LLM3D: A log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data

Nucleic Acids Research

Volumn 39, Issue 13, 2011, Pages 5313-5327

  Geeven, Geert ^a   MacGillavry, Harold D ^a,c   Eggers, Ruben ^b   Sassen, Marion M ^a   Verhaagen, Joost ^a,b   Smit, August B ^a   De Gunst, Mathisca C M ^a   Van Kesteren, Ronald E ^a  

^a VU UNIVERSITY AMSTERDAM   (Netherlands)

^b NETHERLANDS INSTITUTE FOR NEUROSCIENCE   (Netherlands)

^c UNIVERSITY OF MARYLAND SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PEROXISOME PROLIFERATOR ACTIVATED RECEPTOR GAMMA; TRANSCRIPTION FACTOR;

ANIMAL CELL; ANIMAL EXPERIMENT; ANIMAL MODEL; ANIMAL TISSUE; ARTICLE; BINDING SITE; CELL RENEWAL; CONTROLLED STUDY; EMBRYONIC STEM CELL; GENE EXPRESSION; GENE FUNCTION; GENE INTERACTION; GENE REGULATORY INTERACTION; GENETIC TRANSCRIPTION; IN VIVO STUDY; LOGLINEAR MODEL; METABOLIC REGULATION; NERVE FIBER GROWTH; NERVE FIBER REGENERATION; NERVE INJURY; NONHUMAN; NUCLEOTIDE SEQUENCE; PREDICTION; PRIORITY JOURNAL; RAT; STATISTICAL ANALYSIS; YEAST;

ANIMALS; BINDING SITES; CELL LINE; EMBRYONIC STEM CELLS; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; GENOME; LINEAR MODELS; MICE; NERVE REGENERATION; NEURONS; PPAR GAMMA; RATS; RATS, WISTAR; TRANSCRIPTION FACTORS; YEASTS;

MAMMALIA;

EID: 80052214201     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkr139     Document Type: Article

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