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In order to determine the presence/absence of a gene, we developed a threshold based algorithm to identify absent genes for each test strain based on the probe hybridization calls.
In this paper, we have proposed an improved logistic regression based algorithm to identify disease genes by using either a single network or multiple networks.
The identification of pneumonia and influenza cases involved two steps: 1) identifying CUIs relating to pneumonia and influenza and 2) use of the CUIs to create a rules based algorithm to identify cases.
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Finally, we assessed the enrichment of protein on AuNP surface and used graph-theory based algorithms to identify cancer-specific hub nodes in the biological networks.
Since our focus is on solving degenerate and long motifs, we adopt the enumerative approach that is guaranteed to find the optimal motif by applying a novel rule-based algorithm to identify all optimal motif candidates without the expense of exploring the entire 4l space exhaustively.
They used the EyeLink I tracker and used a velocity-based algorithm to identify fixations.
More recently, Klingelhoefer et al. [ 13] used a stochastic logistic regression-based algorithm to identify relevant features associated with siRNA potency.
In this study, we focus on developing a graph-based algorithm to identify common and frequent network motifs from PPI networks of nine different cancers.
Ideker et al 27 have developed a simulated annealing-based algorithm to identify differentially activated networks by utilizing differentially expressed genes and protein interaction networks.
We propose a new fully automated velocity-based algorithm to identify fixations from eye-movement records of both eyes, with individual-specific thresholds.
The gain in PPV by using a claims-based algorithm to identify individuals with LDL cholesterol ≥70 mg/dL may not outweigh the potential loss in representativeness as compared with all statin users at high risk for CHD events.
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