Research work in the biomedical domain has been increasing at fast pace. Hence, the knowledge in the field of biomedical domain is growing exponentially. Consequently, the number of text documents containing the knowledge in this field is growing very rapidly. It is often very difficult for researchers to track the knowledge and assimilate it for generating new ideas. Therefore, it is highly desirable to organize such documents for extracting useful information from textual literature and store them in a structured form. As this information is embedded within text, so it is a challenging task to extract them. This paper presents a rule based system to extract biomedical relations along with biomedical entities from biomedical literatures. The system first generates a dependency tree of each sentence of a given literature, and then the rules are applied to extract the information components. The biomedical relations are embedded within these information components. Further, these information components are used to get feasible biomedical relations from a set of abstracts of biomedical literature. Furthermore, the system has been validated on a corpus of 500 abstracts downloaded from PubMed database on Alzheimer key word.

Text mining; Biomedical text mining, Biomedical relation extraction

[1] M. Habibi, L. Weber, M. Neves, D.L. Wiegandt, U. Leser, “Deep learning with word embeddings improves biomedical named entity recognition”, Bioinformatics, 33(14), pp. i37-i48, 2017.
[2] R. Jelier, G. Jenster, L.C. Dorssers, C.C. van der Eijk, E.M. van Mulligen, B. Mons, J.A. Kors, “Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes”, Bioinformatics, 21, pp. 2049–2058, 2005.
[3] T.K. Jenssen,A. Laegreid, J. Komorowski, E. Hovig, “A literature network of human genes for high-throughput analysis of gene expression”, Nature Genetics, 28(1), pp. 21–28, 2001.
[4] J. Ding, D. Berleant, D. Nettleton, E. Wurtele, “Mining Medline: abstracts, sentences, or phrases?”, In the Proceedings of the 7th Pacific Symposium on Biocomputing, Lihue, Hawaii, pp. 326–337, 2002.
[5] A. Divoli, T.K. Attwood, “BioIE: extracting informative sentences from the biomedical literature”, Bioinformatics, 21, pp. 2138–2139, 2005.
[6] K. Fundel, R. Kuffner, R. Zimmer, “RelEx—Relation extraction using dependency parse trees”, Bioinformatics, 23(3), pp. 365–371, 2007.
[7] Jahiruddin, M. Abulaish, L. Dey, “A concept-driven biomedical knowledge extraction and visualization framework for conceptualization of text corpora”, Journal of Biomedical Informatics, 43, pp. 1020-1035, 2010.
[8] E.S. Chen, G. Hripcsak, H. Xu, M. Markatou, C. Friedman, “Automated acquisition of disease–drug knowledge from biomedical and clinical documents: an initial study”, Journal of the American Medical Informatics Association, 15(1), pp. 87–98, 2008.
[9] J. Saric, L. Jensen, R. Ouzounova, I. Rojas, P. Bork, “Extraction of regulatory gene/protein networks from Medline”, Bioinformatics, 22(6), pp. 645–650, 2006.
[10] J. Hakenberg, C. Plake, U. Leser, H. Kirsch, D. Rebholz-Schuhmann, “LLL’05challenge: Genic interaction extraction-identification of language patterns based on alignment and finite state automata”, In the Proceedings of the 4th Learning Language in Logic workshop (LLL05), Bonn, Germany, pp. 38–45, 2005.
[11] B. Rink, S. Harabagiu, K. Roberts, “Automatic extraction of relations between medical concepts in clinical texts”, Journal of the American Medical Informatics Association, 18(5), pp. 594–600, 2011.
[12] M. Bundschus, M. Dejori, M. Stetter, V. Tresp, H.P. Kriegel, “Extraction of semantic biomedical relations from text using conditional random fields”, BMC bioinformatics, 9(1), pp. 207-220, 2008.
[13] Y. Miyao, K. Sagae, K. Saetre, T. Matsuzaki, J. Tsujii, “Evaluating contributions of natural language parsers to protein–protein interaction extraction” Bioinformatics, 25(3), pp. 394–400, 2009.
[14] M.S. Simpson, D. Demner-Fushman, “Biomedical text mining: A survey of recent progress”, Mining Text Data, Springer, pp. 465–517, 2012.
[15] A. Airola, S. Pyysalo, J. Björne, T. Pahikkala, F. Ginter, T. Salakoski, “A graph kernel for protein-protein interaction extraction”, In the Proceedings of the workshop on current trends in biomedical natural language processing. Association for Computational Linguistics, Columbus, Ohio, USA, pp. 1–9, 2008.
[16] M. Miwa, R. Saetre, Y. Miyao, J. Tsujii, “Protein– protein interaction extraction by leveraging multiple kernels and parsers”, International journal of medical informatics, 78(12), pp. e39–e46, 2009.
[17] S. Kim, J. Yoon, J. Yang, “Kernel approaches for genic interaction extraction”, Bioinformatics, 24(1), pp. 118–126, 2008.
[18] R.T.H. Tsai, W.C. Chou, Y.S. Su, Y.C. Lin, C.L. Sung, H.J. Dai, I.T.H. Yeh, W. Ku, T.Y. Sung, W.L. Hsu, “BIOSMILE: A semantic role labeling system for biomedical verbs using a maximum-entropy model with automatically generated template features”, BMC bioinformatics, 8(1), pp. 325-332, 2007.
[19] P. Thompson, S.A. Iqbal, J. McNaught, S. Ananiadou, “Construction of an annotated corpus to support biomedical information extraction”, BMC bioinformatics, 10(1), pp. 349-367, 2009.