Sentence examples for most existing computational from inspiring English sources
However, when simulations are conducted on models with sufficient biophysical details, great challenges arise from limited computer power, thereby restricting most existing computational works with biophysical models only to small-scale networks.
However, most existing computational models are designed for detecting saliency in visible images or videos.
Most existing computational visual saliency models follow a bottom-up framework that generates independent saliency map in each selected visual feature space and combines them in a proper way.
Despite the high degree of variability in the length of MHC-II binding peptides, most existing computational methods for predicting MHC-II binding peptides focus on identifying a 9-mer core peptide.
Most existing computational methods were applied on static PPI networks to identify the protein complex.
Most existing computational approaches for studying differential gene expression in microarray datasets involve clustering algorithms designed to group genes with similar expression profiles, with the goal of identifying potential annotations for unknown genes [ 10- 17].
Most existing microscale computational fluid dynamics (CFD) models and mesoscale meteorological models cannot consider multi-scale urban wind flows, as neither can completely take into account the mesoscale and microscale physics, and their interactions.
Most of the existing computational approaches predict the pathogenic effect using statistical methods, machine learning techniques, or protein evolution models, based on features such as sequence homology, biochemical properties, and structural information (hydrogen-bond network, solvent accessibility, and so on).
Most of the existing computational studies that consider drug susceptibility prediction against a panel of drugs formulate a separate learning problem for each drug, which cannot make use of commonalities between subsets of drugs.
Most of the existing computational tools for subcellular localization predictions are designed for the general subcellular localization prediction, and their predictions for Golgi-resident proteins are less than adequate (Sprenger et al., 2006).
However, most existing algorithms cannot achieve computational efficiency and accuracy simultaneously.
