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Abstract. Sequential mining is the process of applying data mining techniques to a sequential database for the purposes of discovering the correlation relationships that exist among an ordered list of events. An important application of sequential mining techniques is web usage mining, for mining web log accesses, where the sequences of web page accesses made by different web users over a period of time, through a server, are recorded. Web access pattern tree (WAP-tree) mining is a sequential pattern mining technique for web log access sequences, which first stores the original web access sequence database on a prefix tree, similar to the frequent pattern tree (FP-tree) for storing non-sequential data. WAP-tree algorithm then, mines the frequent sequences from the WAP-tree by recursively re-constructing intermediate trees, starting with suffix sequences and ending with prefix sequences. This paper proposes a more efficient approach for using the WAP-tree to mine frequent sequences, which totally eliminates the need to engage in numerous re-construction of intermediate WAP-trees during mining. The proposed algorithm builds the frequent header node links of the original WAP-tree in a pre-order fashion and uses the position code of each node to identify the ancestor/descendant relationships between nodes of the tree. It then, finds each frequent sequential pattern, through progressive prefix sequence search, starting with its first prefix subsequence event. Experiments show huge performance gain over the WAP-tree technique.

An important goal of software development in the medical field is the design of methods which are able to integrate information obtained from various imaging and nonimaging modalities into a cohesive framework in order to understand the results of qualitatively different measurements in a larger context. Moreover, it is essential to assess the various features of the data quantitatively so that relationships in anatomical and functional domains between complementing modalities can be expressed mathematically. This paper presents a clinically feasible software environment for the quantitative assessment of the relationship among biochemical functions as assessed by PET imaging and electrophysiological parameters derived from intracranial EEG. Based on the developed software tools, quantitative results obtained from individual modalities can be merged into a data structure allowing a consistent framework for advanced data mining techniques and 3D visualization. Moreover, an effort was made to derive quantitative variables (such as the spatial proximity index, SPI) characterizing the relationship between complementing modalities on a more generic level as a prerequisite for efficient data mining strategies. We describe the implementation of this software environment in twelve children (mean age 5.2 ± 4.3 years) with medically intractable partial epilepsy who underwent both high-resolution structural MR and functional PET imaging. Our experiments demonstrate that our approach will lead to a better understanding of the mechanisms of epileptogenesis and might ultimately have an impact on treatment. Moreover, our software environment holds promise to be useful in many other neurological disorders, where integration of multimodality data is crucial for a better understanding of the underlying disease mechanisms.

PURPOSE Hypoxia-inducible factor (HIF) controls the expression of genes in response to hypoxia, as well as a wide range of other cellular processes. We previously showed constitutive stabilization of HIF-1alpha in the majority of patients with diffuse large B-cell lymphoma (DLBCL). To our knowledge, the prognostic significance of HIF in lymphoma has never been investigated. PATIENTS AND METHODS We studied the immunohistochemical protein expression of HIF-1alpha on tissue microarrays from 153 patients with DLBCL treated in sequential cohorts with cyclophosphamide, doxorubicin, oncovin, and prednisone (CHOP) or rituximab-CHOP (R-CHOP) from 1999 to 2002. Results were correlated with patient outcome. Results Median follow-up for all patients was 80 months. Among all patients, HIF-1alpha was expressed in 62% of germinal center and 59% of non-germinal center patients. With HIF-1alpha analyzed as a dependent variable, there were no survival differences in CHOP-treated patients. In the R-CHOP group, however, HIF-1alpha protein expression correlated with significantly improved progression-free survival (PFS) and overall survival (OS). Five-year PFS for HIF-1alpha-positive patients was 71% v 43% for HIF-1alpha-negative patients (P = .0187), whereas 5-year OS was 75% and 54%, respectively (P = .025). In multivariate analysis with International Prognostic Index criteria, HIF-1alpha remained a significant predictor for PFS (P = .026) and OS (P = .043). Compared with other biomarkers, HIF-1alpha correlated only with BCL6 (P = .004). In terms of gene expression, we found several common gene associations of HIF-1alpha and the stromal-1 signature with genes predominantly involved in regulation of the extracellular matrix (eg, BGN, COL1A2, COL5A1, and PLOD2). CONCLUSION The expression of HIF-1alpha protein is an important independent favorable prognostic factor for survival in patients with DLBCL treated with R-CHOP.

Background: Microarray techniques have revolutionized genomic research by making it possible to monitor the expression of thousands of genes in parallel. As the amount of microarray data being produced is increasing at an exponential rate, there is a great demand for efficient and effective expression data analysis tools. Comparison of gene expression profiles of patients against those of normal counterpart people will enhance our understanding of a disease and identify leads for therapeutic intervention.

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