Rensselaer Polytechnic Institute

About: Rensselaer Polytechnic Institute is a education organization based out in Troy, New York, United States. It is known for research contribution in the topics: Terahertz radiation & Population. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.

OligoArray 2.0: design of oligonucleotide probes for DNA microarrays using a thermodynamic approach

Abstract: There is a substantial interest in implementing bioinformatics technologies that allow the design of oligonucleotides to support the development of microarrays made from short synthetic DNA fragments spotted or in situ synthesized on slides. Ideally, such oligonucleotides should be totally specific to their respective targets to avoid any cross-hybridization and should not form stable secondary structures that may interfere with the labeled probes during hybridization. We have developed OligoArray 2.0, a program that designs specific oligonucleotides at the genomic scale. It uses a thermodynamic approach to predict secondary structures and to calculate the specificity of targets on chips for a unique probe in a mixture of labeled probes. Furthermore, OligoArray 2.0 can adjust the oligonucleotide length, according to user input, to fit a narrow T(m) range compatible with hybridization requirements. Combined with on chip oligonucleotide synthesis, this program makes it feasible to perform expression analysis on a genomic scale for any organism for which the genome sequence is known. This is without relying on cDNA or oligonucleotide libraries. OligoArray 2.0 was used to design 75 764 oligonucleotides representing 26 140 transcripts from Arabidopsis thaliana. Among this set, we provide at least one specific oligonucleotide for 93% of these transcripts.

Multicategory Classification by Support Vector Machines

Abstract: We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how two-class discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic programming (QP) approaches based on Vapnik‘s Support Vector Machine (SVM) can be combined to yield two new approaches to the multiclass problem. In LP multiclass discrimination, a single linear program is used to construct a piecewise-linear classification function. In our proposed multiclass SVM method, a single quadratic program is used to construct a piecewise-nonlinear classification function. Each piece of this function can take the form of a polynomial, a radial basis function, or even a neural network. For the k > 2-class problems, the SVM method as originally proposed required the construction of a two-class SVM to separate each class from the remaining classes. Similarily, k two-class linear programs can be used for the multiclass problem. We performed an empirical study of the original LP method, the proposed k LP method, the proposed single QP method and the original k QP methods. We discuss the advantages and disadvantages of each approach.