title: Estimating gene function with least squares nonnegative matrix factorization.
abstract: Nonnegative matrix factorization is a machine learning algorithm that has extracted information from data in a number of fields, including imaging and spectral analysis, text mining, and microarray data analysis. One limitation with the method for linking genes through microarray data in order to estimate gene function is the high variance observed in transcription levels between different genes. Least squares nonnegative matrix factorization uses estimates of the uncertainties on the mRNA levels for each gene in each condition, to guide the algorithm to a local minimum in normalized chi2, rather than a Euclidean distance or divergence between the reconstructed data and the data itself. Herein, application of this method to microarray data is demonstrated in order to predict gene function.
authors.affiliation: Fox Chase Cancer Center, Philadelphia, PA, USA.
authors.forename: Guoli
authors.initials: G
authors.lastname: Wang
authors.forename: Michael F
authors.initials: MF
authors.lastname: Ochs
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D005821:Genetic Techniques / Q000706:statistics & numerical data*; D006801:Humans; D016018:Least-Squares Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data; D014584:User-Computer Interface