Decision support based on genomics:integration of data-and knowledge-driven reasoning

The breadth and depth of available clinico-genomic information, present an enormous opportunity for improving our ability to study disease mechanisms and meet the individualised medicine needs. A difficulty occurs when the results are to be transferred 'from bench to bedside'. Diversity of methods is one of the causes, but the most critical one relates to our inability to share and jointly exploit data and tools. This paper presents a perspective on current state-of-the-art in the analysis of clinico-genomic data and its relevance to medical decision support. It is an attempt to investigate the issues related to data and knowledge integration. Copyright © 2010 Inderscience Enterprises Ltd.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Genomic evaluation during permeability of indomethacin and its solid dispersion

Drug resistance was first identified in cancer cells that express proteins known as multidrug resistance proteins that extrude the therapeutic agents out of the cells resulting in alteration of pharmacokinetics, tissue distribution, and pharmacodynamics of drugs. To this end studies were carried out to investigate the role of pharmacological inhibitors and pharmaceutical excipients with a primary focus on P-glycoprotein (P-gp). The aim of this study was to investigate holistic changes in transporter gene expression during permeability upon formulation of indomethacin as solid dispersion. Initial characterization studies of solid dispersion of indomethacin showed that the drug was dispersed within the carrier in amorphous form. Analysis of permeability data across Caco-2 monolayers revealed that drug absorption increased by 4-fold when reformulated as solid dispersion. The last phase of the work involved investigation of gene expression changes of transporter genes during permeability. The results showed that there were significant differences in the expression of both ATP-binding cassette (ABC) transporter genes as well as solute carrier transporter (SLC) genes suggesting that the inclusion of polyethylene glycol as well as changes in molecular form of drug from crystalline to amorphous have a significant bearing on the expression of transporter network genes resulting in differences in drug permeability. © 2011 Informa UK, Ltd.