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Multiple computational algorithms have been devised to address gaps in metabolic network models in an automated manner [ 26].
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Using multiple computational target predicting algorithms together with the luciferase-based reporting platform, the interactions between HF-related microRNAs and the 3′ untranslated regions (3′UTRs) of neurohormone associated genes were examined and compared.
Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal.
Furthermore, computational algorithms and multiple confirmatory assay formats combined with high-throughput screening methodologies greatly contribute to the identification and characterization of novel potent drug targets [ 67].
Parallel computing algorithms adopted in the framework to solve multiple computational problems simultaneously are also summarized in the paper.
The proposed algorithms can be applied to multiple computational platforms and PEFC and related model architectures.
Using two computational algorithms, four copy-number variants (CNVs) were identified in multiple cases that were absent in study controls and markedly less frequent in ∼1500 population-based controls.
The result revealed new formulas and computational algorithms that are suitable for simulating the vibration of the string against multiple stoppers.
These developed computational algorithms and systems biology strategies will enhance the knowledge discovery and hypothesis-driven analysis of multiple next generation sequencing data, for t-AML and other complex diseases.
Given the limitations and the relatively low-throughput of the PEM strategy, computational algorithms have been developed that benefit from the interplay between structural and nucleotide variation to predict inversions in multiple individuals and/or estimate inversion genotypes [ 40 45].
The same is true for researchers working with computational algorithms.
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