Abstract
The use of two statistical metrics in automatically identifying important keywords associated with a concept such as a gene by mining scientific literature is reviewed. Starting with a subset of MEDLINE® abstracts that contain the name or synonyms of a gene in their titles, the aforementioned metrics contrast the prevalence of specific words in these documents against a broader “background set” of abstracts. If a word occurs substantially more often in the document subset associated with a gene than in the background set that acts as a reference, then the word is viewed as capturing some specific attribute of the gene.
The keywords thus automatically identified may be used as gene features in clustering algorithms. Since the background set is the reference against which keyword prevalence is contrasted, the authors hypothesize that different background document sets can lead to somewhat different sets of keywords to be identified as specific to a gene. Two different background sets are discussed that are useful for two somewhat different purposes, namely, characterizing the function of a gene, and clustering a set of genes based on their shared functional similarities. Experimental results that reveal the significance of the choice of background set are presented.
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Notes
- 1.
This is also sometimes called the collection frequency of the term in the set of documents, and counts the total number of occurrences of the term in all the documents of the collection. It differs from the document frequency of a term in a collection of documents in that the document frequency just counts how many documents contain the term (with no distinction on the number of occurrences).
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Acknowledgments
The authors acknowledge that the MEDLINE® data used in this research are covered by a license agreement supported by the U.S. National Library of Medicine. Thanks are also due to Professor Rajnish Singh (Kennesaw State University) for her assistance in relation to evaluating the keywords for the various genes, and for her help in other ways related to this work.
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Dasigi, V.G., Karam, O., Pydimarri, S. (2019). Impact of Context on Keyword Identification and Use in Biomedical Literature Mining. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-02686-8_38
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