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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE/ACM transactions on computational biology and bioinformatics Jg. 7; H. 1; S. 118 - 125
Hauptverfasser:	Perkins, T.J., Hallett, M.T.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2010 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!