Data mining has in recent years emerged as an interesting area in the boundary between algorithms, probabilistic modeling, statistics, and databases. Data mining research can be divided into global approaches, which try to model the whole data, and local methods, which try to find useful patterns occurring in the data. We discuss briefly some simple local and global techniques, review two attempts at combining the approaches, and list open problems with an algorithmic flavor.

Abstract. To date, the majority of Web search engines have provided simple keyword search interfaces that present the results as a ranked list of hyperlinks. More recently researchers have been investigating interactive, graphical and multimedia approaches which use ontologies to model the knowledge space. Such systems use the semantic relationships to structure the assimilated search results into interactive semantic graphs or hypermedia presentations which enable the user to quickly and easily explore the results and detect previously unrecognized associations. More recently, the proliferation of eResearch communities has led to a demand for search interfaces which automate the discovery, analysis and assimilation of multiple information sources in order to prove or disprove a particular scientific theory or hypothesis. We believe that such semiautomated analysis, assimilation and hypothesis-driven approaches represent the next generation of search engines. In this paper we describe and evaluate such a search interface which we have developed for a particular eScience application.

There is a critical need for data-mining methods that can identify SNPs that predict among-individual variation in a phenotype of interest and reverse-engineer the biological network of relationships between SNPs, phenotypes, and other factors. This problem is both challenging and important in light of the large number of SNPs in many genes of interest and across the human genome. A potentially fruitful form of exploratory data analysis is the Bayesian or Belief network. A Bayesian or Belief network provides an analytic approach for identifying robust predictors of among-individual variation in a disease endpoints or risk factor levels. We have applied Belief networks to SNP variation in the human APOE gene and plasma apolipoprotein E levels from two samples: 702 African-Americans from Jackson, MS, and 854 non-Hispanic whites from Rochester, MN. Twenty variable sites in the APOE gene were genotyped in both samples. In Jackson, MS, SNPs 4036 and