Literature mining method RaJoLink for uncovering relations between biomedical concepts
Journal of Biomedical Informatics
A new evaluation methodology for literature-based discovery systems
Journal of Biomedical Informatics
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
Evaluating outliers for cross-context link discovery
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Selecting the links in bisonets generated from document collections
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Selecting the links in bisonets generated from document collections
Bisociative Knowledge Discovery
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
Semantic subgroup discovery and cross-context linking for microarray data analysis
Bisociative Knowledge Discovery
Supervised hypothesis discovery using syllogistic patterns in the biomedical literature
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Knowledge network of scientific claims derived from a semantic publication system
Information Services and Use - 16th International Conference on Electronic Publishing --ELPUB 2012 --Social Shaping of Digital Publishing: Exploring the Interplay between Culture and Technology
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Arrowsmith, a computer-assisted process for literature-based discovery, takes as input two disjoint sets of records (A, C) from the Medline database. It produces a list of title words and phrases, B, that are common to A and C, and displays the title context in which each B-term occurs within A and within C. Subject experts then can try to find A–B and B–C title-pairs that together may suggest novel and plausible indirect A–C relationships (via B-terms) that are of particular interest in the absence of any known direct A–C relationship. The list of B-terms typically is so large that it is difficult to find the relatively few that contribute to scientifically interesting connections. The purpose of the present article is to propose and test several techniques for improving the quality of the B-list. These techniques exploit the Medical Subject Headings (MeSH) that are assigned to each input record. A MesH-based concept of literature cohesiveness is defined and plays a key role. The proposed techniques are tested on a published example of indirect connections between migraine and magnesium deficiency. The tests demonstrate how the earlier results can be replicated with a more efficient and more systematic computer-aided process. © 2006 Wiley Periodicals, Inc.