Can we use linear Gaussian networks to model dynamic interactions among genes? Results from a simulation study

2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006

Volumn , Issue , 2006, Pages 13-14

  Ferrazzi, Fulvia ^a,b   Amici, Roberta ^a   Sebastiani, Paola ^c   Kohane, Isaac S ^b   Ramoni, Marco F ^b   Bellazzi, Riccardo ^a  

^a UNIVERSITY OF PAVIA   (Italy)

^b HARVARD MEDICAL SCHOOL   (United States)

^c BOSTON UNIVERSITY SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; CONTROL THEORY; EIGENVALUES AND EIGENFUNCTIONS; ELECTRON BEAM LITHOGRAPHY; FLOW INTERACTIONS; GENES; INFERENCE ENGINES; KNOWLEDGE BASED SYSTEMS; MILITARY DATA PROCESSING; ROBUST CONTROL; SIGNAL PROCESSING; THROUGHPUT; TRELLIS CODES;

CELL CYCLE CONTROL; CELLULAR MECHANISMS; DISCRETIZATION; DYNAMIC BAYESIAN NETWORK (DBN); DYNAMIC DATA; DYNAMIC INTERACTIONS; GAUSSIAN NETWORKS; GENOMIC SIGNAL PROCESSING; HIGH-THROUGHPUT (HT); INFORMATION LOSSES; INTERNATIONAL (CO); LEARNING PROCESSES; LINEAR MODELING; MODELING TOOLS; SIMULATED DATA; SIMULATION STUDIES;

MATHEMATICAL MODELS;

EID: 34547219811     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/GENSIPS.2006.353132     Document Type: Conference Paper

References (6)

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6. G. F. Cooper and E. Herskovitz, "A Bayesian method for the induction of probabilistic networks from data," Machine Learning, vol. 9, pp. 309-347, 1992.