title: Multi-view spectral clustering and its chemical application.
abstract: Clustering is an unsupervised method that allows researchers to group objects and gather information about their relationships. In chemoinformatics, clustering enables hypotheses to be drawn about a compound's biological, chemical and physical property in comparison to another. We introduce a novel improved spectral clustering algorithm, proposed for chemical compound clustering, using multiple data sources. Tensor-based spectral methods, used in this paper, provide chemically appropriate and statistically significant results when attempting to cluster compounds from both the GSK-Chembl Malaria data set and the Zinc database. Spectral clustering algorithms based on the tensor method give robust results on the mid-size compound sets used here. The goal of this paper is to present the clustering of chemical compounds, using a tensor-based multi-view method which proves of value to the medicinal chemistry community. Our findings show compounds of extremely different chemotypes clustering together, this is a hint to the chemogenomics nature of our method.
authors: Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven, Belgium. tunde.adefioye@esat.kuleuven.be
authors: Adeshola A
authors: AA
authors: Adefioye
authors: Xinhai
authors: X
authors: Liu
authors: Bart
authors: B
authors: De Moor
mesh_terms: D000465:Algorithms; D000962:Antimalarials / Q000737:chemistry / Q000494:pharmacology; D002626:Chemistry, Pharmaceutical / Q000379:methods*; D016000:Cluster Analysis; D016208:Databases, Factual; D023281:Genomics / Q000379:methods*; D006801:Humans; D008956:Models, Chemical; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000145:classification*; D015203:Reproducibility of Results
chemical_list: D000962:Antimalarials; D004364:Pharmaceutical Preparations