Pattern recognition: human and mechanical
Pattern recognition: human and mechanical
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
A survey of logical models for OLAP databases
ACM SIGMOD Record
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Data Mining in the Bioinformatics Domain
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Parallel data intensive computing in scientific and commercial applications
Parallel Computing - Parallel data-intensive algorithms and applications
Scientific OLAP for the Biotech Domain
Proceedings of the 27th International Conference on Very Large Data Bases
Analytical processing of XML documents: opportunities and challenges
ACM SIGMOD Record
Multimedia data warehouses: a multiversion model and a medical application
Multimedia Tools and Applications
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Biotech companies routinely generate vast amounts of biological measurement data that must be analyzed rapidly and mined for diagnostic, prognostic, or drug evaluation purposes. While these data analysis tasks are critical to their success, they have not benefited from recent advances that emerged from database and KDD research. In this paper, we focus on two such tasks: on-line analysis of clinical study data, and mining broad datasets for biomarkers. We examine the new requirements that are not met by current data analysis technologies and we identify new database and KDD research to address these needs. We describe our experience implementing a Scientific OLAP system and a data mining platform for the support of biomarker discovery at SurroMed, and we outline some key technical challenges that must be overcome before data analysis and data mining technologies can be widely adopted in the biotech industry.