A molecular structure matching approach to efficient identification of endogenous mammalian biochemical structures

Metabolomics is the study of small molecules, called metabolites, of a cell, tissue or organism. It is of particular interest as endogenous metabolites represent the phenotype resulting from gene expression. A major challenge in metabolomics research is the structural identification of unknown biochemical compounds in complex biofluids. In this paper we present an efficient cheminformatics tool, BioSMXpress that uses known endogenous mammalian biochemicals and graph matching methods to identify endogenous mammalian biochemical structures in chemical structure space. The results of a comprehensive set of empirical experiments suggest that BioSMXpress identifies endogenous mammalian biochemical structures with high accuracy. BioSMXpress is 8 times faster than our previous work BioSM without compromising the accuracy of the predictions made. BioSMXpress is freely available at http://engr.uconn.edu/~rajasek/BioSMXpress.zip

Integrating Clonal Selection and Deterministic Sampling for Efficient Associative Classification

Traditional Associative Classification (AC) algorithms typically search for all possible association rules to find a representative subset of those rules. Since the search space of such rules may grow exponentially as the support threshold decreases, the rules discovery process can be computationally expensive. One effective way to tackle this problem is to directly find a set of high-stakes association rules that potentially builds a highly accurate classifier. This paper introduces AC-CS, an AC algorithm that integrates the clonal selection of the immune system along with deterministic data sampling. Upon picking a representative sample of the original data, it proceeds in an evolutionary fashion to populate only rules that are likely to yield good classification accuracy. Empirical results on several real datasets show that the approach generates dramatically less rules than traditional AC algorithms. In addition, the proposed approach is significantly more efficient than traditional AC algorithms while achieving a competitive accuracy.

Disposable screen-printed sensors for determination of duloxetine hydrochloride

A screen-printed disposable electrode system for the determination of duloxetine hydrochloride (DL) was developed using screen-printing technology. Homemade printing has been characterized and optimized on the basis of effects of the modifier and plasticizers. The fabricated bi-electrode potentiometric strip containing both working and reference electrodes was used as duloxetine hydrochloride sensor. The proposed sensors worked satisfactorily in the concentration range from 1.0 × 10-6-1.0 × 10-2 mol L-1 with detection limit reaching 5.0 × 10-7 mol L-1 and adequate shelf life of 6 months. The method is accurate, precise and economical. The proposed method has been applied successfully for the analysis of the drug in pure and in its dosage forms. In this method, there is no interference from any common pharmaceutical additives and diluents. Results of the analysis were validated statistically by recovery studies.

Improved algorithms for parsing ESLTAGs: a grammatical model suitable for RNA pseudoknots

Formal grammars have been employed in biology to solve various important problems. In particular, grammars have been used to model and predict RNA structures. Two such grammars are Simple Linear Tree Adjoining Grammars (SLTAGs) and Extended SLTAGs (ESLTAGs). Performances of techniques that employ grammatical formalisms critically depend on the efficiency of the underlying parsing algorithms. In this paper, we present efficient algorithms for parsing SLTAGs and ESLTAGs. Our algorithm for SLTAGs parsing takes O(min{m,n⁴}) time and O(min{m,n⁴}) space, where m is the number of entries that will ever be made in the matrix M (that is normally used by TAG parsing algorithms). Our algorithm for ESLTAGs parsing takes O(min{m,n⁴}) time and O(min{m,n⁴}) space. We show that these algorithms perform better, in practice, than the algorithms of Uemura et al.