title: Sparse factorizations of gene expression data guided by binding data.
abstract: Existing clustering methods do not deal well with overlapping clusters, are unstable and do not take into account the robustness of biological systems, or more complex background knowledge such as regulator binding data. Here we describe a nonnegative sparse factorization algorithm dealing with the above problems: cluster overlaps are allowed by design, the nonnegativity constraints implicitly approximate the robustness of biological systems and regulator binding data is used to guide the factorization. Preliminary results show the feasibility of our approach.
authors.affiliation: AI Lab, National Institute for Research and Development in Informatics, Bucharest, Romania. badea@ici.ro
authors.forename: Liviu
authors.initials: L
authors.lastname: Badea
authors.forename: Doina
authors.initials: D
authors.lastname: Tilivea
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D004271:DNA, Fungal / Q000235:genetics; D030561:Databases, Nucleic Acid; D005656:Fungal Proteins / Q000235:genetics; D015870:Gene Expression; D005800:Genes, Fungal; D005815:Genetic Code; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics*
chemical_list: D004271:DNA, Fungal; D005656:Fungal Proteins