentence examples for most computational approaches from inspiring English sources

On the other hand, most computational approaches to identifying or predicting disease genes require a pre-defined set of "golden standard genes"[40].

Gene expression data were a primary source of information used by most computational approaches.

Since experimental discovery and validation of miRNAs is difficult, computational predictions are indispensable and today most computational approaches employ machine learning.

Still, most computational approaches just take pair-wise similarity, not interactions between genes, into account when inferring network from expression data.

Most computational approaches rely on the integration of several sources of heterogeneous data such as sequence features, gene expression data and protein protein interactions (PPIs).

Most computational approaches formally generate extreme currents due to the common practice of treating reversible reactions as two separate irreversible reactions [ 6],[ 7].

The most established computational approaches to function detection primarily depend on homology matching to genes with known functions utilizing programs such as FASTA [4] and PSI-BLAST [5].

Identification of the pharmacophoric features is one of the most important computational approaches in a rational drug design process.

Docking is one of the most popular computational approaches used for identifying potential binding sites and favorable ligand substrate poses.

The major events and their time scales are summarized in Figure 8. Atomistic MD simulations belong to the most informative computational approaches for studying bilayers.

Most such computational approaches use a nearest-neighbor model (39, 40) for predicting the stabilities of stems 1 and 2. This is coupled with various models for the free energy contribution from the loops (26, 41, 42).