7) Duplicate Detection in Biological Data using Association.

Koh JLY, Lee ML, Khan AM, Tan PTJ, Brusic V
2nd European Workshop on Data Mining and Text Mining for Bioinformatics.
Pisa, Italy, September 24, 2004.
Out link: Full-text
Impact Factor Year 2009: NA
No. of Citations: 19 (total): 16 (non-self) & 3 (self)

ABSTRACT :

Recent advancement in biotechnology has produced a massive amount of raw biological data which are accumulating at an exponential rate. Errors, redundancy and discrepancies are prevalent in the raw data, and there is a serious need for systematic approaches towards biological data cleaning. This work examines the extent of redundancy in biological data and proposes a method for detecting duplicates in biological data. Duplicate relations in a real-world biological dataset are modeled
into forms of association rules so that these duplicate relations or rules can be induced from data with known duplicates using association rule mining. Our approach of using association rule induction to find duplicate relations is new. Evaluation of our method on a real-world dataset shows that our duplicate association rules can accurately identify up to 96.8% of the duplicates in the dataset at the accuracy of 0.3% false positives and 0.0038% false negatives.

This article has been cited by other articles:

1) McCallum, A, Bellare, K, Pereira, F. A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance. Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence (UAI 2005). http://www.seas.upenn.edu/~strctlrn/bib/PDF/crfstredit.pdf

2) Jakoniene, V, Rundqvist, D, Lambrix, P. A method for similarity-based grouping of biological data. Lecture Notes in Computer Science Series. In the book Data Integration in the Life Sciences. Page: 136-151.

3) David Rundqvist. Grouping Biological Data. PhD Thesis. Dept. of Computer and Information Science at Link¨oping University. http://www.diva-portal.org/diva/getDocument?urn_nbn_se_liu_diva-6327-1__fulltext.pdf

4) Veeramani, A, Gopalakrishnan, K, Brusic, V, Koh, JLY. Biodart - Catalogue of Biological Data Artifact Examples. International Conference on Biomedical and Pharmaceutical Engineering, 2006. 2006 Page(s):c1 - xiii. http://antigen.i2r.a-star.edu.sg/ani/textmin/kddinfo/pdf/261_1.pdf

5) Zhu YY, Xiong Y. DNA sequence data mining technique. Journal of Software, 2007,18(11):2766-2781. http://www.jos.org.cn/1000-9825/18/2766.htm

6) Judice, Lie Yong Koh. Correlation-Based Methods for Biological Data Cleaning. PhD thesis. 2007, National University of Singapore

7) van Grootel RJ, van der Bilt A, van der Glas HW. Long-term reliable change of pain scores in individual myogenous TMD patients. Eur J Pain. 2007 Aug;11(6):635-43. Epub 2006 Nov 22. PMID: 17118682

8) Arraes LC, de Souza PR, Bruneska D, Castelo Filho A, Cavada Bde S, de Lima Filho JL, Crovella S. A cost-effective melting temperature assay for the detection of single-nucleotide polymorphism in the MBL2 gene of HIV-1-infected children. Braz J Med Biol Res. 2006 Jun;39(6):719-23. Epub 2006 Jun 2. PMID: 16751976

9) Szalma SJ, Buckler ES 4th, Snook ME, McMullen MD.
Association analysis of candidate genes for maysin and chlorogenic acid accumulation in maize silks. Theor Appl Genet. 2005 May;110(7):1324-33. Epub 2005 Apr 2. PMID: 15806344

10) Ferguson M, Heath A. Report of a collaborative study to calibrate the Second International Standard for parvovirus B19 antibody. Biologicals. 2004 Dec;32(4):207-12. PMID: 15572102

11) D Apiletti, G Bruno, E Ficarra, E Baralis. Data Cleaning and Semantic Improvement in Biological Databases. Journal of Integrative Bioinformatics, 2006 - imbio.de

12) http://www.cqvip.com/qk/96857x/2007011/25789686.html

13) A classification of biological data artifacts

JLY Koh, ML Lee, V Brusic

Workshop on Database Issues in Biological Databases

January 8-9, 2005

National e-Science Centre, Edinburgh, UK

Organized by the European Bioinformatics Institute & Edinburgh Database Group

homepages.inf.ed.ac.uk

14) A tool for evaluating strategies for grouping of biological data

V Jakoniene, P Lambrix - Journal of Integrative Bioinformatics, 2007

15) Quantitative association rules mining

F Karel - Proc. 10th Int. Conf. Knowl.-Based Intell. Inf. Eng. Syst, 2006

16) Detecting duplicate biological entities using Markov random field-based edit distance

M Song, A Rudniy

Bioinformatics and Biomedicine, 2008 (BIBM '08)

ieeexplore.ieee.org

17) A Comprehensive Review of Significant Researches on Duplicate Record Detection in Databases

Deepa K & Rangarajan R

Advances in Computational Sciences and Technology. 2009; 2(2)

18) Extraction of Constraints from Biological Data

Daniele Apiletti, Giulia Bruno, Elisa Ficarra and Elena Baralis

BOMEDICAL DATA AND APPLICATIONS

Studies in Computational Intelligence, 2009, Volume 224/2009, 169-186, DOI: 10.1007/978-3-642-02193-0_7

19) C Markschies

Describing Differences between Overlapping Databases

PhD Thesis, 2008

edoc.hu-berlin.de

Humboldt-Universität zu Berlin:

1) Bioinformatics for venom and toxin sciences.

Tan PT, Khan AM, Brusic V.
Brief Bioinform. 2003 Mar;4(1):53-62. Review.
PUBMED PMID: 12715834
Out link: Full-text
Impact Factor Year 2009: 7.329
No. of Citations: 19 (total): 10 (non-self) & 9 (self)

ABSTRACT :

Venomous animals produce a myriad of important pharmacological components. The individual components, or venoms (toxins), are used in ion channel and receptor studies, drug discovery, and formulation of insecticides. The toxin data are scattered across public databases which provide sequence and structural descriptions, but very limited functional annotation. The exponential growth of newly identified toxin data has created a need for better data management. Venominformatics is a systematic bioinformatics approach in which classified, consolidated and cleaned venom data are stored into repositories and integrated with advanced bioinformatics tools for the analysis of structure and function of toxins. Venominformatics complements experimental studies and helps reduce the number of essential experiments.

This article has been cited by other articles:

1) Kini RM. Excitement ahead: structure, function and mechanism of snake venom phospholipase A2 enzymes. Toxicon. 2003 Dec 15;42(8):827-40. Review. PMID: 15019485

2) Lenffer J, Lai P, El Mejaber W, Khan AM, Koh JL, Tan PT, Seah SH, Brusic V. CysView: protein classification based on cysteine pairing patterns. Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W350-5. PMID: 15215409

3) Siew JP, Khan AM, Tan PT, Koh JL, Seah SH, Koo CY, Chai SC, Armugam A, Brusic V, Jeyaseelan K. Systematic analysis of snake neurotoxins' functional classification using a data warehousing approach. Bioinformatics. 2004 Dec 12;20(18):3466-80. Epub 2004 Jul 22. PMID: 15271784

4) Koh JLY, Krishnan SPT, Seah SH, Tan PT, Khan AM, Lee ML, Brusic V. BioWare: A framework for bioinformatics data retrieval, annotation and publishing. 27th Annual International ACM SIGIR Conference on Research and Development in IR. Sheffield, UK, July 29, 2004. http://lair.indiana.edu/sigirbio/final/koh.pdf

5) Kini RM. Structure-function relationships and mechanism of anticoagulant phospholipase A2 enzymes from snake venoms. Toxicon. 2005 Jun 15;45(8):1147-61. Epub 2005 Apr 13. Review. PMID: 15922780

6) Tan PT, Srinivasan KN, Seah SH, Koh JL, Tan TW, Ranganathan S, Brusic V. Accurate prediction of scorpion toxin functional properties from primary structures. J Mol Graph Model. 2005 Sep;24(1):17-24. PMID: 15950506

7) Smith WL, Wheeler WC. Venom evolution widespread in fishes: a phylogenetic road map for the bioprospecting of piscine venoms. J Hered. 2006 May-Jun;97(3):206-17. Epub 2006 Jun 1. PMID: 16740627

8) Kini RM. Anticoagulant proteins from snake venoms: structure, function and mechanism. Biochem J. 2006 Aug 1;397(3):377-87. Review. PMID: 16831131

9) Wagstaff SC, Laing GD, Theakston RD, Papaspyridis C, Harrison RA. Bioinformatics and multiepitope DNA immunization to design rational snake antivenom. PLoS Med. 2006 Jun;3(6):e184. PMID: 16737347

10) K Lam, JLY Koh, B Veeravalli, V Brusic. Incremental Maintenance of Biological Databases Using Association Rule Mining. LECTURE NOTES IN COMPUTER SCIENCE, 2006 - Springer

11) Seah, S.H., Kwoh, C.K., Brusic, V., Sakharkar, M.K., Ng, G.S. Functional prediction of snake neurotoxins. 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06, art. no. 4150399

12) Bhogal, S., Revett, K. Animal toxins: What features differentiate pore blockers from gate modifiers? 2005 ICSC Congress on Computational Intelligence Methods and Applications 2005, art. no. 1662329

13) Saha S, Raghava GP. Prediction of neurotoxins based on their function and source. In Silico Biol. 2007 Apr 6;7:0025 [Epub ahead of print]. PMID: 17688450

14) Judice, Lie Yong Koh. Correlation-Based Methods for Biological Data Cleaning. PhD thesis. 2007, National University of Singapore

15) Sivan, G. Fish venom: Pharmacological features and biological significance. Fish and Fisheries, 2009, 10 (2), pp. 159-172

16) Naamati G, Askenazi M, Linial M. ClanTox: a classifier of short animal toxins. Nucleic Acids Res. 2009 Jul 1;37(Web Server issue):W363-8. Epub 2009 May 8. PMID: 19429697

17) Tan Thiam Joo, Paul

Functional prediction of bioactive toxins in scorpion venom through bioinformatics

PhD Thesis, 2006

https://scholarbank.nus.edu.sg/handle/10635/15536

18) Seah SH, Kwoh CK, Brusic V, et al.

Conference Information: 9th International Conference on Control, Automation, Robotics and Vision, DEC 05-08, 2006 Singapore, SINGAPORE

Source: 2006 9th International Conference on Control, Automation, Robotics and Vision, Vols 1- 5 Pages: 2295-2298 Published: 2006

19) Busca de inibidores naturais contra o veneno de Apis mellifera

DMDEM JORGE - hostsh.com.br