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Text Mining for Neuroscience: A Co-morbidity Case Study

  • Chapter
Knowledge-Based Systems in Biomedicine and Computational Life Science

Part of the book series: Studies in Computational Intelligence ((SCI,volume 450))

Abstract

The vast amount of information available to researchers has accelerated the pace of scientific discovery but also challenges researchers to find novel ways to manage and extract information from large databases to drive this process. A solution to this problem is found in text mining, which refers to the process of employing automated algorithms to identify the salient features of databases and predict novel associations based on user-defined keywords. Neuroscience has witnessed an explosion in published research and assimilating information from the > 180 neuroscience journals and mapping these discoveries on to a century of published research becomes a daunting and time-consuming task for the researcher. More importantly, given the vastness of this literature, it is likely that the researcher will not detect converging or related variables. However, text mining provides an automated way for the researcher to mine neuroscience databases and this paper proposes to use this process to identify points of contact between two parallel yet well-established disorder-related literatures - schizophrenia and alcoholism. We mine these databases to identify common pathologies between these disease states to understand why these diseases are commonly comorbid with an eventual aim to identify rational novel treatment strategies.

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References

  1. Alterman, A.I., Erdlen, F.R., McLellan, A.T., Mann, S.C.: Problem drinking in hospitalized schizophrenic patients. Addict. Behav. 5(3), 273–276 (1980)

    Article  Google Scholar 

  2. Barbee, J.G., Clark, P.D., Crapanzano, M.S., Heintz, G.C., Kehoe, C.E.: Alcohol and substance abuse among schizophrenic patients presenting to an emergency psychiatric service. J. Nerv. Ment. Dis. 177(7), 400–407 (1989); PubMed PMID: 2746193

    Article  Google Scholar 

  3. Berg, S.A., Czachowski, C.L., Chambers, R.A.: Alcohol seeking and consumption in the NVHL neurodevelopmental rat model of schizophrenia. Behav. Brain Res. 218(2), 346–349 (2011); Epub 2010 Dec 22. PubMed PMID: 21184782; PubMed Central PMCID: PMC3040447

    Article  Google Scholar 

  4. Burns, G.A., Cheng, W.C.: Tools for knowledge acquisition within the NeuroScholar system and their application to tract-tracing data. Journal of Biomedical Discovery and Collaboration 1, 10 (2006)

    Article  Google Scholar 

  5. Cheng, B., Vaka, H., Mukhopadhyay, S.: Gene-gene association study between Breast Cancer and Osteoporosis using Transminer text mining system. In: 2009 IEEE International Conference on Bioinformatics & Biomedicine (BIBM), Washington D.C, pp. 411–414 (2009)

    Google Scholar 

  6. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to algorithms. MIT Press (2009)

    Google Scholar 

  7. Crasto, C.J., Marenco, L.N., Liu, N., et al.: SenseLab: new developments in disseminating neuroscience information. Briefings in Bioinformatics 8, 150–162 (2007)

    Article  Google Scholar 

  8. Drake, R.E., Osher, F.C., Noordsy, D.L., Hurlbut, S.C., Teague, G.B., Beaudett, M.S.: Diagnosis of alcohol use disorders in schizophrenia. Schizophr Bull. 16(1), 57–67 (1990); PubMed PMID: 2333482

    Article  Google Scholar 

  9. Drake, R.E., Osher, F.C., Wallach, M.A.: Alcohol use and abuse in schizophrenia. A prospective community study. J. Nerv. Ment. Dis. 177(7), 408–414 (1989); PubMed PMID: 2746194

    Article  Google Scholar 

  10. French, L., Pavlidis, P.: Informatics in neuroscience. Briefings in Bioinformatics 8(6), 446–456 (2007)

    Article  Google Scholar 

  11. Fu, Y., Bauer, T., Mostafa, J., Palakal, M., Mukhopadhyay, S.: Concept extraction and association from Cancer literature. In: Eleventh International Conference on Information and Knowledge Management (CIKM2002) / Fourth ACM International Workshop on Web Information and Data Management (ACM WIDM 2002), McLean, VA, USA, pp. 100–103 (2002)

    Google Scholar 

  12. Hoblitzell, A., Mukhopadhyay, S., You, Q., Fang, S., Xia, Y., Bidwell, J.: Text Mining for Bone Biology. In: Proceedings of the ACM International Symposium on High Performance Distributed Computing (HPDC), Emerging Computational Methods for the Life Sciences Workshop, Chicago, June 21-25, pp. 522–530 (2010)

    Google Scholar 

  13. Gonzalez-Burgos, G., Lewis, D.A.: GABA Neurons and the Mechanisms of Network Oscillations: Implications for Understanding Cortical Dysfunction in Schizophrenia. Schizophr Bull. 34(5), 944–961 (2008)

    Article  Google Scholar 

  14. Jayadevaprakash, N., Mukhopadhyay, S., Palakal, M.: An approach to generating association graphs of non-co-occurring text objects using transitive methods. In: 20th ACM Symposium on Applied Computing – Bioinformatics Track, Santa Fe, New Mexico, pp. 141–145 (2005)

    Google Scholar 

  15. Kareken, D.A., Liang, T., et al.: A Polymorphism in GABRA2 Is Associated With the Medial Frontal Response to Alcohol Cues in an fMRI Study. Alcoholism: Clinical and Experimental Research 34, 2169–2178 (2010)

    Article  Google Scholar 

  16. Mukhopadhyay, S., Jayadevaprakash, N.: Automated metadata prediction and its application to biological association discovery. In: First Biocomputing Workshop at IEEE AINA Conference, Okinawa, Japan, pp. 708–713 (2008)

    Google Scholar 

  17. Mukhopadhyay, S., Palakal, M., Maddu, K.: Multi-way association extraction from biological text documents using hyper-graphs. In: IEEE Bioinformatics and Biomedicine (BIBM) Conference, Philadelphia, pp. 257–262 (2008)

    Google Scholar 

  18. Mukhopadhyay, S., Palakal, M., Maddu, K.: Multi-way association extraction and visualization from biological text documents using hyper-graphs: Applications to genetic association studies for diseases. Artificial Intelligence in Medicine Journal 49, 145–154 (2010)

    Article  Google Scholar 

  19. Narayanasamy, V., Mukhopadhyay, S., Palakal, M., Potter, D.: TransMiner: Mining transitive associations among biological objects from text. Journal of Biomedical Science 11(6), 864–873 (2004)

    Article  Google Scholar 

  20. Palakal, M., Kumar, K., Mukhopadhyay, S., Stephens, M., Li, H.: BioMap: Toward the development of a knowledge base of biomedical literature. In: 19th ACM Symposium on Applied Computing (ACM SAC 2004)– Bioinformatics Track, Nicosia, Cyprus, pp. 121–127 (2004)

    Google Scholar 

  21. Palakal, M., Mukhopadhyay, S., Mostafa, J., Raje, R., Mishra, S., N’Cho, M.: An intelligent biological information management system. Bioinformatics 18, 1283–1288 (2002b)

    Article  Google Scholar 

  22. Palakal, M., Mukhopadhyay, S., Stephens, M.: Identification of biological relationships from text documents. In: Chen, H., Fuller, S., Friedman, C., Hersh, W. (eds.) Medical Informatics: Advances in Knowledge Management and Data Mining in Biomedicine, pp. 449–489. Springer (2005)

    Google Scholar 

  23. Palakal, M., Stephens, M., Mukhopadhyay, S., Raje, R., Rhodes, S.: A multi-level text mining method to extract biological relationships. In: IEEE Computer Society Bioinformatics (CSB) Conference, pp. 97–108. Stanford University (2002a)

    Google Scholar 

  24. Palakal, M., Stephens, M., Mukhopadhyay, S., Raje, R., Rhodes, S.: Identification of biological relationships from text documents using efficient computational methods. Journal of Bioinformatics and Computational Biology (JBCB) 1(2), 1–34 (2003)

    Google Scholar 

  25. Regier, D.A., Farmer, M.E., Rae, D.S., Locke, B.Z., Keith, S.J., Judd, L.L., Goodwin, F.K.: Comorbidity of mental disorders with alcohol and other drug abuse. Results from the Epidemiologic Catchment Area (ECA) Study. JAMA 264(19), 2511–2518 (2018); PubMed PMID: 2232018

    Google Scholar 

  26. Salton, G.: Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Reading (1989)

    Google Scholar 

  27. Shepherd, G.M., Mirsky, J.S., Healy, M.D., et al.: The human brain project: neuroinformatics tools for integrating, searching and modeling multidisciplinary neuroscience data. Trends in Neuroscience 21, 460–468 (1998)

    Article  Google Scholar 

  28. Smith, M.J., Barch, D.M., Wolf, T.J., Mamah, D., Csernansky, J.G.: Elevated rates of substance use disorders in non-psychotic siblings of individuals with schizophrenia. Schizophr Res 106(2-3), 294–299 (2008); Epub September 5, 2008. PubMed PMID: 18774264; PubMed Central PMCID: PMC2627404

    Article  Google Scholar 

  29. Stephens, M., Palakal, M., Mukhopadhyay, S., Raje, R., Mostafa, J.: Detecting gene relations from MEDLINE abstracts. In: Pacific Symposium on Biocomputing, Honolulu, Hawaii, pp. 483–495 (2010)

    Google Scholar 

  30. Textpresso for Neuroscience, http://www.textpresso.org/neuroscience (last accessed on January 12, 2010)

  31. Tilak, O., Hoblitzell, A., Mukhopadhyay, S., You, Q., Fang, S., Xia, Y., Bidwell, J.: Multi- Level Text Mining for Bone Biology. Concurrency and Computation: Practice and Experience Journal 23(17), 2355–2364 (2011)

    Article  Google Scholar 

  32. Tirupattur, N., Lapish, C., Mukhopadhyay, S.: Text Mining for Neuroscience. In: Proceedings of Computational Models for Life Sciences (CMLS 2011) Symposium, pp. 118–127. AIP Press, Toyoma City (2011)

    Google Scholar 

  33. Yesavage, J.A., Zarcone, V.: History of drug abuse and dangerous behavior in inpatient schizophrenics. J. Clin. Psychiatry 44(7), 259–261 (1983); PubMed PMID:

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Psychology, Indiana University Purdue University, Indianapolis, USA

    Christopher C. Lapish

  2. Department of Computer & Information Science, Indiana University Purdue University, Indianapolis, USA

    Naveen Tirupattur & Snehasis Mukhopadhyay

Authors
  1. Christopher C. Lapish
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  2. Naveen Tirupattur
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  3. Snehasis Mukhopadhyay
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Editor information

Editors and Affiliations

  1. Aizu Research Cluster for Medical Eng, and Informatics Center for Advanced, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan

    Tuan D. Pham

  2. , Adjunct Professor, University of Canberra, Kirinari St. Mail Room, ACT, 2601, Australia

    Lakhmi C. Jain

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Lapish, C.C., Tirupattur, N., Mukhopadhyay, S. (2013). Text Mining for Neuroscience: A Co-morbidity Case Study. In: Pham, T., Jain, L. (eds) Knowledge-Based Systems in Biomedicine and Computational Life Science. Studies in Computational Intelligence, vol 450. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33015-5_5

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  • DOI: https://doi.org/10.1007/978-3-642-33015-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33014-8

  • Online ISBN: 978-3-642-33015-5

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