Narrow Results

The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Many of the methods developed employ local search or heuristic strategies for identifying the "best" arrangement of features according to some metric. In this article, we present rigorous clustering methods based on the optimal re-ordering of data matrices. Distinct mixed-integer linear programming (MILP) models are utilized for the clustering of (a) dense data matrices, such as gene expression data, and (b) sparse data matrices, which are commonly encountered in the field of drug discovery. Both methods can be used in an iterative framework to bicluster data and assist in the synthesis of drug compounds, respectively. We demonstrate the capability of the proposed optimal re-ordering methods on several data sets from both systems biology and molecular discovery studies and compare our results to other clustering techniques when applicable.

Within the field of protein structure prediction, the packing of α-helical proteins has been one of the more difficult problems. The use of distance constraints and topology predictions is shown to be highly useful for reducing the conformational space that must be searched by deterministic algorithms to find a protein structure of minimum conformational energy. We present a novel first principles framework to predict the structure of α-helical proteins that includes three main stages. These stages include a novel optimization model for generating interhelical distance restraints between α-helices, the analysis and clustering of loop structures with flexible stems, and finally the prediction of tertiary structure using a hybrid optimization algorithm. This approach does not assume the form of the helices, so it is applicable to all α-helical proteins, including helices with kinks and irregular helices. The interhelical contact prediction model was evaluated on 5 proteins, where it identified an average contact distance below 10.0 Å for the entire set. The proposed overall framework was applied to a 63 residue α-helical bundle (1r69) with 5 helices.

The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Many of the methods developed employ local search or heuristic strategies for identifying the "best" arrangement of features according to some metric. In this article, we present rigorous clustering methods based on the optimal re-ordering of data matrices. Distinct mixed-integer linear programming (MILP) models are utilized for the clustering of (a) dense data matrices, such as gene expression data, and (b) sparse data matrices, which are commonly encountered in the field of drug discovery. Both methods can be used in an iterative framework to bicluster data and assist in the synthesis of drug compounds, respectively. We demonstrate the capability of the proposed optimal re-ordering methods on several data sets from both systems biology and molecular discovery studies and compare our results to other clustering techniques when applicable.