LitLinker: capturing connections across the biomedical literature
Proceedings of the 2nd international conference on Knowledge capture
Applications of machine learning: matching problems to tasks and methods
The Knowledge Engineering Review
RaJoLink: A Method for Finding Seeds of Future Discoveries in Nowadays Literature
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
A top-k analysis using multi-level association rule mining for autism treatments
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: applications and services - Volume Part IV
Evaluating outliers for cross-context link discovery
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Inferring hidden relationships from biological literature with multi-level context terms
Proceedings of the ACM fifth international workshop on Data and text mining in biomedical informatics
Bridging concept identification for constructing information networks from text documents
Bisociative Knowledge Discovery
Bisociative knowledge discovery by literature outlier detection
Bisociative Knowledge Discovery
Exploring the power of outliers for cross-domain literature mining
Bisociative Knowledge Discovery
Bisociative literature mining by ensemble heuristics
Bisociative Knowledge Discovery
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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.