A computer-based microarray experiment design-system for gene-regulation pathway discovery.
This paper reports the methods and evaluation of a computer-based system that recommends microarray experimental design for biologists - causal discovery in Gene Expression data using Expected Value of Experimentation (GEEVE). The GEEVE system uses causal Bayesian networks and generates a decision tree for recommendations. To evaluate the GEEVE system, we first built an expression simulation model based on a gene regulation model assessed by an expert biologist. Using the simulation model, we conducted a controlled study that involved 10 biologists, some of whom used GEEVE and some of whom did not. The results show that biologists who used GEEVE reached correct causal assessments about gene regulation more often than did those biologists who did not use GEEVE.
Yoo
Changwon
C
Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA.
Cooper
Gregory F
GF
D019540:Area Under Curve; D001499:Bayes Theorem; D003198:Computer Simulation; D003663:Decision Trees; D017353:Gene Deletion; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D008390:Markov Chains; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D017346:Protein Serine-Threonine Kinases / Q000235:genetics*; D012372:ROC Curve; D012441:Saccharomyces cerevisiae / Q000201:enzymology / Q000235:genetics
C071570:SNF1-related protein kinases; D017346:Protein Serine-Threonine Kinases