Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Proceedings of the 17th International Conference on Data Engineering
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
SIAM Journal on Discrete Mathematics
Transparent access to multiple bioinformatics information sources
IBM Systems Journal - Deep computing for the life sciences
Integration of biological sources: current systems and challenges ahead
ACM SIGMOD Record
Methodological Review: Data integration and genomic medicine
Journal of Biomedical Informatics
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Weighted rank aggregation of cluster validation measures
Bioinformatics
Techniques for clustering gene expression data
Computers in Biology and Medicine
A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning
The Journal of Machine Learning Research
IEEE Internet Computing
DILS '09 Proceedings of the 6th International Workshop on Data Integration in the Life Sciences
Search computing: integrating ranked data in the life sciences
DILS'10 Proceedings of the 7th international conference on Data integration in the life sciences
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Search Computing has been proposed to support the integration of the results of search engines with other data and computational resources. A key feature of the resulting integration platform is direct support for multi-domain ordered data, reflecting the fact that search engines produce ranked outputs, which should be taken into account when the results of several requests are combined. In the life sciences, there are many different types of ranked data. For example, ranked data may represent many different phenomena, including physical ordering within a genome, algorithmically assigned scores that represent levels of sequence similarity, and experimentally measured values such as expression levels. This chapter explores the extent to which the search computing functionalities designed for use with search engine results may be applicable for different forms of ranked data that are encountered when carrying out data integration in the life sciences. This is done by classifying different types of ranked data in the life sciences, providing examples of different types of ranking and ranking integration needs in the life sciences, identifying issues in the integration of such ranked data, and discussing techniques for drawing conclusions from diverse rankings.