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.

Chitosan-modified dry powder formulations for pulmonary gene delivery

Spray-drying is an effective process for preparing micron-dimensioned particles for pulmonary delivery. Previously, we have demonstrated enhanced dispersibility and fine particle fraction of spray-dried nonviral gene delivery formulations using amino acids or absorption enhancers as dispersibility-enhancing excipients. In this study, we investigate the use of the cationic polymer chitosan as a readily available and biocompatible dispersibility enhancer. Lactose-lipid:polycation:pDNA (LPD) powders were prepared by spray-drying and post-mixed with chitosan or spray-dried chitosan. In addition, the water-soluble chitosan derivative, trimethyl chitosan, was added to the lactose-LPD formulation before spray-drying. Spray-dried chitosan particles, displaying an irregular surface morphology and diameter of less than 2 mu m, readily adsorbed to lactose-LPD particles following mixing. In contrast with the smooth spherical surface of lactose-LPD particles, spray-dried trimethyl chitosan-lactose-LPD particles demonstrated increased surface roughness and a unimodal particle size distribution (mean diameter 3.4 mu m), compared with the multimodal distribution for unmodified lactose-LPD powders (mean diameter 23.7 mu m). The emitted dose and in vitro deposition of chitosan-modified powders was significantly greater than that of unmodified powders. Moreover, the inclusion of chitosan mediated an enhanced level of reporter gene expression. In summary, chitosan enhances the dispersibility and in vitro pulmonary deposition performance of spray-dried powders.