Abstract
In this article we present a literature mining method RaJoLink that upgrades Swanson’s ABC model approach to uncovering hidden relations from a set of articles in a given domain. When these relations are interesting from medical point of view and can be verified by medical experts, they represent new pieces of knowledge and can contribute to better understanding of diseases. In our study we analyzed biomedical literature about autism, which is a very complex and not yet sufficiently understood domain. On the basis of word frequency statistics several rare terms were identified with the aim of generating potentially new explanations for the impairments that are observed in the affected population. Calcineurin was discovered as a joint term in the intersection of their corresponding literature. Similarly, NF-kappaB was recognized as a joint term. Pairs of documents that point to potential relations between the identified joint terms and autism were also automatically detected. Expert evaluation confirmed the relevance of these relations.
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Urbančič, T., Petrič, I., Cestnik, B., Macedoni-Lukšič, M. (2007). Literature Mining: Towards Better Understanding of Autism. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_29
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DOI: https://doi.org/10.1007/978-3-540-73599-1_29
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