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Volumn , Issue , 2008, Pages 83-90

Comparison of classification methods on protein-protein interaction document classification

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; CYTOLOGY; FLOW INTERACTIONS; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; MOLECULAR BIOLOGY; TECHNICAL PRESENTATIONS;

EID: 58049170453     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBMW.2008.4686213     Document Type: Conference Paper
Times cited : (1)

References (39)
  • 1
    • 41949139082 scopus 로고    scopus 로고
    • Fitting a Geometric Graph to a Protein-Protein Interaction Network
    • DJ Higham, M Rasajski, N Przulj. Fitting a Geometric Graph to a Protein-Protein Interaction Network. Bioinformatics, 24(8): 1093-9,2008.
    • (2008) Bioinformatics , vol.24 , Issue.8 , pp. 1093-1099
    • Higham, D.J.1    Rasajski, M.2    Przulj, N.3
  • 2
  • 3
    • 0242559066 scopus 로고    scopus 로고
    • Extraction of protein interaction information from unstructured text using a context-free grammar
    • JM Temkin and MR Gilder. Extraction of protein interaction information from unstructured text using a context-free grammar. Bioinformatics, 19(16): 2046-2053, 2003.
    • (2003) Bioinformatics , vol.19 , Issue.16 , pp. 2046-2053
    • Temkin, J.M.1    Gilder, M.R.2
  • 5
    • 1842559914 scopus 로고    scopus 로고
    • Extracting human protein interactions from MEDLINE using a full-sentence parser
    • N Daraselia, A Yuryev, S Egorov, S Novichkova, A Nikitin and I Mazo. Extracting human protein interactions from MEDLINE using a full-sentence parser. Bioinformatics 20(5): 604-611, 2004.
    • (2004) Bioinformatics , vol.20 , Issue.5 , pp. 604-611
    • Daraselia, N.1    Yuryev, A.2    Egorov, S.3    Novichkova, S.4    Nikitin, A.5    Mazo, I.6
  • 7
    • 85081493506 scopus 로고    scopus 로고
    • J Xiao, J Su, GD Zhou, and CL Tan. Protein-Protein Interaction Extraction: A Supervised Learning Approach J Xiao 2005. In Proc Symp on Semantic Mining in Biomedicine, 2005.
    • J Xiao, J Su, GD Zhou, and CL Tan. Protein-Protein Interaction Extraction: A Supervised Learning Approach J Xiao 2005. In Proc Symp on Semantic Mining in Biomedicine, 2005.
  • 8
    • 20844458491 scopus 로고    scopus 로고
    • Mining with Rarity: A Unifying Framework
    • GM Weiss. Mining with Rarity: A Unifying Framework. SIGKDD Explorations, 6: 7-19, 2004.
    • (2004) SIGKDD Explorations , vol.6 , pp. 7-19
    • Weiss, G.M.1
  • 12
    • 49749113225 scopus 로고    scopus 로고
    • Using Significant, Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets
    • F Verhein, S Chawla. Using Significant, Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets. In Seventh IEEE International Conference on Data Mining. Pages 679-684, 2007.
    • (2007) Seventh IEEE International Conference on Data Mining , pp. 679-684
    • Verhein, F.1    Chawla, S.2
  • 13
    • 48649107941 scopus 로고    scopus 로고
    • Chris Seiffert,Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano.Mining Data with Rare Events: A Case Study. In 19th IEEE International Conference on Tools with Artificial Intelligence. pages 132-139,2007.
    • Chris Seiffert,Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano.Mining Data with Rare Events: A Case Study. In 19th IEEE International Conference on Tools with Artificial Intelligence. pages 132-139,2007.
  • 15
    • 85081522323 scopus 로고    scopus 로고
    • DD Lewis, M Ringuette. A comparison of two learning algorithms for text categorization. In Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval, pages 81-93, 1994.
    • DD Lewis, M Ringuette. A comparison of two learning algorithms for text categorization. In Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval, pages 81-93, 1994.
  • 19
    • 0003653039 scopus 로고
    • Introduction to Modern Information Retrieval
    • G Salton, M McGill. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.
    • (1983) McGraw-Hill
    • Salton, G.1    McGill, M.2
  • 23
    • 84871981420 scopus 로고    scopus 로고
    • Exploiting likely-positive and unlabeled data to improve the identification of protein-protein interaction articles
    • S
    • RTH Tsai, HC Hung, HJ Dai, YW Lin, WL Hsu. Exploiting likely-positive and unlabeled data to improve the identification of protein-protein interaction articles. BMC Bioinformatics, 2008, 9(Suppl 1): S3, 2008.
    • (2008) BMC Bioinformatics , vol.9 , Issue.SUPPL. 1
    • Tsai, R.T.H.1    Hung, H.C.2    Dai, H.J.3    Lin, Y.W.4    Hsu, W.L.5
  • 29
    • 23944466178 scopus 로고    scopus 로고
    • The Effect of Imbalanced Data Class Distribution on Fuzzy Classifiers - Experimental Study
    • S Visa, A Ralescu. The Effect of Imbalanced Data Class Distribution on Fuzzy Classifiers - Experimental Study. In 2005 IEEE International Conference on Fuzzy Systems, pages 749-754, 2005.
    • (2005) 2005 IEEE International Conference on Fuzzy Systems , pp. 749-754
    • Visa, S.1    Ralescu, A.2
  • 31
    • 2942731012 scopus 로고    scopus 로고
    • An Extensive Emporical Study of Feature Selectiong Metrics for Text Classification
    • G Forman. An Extensive Emporical Study of Feature Selectiong Metrics for Text Classification. Journal of Machine Learning Research, 3: 1289-1305, 2003.
    • (2003) Journal of Machine Learning Research , vol.3 , pp. 1289-1305
    • Forman, G.1
  • 32
    • 58049141286 scopus 로고    scopus 로고
    • FAST: A ROC-based Feature Selection Metric for Small Samples and Imbalanced Data Classification Problems
    • pages
    • X Chen, M Wasikowski. FAST: A ROC-based Feature Selection Metric for Small Samples and Imbalanced Data Classification Problems. KDD'08, pages: 124-132. 2008.
    • (2008) KDD'08 , pp. 124-132
    • Chen, X.1    Wasikowski, M.2
  • 33
    • 36448992227 scopus 로고    scopus 로고
    • S Ertekin, J Huang, CL Giles. Active Learning for Class Imbalance Problem. SIGIR 2007.
    • S Ertekin, J Huang, CL Giles. Active Learning for Class Imbalance Problem. SIGIR 2007.
  • 34
    • 85081496543 scopus 로고    scopus 로고
    • CIKM'07
    • 1
    • S Ertekin1, J Huang, Léon Bottou, CL Giles. Learning on the Border:Active Learning in Imbalanced Data Classification. CIKM'07.
    • Ertekin, S.1
  • 35
    • 58049158462 scopus 로고    scopus 로고
    • Document Classification for Mining Host Pathogen Protein-Protein Interactions
    • press
    • G Xu, L Yin, M Torii, Z Niu, C Wu, Z Hu and H Liu. Document Classification for Mining Host Pathogen Protein-Protein Interactions. IEEE BIBM 2008, in press.
    • (2008) IEEE BIBM
    • Xu, G.1    Yin, L.2    Torii, M.3    Niu, Z.4    Wu, C.5    Hu, Z.6    Liu, H.7
  • 37
    • 0345863927 scopus 로고    scopus 로고
    • The Unified Medical Language System (UMLS): Integrating biomedical terminology
    • O Bodenreider. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Research, 32: D267-D270,2004.
    • (2004) Nucleic Acids Research , vol.32
    • Bodenreider, O.1
  • 38
    • 85081514943 scopus 로고    scopus 로고
    • http://www.cs.umass.edu/~mccallum/bow/
  • 39
    • 8844253324 scopus 로고    scopus 로고
    • Understanding inverse document frequency: On theoretical arguments for IDF
    • S Robertson. Understanding inverse document frequency: on theoretical arguments for IDF. Journal of Documentation. 60(5): 503-520, 2004.
    • (2004) Journal of Documentation , vol.60 , Issue.5 , pp. 503-520
    • Robertson, S.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.