J
John MacCuish
Researcher at University of New Mexico
Publications - 11
Citations - 208
John MacCuish is an academic researcher from University of New Mexico. The author has contributed to research in topics: Cluster analysis & Hierarchical clustering. The author has an hindex of 6, co-authored 11 publications receiving 195 citations.
Papers
More filters
Journal ArticleDOI
Ties in proximity and clustering compounds.
TL;DR: This work explores the ties in proximity problem, using a number of chemical collections with varying degrees of diversity, given several common similarity measures and clustering algorithms and shows that this problem is significant for relatively small compound sets.
Journal ArticleDOI
Variable selection and model validation of 2D and 3D molecular descriptors
TL;DR: This paper shows the use of molecular shape and electrostatics in a form of similarity searching with respect to a crystal structure of a known bound ligand in quantitative structure–activity relationships (QSAR) analysis.
Journal ArticleDOI
Chemoinformatics applications of cluster analysis
John MacCuish,Norah E. MacCuish +1 more
TL;DR: Clustering techniques, such as coclustering or self‐organizing trees, commonly found in bioinformatics, are beginning to find chemoinformatic application uses and new validation techniques have been introduced in the chemoinformatics literature that now allow for both a better understanding of the clustering results and help point to methods of greater efficacy.
Interactive layout mechanisms for image database retrieval
TL;DR: A user interface for image retrieval using query- by-example technology, CANDID Camera, and several new layout algorithms based on multidimensional scaling techniques that visually display global and local relationships between images within a large image database are presented.
Book
Clustering in Bioinformatics and Drug Discovery
John MacCuish,Norah E. MacCuish +1 more
TL;DR: This chapter discusses K-Means and Variants, a meta-modelling framework for solving the challenge of integrating self-Organizing Hybrids and Hierarchical Mixture Models into a single data type.