Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Chloroplast DNA evidence of colonization, adaptive radiation, and hybridization in the evolution of the Macaronesian flora.

Most evolutionary studies of oceanic islands have focused on the Pacific Ocean. There are very few examples from the Atlantic archipelagos, especially Macaronesia, despite their unusual combination of features, including a close proximity to the continent, a broad range of geological ages, and a biota linked to a source area that existed in the Mediterranean basin before the late Tertiary. A chloroplast DNA (cpDNA) restriction site analysis of Argyranthemum (Asteraceae: Anthemideae), the largest endemic genus of plants of any volcanic archipelago in the Atlantic Ocean, was performed to examine patterns of plant evolution in Macaronesia. cpDNA data indicated that Argyranthemum is a monophyletic group that has speciated recently. The cpDNA tree showed a weak correlation with the current sectional classification and insular distribution. Two major cpDNA lineages were identified. One was restricted to northern archipelagos--e.g., Madeira, Desertas, and Selvagens--and the second comprised taxa endemic to the southern archipelago--e.g., the Canary Islands. The two major radiations identified in the Canaries are correlated with distinct ecological habitats; one is restricted to ecological zones under the influence of the northeastern trade winds and the other to regions that are not affected by these winds. The patterns of phylogenetic relationships in Argyranthemum indicate that interisland colonization between similar ecological zones is the main mechanism for establishing founder populations. This phenomenon, combined with rapid radiation into distinct ecological zones and interspecific hybridization, is the primary explanation for species diversification.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.