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In this model, gene expression dynamics are represented by a special set of paths, with each path characterizing a stochastic pattern.
Artificial neural networks were used to model gene expression (Vohradsky 2001).
Developing computational methods to compare, annotate, and model gene expression based on images is imperative, considering that available data are rapidly increasing.
Although our studies focused on prostate, we believe the methodology to model gene expression presented here has much broader application to optimize treatment plans in other solid tumor sites; this assertion should be confirmed experimentally.
In the model, gene expression both directs development and is controlled by the shape of developing teeth.
For each model, gene expression patterns associated with epidermis development and keratinization mirrored those of clinical psoriasis.
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Modelling gene expression features: Gene expression data are time-series data with several measurements spread across time or across different types of control experiments.
In the PBN modelling framework, gene expression is quantised to two levels: ON and OFF.
Dong X, Greven MC, Kundaje A, Djebali S, Brown JB, Cheng C, Gingeras TR, Gerstein M, Guigó R, Birney E, Weng Z. Modeling gene expression using chromatin features in various cellular contexts.
However, the guidelines for modeling gene expression with fuzzy logic are fairly open, and the need arises to investigate how adjustments in the modeling scheme will affect the results.
Furthermore, many methods have been proposed for constructing gene regulatory networks [10] [17], which are bases for modeling gene expression.
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