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.

Assisting developing countries toward food self-reliance

Per capita food availability in the developing world has increased by 20% since the early 1960s, according to the Food and Agriculture Organization, and today the world has twice as many people but 150 million fewer hungry people than in 1960. The world agricultural system has not done too bad a job over the past 35 years. It is likely that global agricultural production will continue to at least match growth in food demand over the next decade, assuming no major weather anomalies. Continued support of the Consultative Group for International Agricultural Research and programs involving U.S. universities is important to sharing knowledge about agriculture with colleagues in the developing world. This paper explores the reasons for providing agricultural development assistance, the benefits to the United States that come from doing so, and the special challenges facing the world over the next few decades.