Literature Mining: Towards Better Understanding of Autism Export

@incollection{citeulike:1825153, 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.}, author = {Urban\v{c}i\v{c}, Tanja and Petri\v{c}, Ingrid and Cestnik, Bojan and Macedoni-Luk\v{s}i\v{c}, Marta}, citeulike-article-id = {1825153}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-73599-1_29}, citeulike-linkout-1 = {http://www.springerlink.com/content/v2mw805311522215}, doi = {10.1007/978-3-540-73599-1_29}, journal = {Artificial Intelligence in Medicine}, keywords = {autism, literature-based-discovery, text-mining}, pages = {217--226}, posted-at = {2009-09-22 14:32:43}, priority = {2}, title = {Literature Mining: Towards Better Understanding of Autism}, url = {http://dx.doi.org/10.1007/978-3-540-73599-1_29}, year = {2007} }

TY - CHAP ID - citeulike:1825153 L3 - citeulike-article-id:1825153 N2 - 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. JF - Artificial Intelligence in Medicine EP - 226 TI - Literature Mining: Towards Better Understanding of Autism SP - 217 KW - autism KW - literature-based-discovery KW - text-mining AU - Urbančič, Tanja AU - Petrič, Ingrid AU - Cestnik, Bojan AU - Macedoni-Lukšič, Marta PY - 2007/// UR - http://dx.doi.org/10.1007/978-3-540-73599-1_29 ER -