A Trade-Off between Sample Complexity and Computational Complexity in Learning Boolean Networks from Time-Series Data

A key problem in molecular biology is to infer regulatory relationships between genes from expression data. This paper studies a simplified model of such inference problems in which one or more Boolean variables, modeling, for example, the expression levels of genes, each depend deterministically on...

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Vydané v:	IEEE/ACM transactions on computational biology and bioinformatics Ročník 7; číslo 1; s. 118 - 125
Hlavní autori:	Perkins, T.J., Hallett, M.T.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2010 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Artificial Intelligence association rules Biological system modeling Boolean functions classification clustering Computational biology Computational complexity Computer Simulation feature extraction or construction Gene expression Gene Expression Profiling - methods Genetics Inference algorithms Input variables Logistic Models Machine learning Models, Biological Organisms parameter learning Proteome - metabolism Signal Processing, Computer-Assisted Signal Transduction - physiology Studies Time Factors Time series analysis Parameter learning Clustering classification and association rules Machine learning Feature extraction or construction
ISSN:	1545-5963, 1557-9964, 1557-9964
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