Paper Info
Title
Literature Mining: Towards Better Understanding of Autism
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.
Year
DOI
Venue
2007
10.1007/978-3-540-73599-1_29
AIME '87
Keywords
Field
DocType
literature mining,rare term,medical point,medical expert,new explanation,biomedical literature,abc model approach,towards better understanding,literature mining method rajolink,new piece,corresponding literature,joint term,knowledge discovery,word frequency
Autism,Data mining,Population,Word lists by frequency,Computer science,Artificial intelligence,Knowledge extraction,Machine learning
Conference
Volume
ISSN
Citations 
4594
0302-9743
10
PageRank 
References 
Authors
0.73
5
4
Name
Order
Citations
PageRank
Tanja Urbančič1504.33
Ingrid Petrič2493.62
Bojan Cestnik3716262.57
Marta Macedoni-Lukšič4392.49