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 Learning Causal Networks from Gene Expression Data. A Probabilistic Time Series Model for Gene Regulatory Relationships and Learning the Model from Gene Expression Data
Наименование:

Learning Causal Networks from Gene Expression Data. A Probabilistic Time Series Model for Gene Regulatory Relationships and Learning the Model from Gene Expression Data

автор Nasir A. издательство Книга по Требованию Математический анализ. Кратные и криволинейные интегралы. Задачи, решения
Cтраниц 172        мягкая обложка       
Аннотация:The model incorporates both probabilistic and temporal aspects, but is kept deliberately simple to make it amenable for learning from the gene expression data of microarray experiments. In this work we present a new model for identifying dependencies within a gene regulatory cycle. We have validated the learning algorithm on simulated data, and carried out experiments on real microarray data. In doing so, we have discovered novel, yet plausible, biological relationships. A key simplifying feature in our model is the use of a compression function for collapsing multiple causes of gene expression into a single cause. This allows us to introduce a learning algorithm which avoids the over-fitting tendencies of models with many parameters.
Автор: Nasir A.
Издательство: Книга по Требованию
Год:2009
Цена:
2783 руб
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Поиск: Learning Causal Networks from Gene Expression Data. A Probabilistic Time Series Model for Gene Regulatory Relationships and Learning the Model from Gene Expression Data
НОВЫЙ ГОД СИМВОЛ 2013 ГОДАУКРАШЕНИЯ
КАРНАВАЛ НА НОВЫЙ ГОД РАСПРОДАЖА
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