A genetic association study between breast cancer and osteoporosis using transitive text mining

2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009

Volumn , Issue , 2009, Pages 411-414

  Cheng, Bi Hua ^a,b   Vaka, Harsha Gopal Goud ^b   Mukhopadhyay, Snehasis ^b  

^a CHANG GUNG UNIVERSITY   (Taiwan)

^b INDIANA UNIVERSITY   (United States)

Author keywords

Breast cancer; Literature mining; Menopause; Osteoporosis; Text mining; Transitive text mining

Indexed keywords

AUTOMATED APPROACH; BREAST CANCER; CO-OCCURRENCE; COMPLEX INTERACTION; GENE INTERACTIONS; GENE LISTS; GRAPH-BASED; INDIRECT ASSOCIATION; LITERATURE MINING; POSTMENOPAUSAL WOMEN; SCORING METHODS; TEXT MINING; TRANSITIVE ASSOCIATIONS; TRANSITIVE CLOSURE;

BIOINFORMATICS; GENES;

COMPLEXATION;

EID: 74549182686     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBM.2009.35     Document Type: Conference Paper

References (15)

1. http://www.ncbi.nlm.nih.gov/PubMed/.
2. Cohen AM, Hersh WR: A survey of current work in biomedical text mining. Brief Bioinform 2005, 6(1):57-71.
3. Gajendran VK, Lin JR, Fyhrie DP: An application of bioinformatics and text mining to the discovery of novel genes related to bone biology. Bone 2007, 40(5):1378-1388.
4. Swanson DR: Migraine and magnesium: eleven neglected connections. Perspect Biol Med 1988, 31(4):526-557.
5. Thomas J, Thomas E, Tomb E: Serum and erythrocyte magnesium concentrations and migraine. Magnes Res 1992, 5(2):127-130.
6. Narayanasamy V, Mukhopadhyay S, Palakal M, Potter DA: TransMiner: mining transitive associations among biological objects from text. J Biomed Sci 2004, 11(6):864-873.
7. Vaka H.G.G., Mukhopadyay S. Knowledge Extraction and Extrapolation Using Ancient and Modern Biomedical Literature. Accepted for 2008 IEEE BioCom Workshop 2009, conjunction with AINA 2009. (Will be held in Bradford UK, 26-29 May 2009).
8. Palakal M., Stephens M., Mukhopadyay S., Raje R.Identification of biological relationships from text documents using efficient computational methods, Journal of Bioinformatics and Computational Biology, Vol. 1, No.2(2003) 307-342
9. Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, Liu Q, Cochran C, Bennett LM, Ding W et al: A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 1994, 266(5182):66-71.
10. Cornell University: Program on breast cancer and environmental risk factors. http://envirocancercornelledu/ 2009.
11. Bethesda MD: SEER Stat Fact Sheets, Breast Cancer. http:// seercancergov/csr/1975-2005/ 2008.
12. Hodges-Gallagher L, Valentine CD, El Bader S, Kushner PJ: Estrogen receptor beta increases the efficacy of antiestrogens by effects on apoptosis and cell cycling in breast cancer cells. Breast Cancer Res Treat 2008, 109(2):241-250.
13. Fischer D, Seifert M, Becker S, Ludders D, Cordes T, Reichrath J, Friedrich M: 25-Hydroxyvitamin D3 1alpha-hydroxylase splice variants in breast cell lines MCF-7 and MCF-10. Cancer Genomics Proteomics 2007, 4(4):295-300.
14. Salton G. Automatic Text Processing. Addison-Wesley (1989).
15. Warshall S. A Theorem on Boolean Matrices. JACM 9:11-12;1962.