Abstract
Annotation Enrichment Analysis (AEA) is a widely used analytical approach to process data generated by high-throughput genomic and proteomic experiments such as gene expression microarrays. We briefly review ideas behind AEA, identify some limitations and propose a novel logic-based Annotation Concept Synthesis and Enrichment Analysis (ACSEA) approach. ACSEA fuses inductive logic reasoning with statistical inference to uncover more complex phenomena captured by the experiments. The results of the evaluation suggest that ACSEA can boost effectiveness of the processing of high-throughput experiments.
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Jiline, M., Matwin, S., Turcotte, M. (2010). Annotation Concept Synthesis and Enrichment Analysis. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_34
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DOI: https://doi.org/10.1007/978-3-642-13059-5_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13058-8
Online ISBN: 978-3-642-13059-5
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