메뉴 건너뛰기




Volumn , Issue , 2009, Pages 195-200

Structural domain based multiple instance learning for predicting gram-positive bacterial protein subcellular localization

Author keywords

Machine learning; Multiple instance learning; Multiple instance multiple label learning; Protein domain; Protein subcelluar location

Indexed keywords

MACHINE-LEARNING; MULTIPLE INSTANCE LEARNING; MULTIPLE INSTANCES; MULTIPLE LABELS; PROTEIN DOMAINS;

EID: 70450205609     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCBS.2009.14     Document Type: Conference Paper
Times cited : (6)

References (27)
  • 2
    • 56649094590 scopus 로고    scopus 로고
    • Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species
    • KiYoung Lee, Han-Yu Chuang, Andreas Beyer, Min-Kyung Sung,Won-Ki Huh, Bonghee Lee and Trey Ideker. Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species. Nucleic Acids Research, 2008, Vol. 36, No. 20.
    • (2008) Nucleic Acids Research , vol.36 , Issue.20
    • Lee, K.1    Chuang, H.-Y.2    Beyer, A.3    Sung, M.-K.4    Huh, W.-K.5    Lee, B.6    Ideker, T.7
  • 3
    • 0141515750 scopus 로고    scopus 로고
    • Prediction of protein subcellular location by support vector machines using compositions of amino acids and amino acid pairs
    • Park,K.-J. and Kanehisa,M. Prediction of protein subcellular location by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics, 2003, 19, 1656-1663.
    • (2003) Bioinformatics , vol.19 , pp. 1656-1663
    • Park, K.-J.1    Kanehisa, M.2
  • 4
  • 5
    • 0038705861 scopus 로고    scopus 로고
    • Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudoamino acid composition
    • Cai,Y.D. and Chou,K.C. Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudoamino acid composition. Biochem. Biophys. Res. Commun.,2003, 305, 407-411.
    • (2003) Biochem. Biophys. Res. Commun. , vol.305 , pp. 407-411
    • Cai, Y.D.1    Chou, K.C.2
  • 6
    • 34247218437 scopus 로고    scopus 로고
    • Prediction of subcellular protein localization based on functional domain composition
    • DOI 10.1016/j.bbrc.2007.03.139, PII S0006291X07006067
    • Peilin Jia , Ziliang Qian, ZhenBin Zeng, Yudong Cai and Yixue Li. Prediction of subcellular protein localization based on functional domain composition. Biochemical and Biophysical Research Communications, 2007, 357,366-370. (Pubitemid 46604876)
    • (2007) Biochemical and Biophysical Research Communications , vol.357 , Issue.2 , pp. 366-370
    • Jia, P.1    Qian, Z.2    Zeng, Z.3    Cai, Y.4    Li, Y.5
  • 7
    • 3242877807 scopus 로고    scopus 로고
    • ELSpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSIBLAST
    • Bhasin,M. and Raghava,G.P. ELSpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSIBLAST. Nucleic Acid Res. 2004, 32 (Web Server issue), W414-W419.
    • (2004) Nucleic Acid Res. , vol.32 , Issue.WEB SERVER ISSUE
    • Bhasin, M.1    Raghava, G.P.2
  • 8
    • 0034687538 scopus 로고    scopus 로고
    • Prediction of protein subcellular locations by incorporating quasi-sequence-order effect
    • Chou K.C. Prediction of Protein Subcellular Locations by Incorporating Quasi-Sequence-Order Effect. Biochemical and Biophysical Research Communications , 2000,278, 477-483.
    • (2000) Biochemical and Biophysical Research Communications , vol.278 , pp. 477-483
    • Chou, K.C.1
  • 9
    • 34548606295 scopus 로고    scopus 로고
    • Review: Recent progresses in protein subcellular location prediction
    • Chou, K.C. and Shen, H.B. Review: Recent progresses in protein subcellular location prediction, Analytical Biochemistry, 2007, 370, 1-16.
    • (2007) Analytical Biochemistry , vol.370 , pp. 1-16
    • Chou, K.C.1    Shen, H.B.2
  • 10
    • 39449105071 scopus 로고    scopus 로고
    • Cell-PLoc: A package of web-servers for predicting subcellular localization of proteins in various organisms
    • Chou, K.C. and Shen, H.B. Cell-PLoc: A package of web-servers for predicting subcellular localization of proteins in various organisms, Nature Protocols, 2008,3, 153-162.
    • (2008) Nature Protocols , vol.3 , pp. 153-162
    • Chou, K.C.1    Shen, H.B.2
  • 11
    • 0034697980 scopus 로고    scopus 로고
    • Predicting subcellular localization of proteins based on their N-terminal amino acid sequence
    • Emanuelsson,O. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol.,2000, 300, 1005-1016.
    • (2000) J. Mol. Biol. , vol.300 , pp. 1005-1016
    • Emanuelsson, O.1
  • 12
    • 33646861792 scopus 로고    scopus 로고
    • MultiLoc: Prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition
    • Annette Hoglund, Pierre Donnes, Torsten Bluml, Hans-Werner Adolph and Oliver Kohlbacher. MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition. Bioinformatics, 2006, Vol. 22 no. 10, pages 1158-1165.
    • (2006) Bioinformatics , vol.22 , Issue.10 , pp. 1158-1165
    • Hoglund, A.1    Donnes, P.2    Bluml, T.3    Adolph, H.-W.4    Kohlbacher, O.5
  • 13
    • 70450153225 scopus 로고    scopus 로고
    • Jennifer L. Gardy, Cory Spencer, Ke Wang, Martin Ester1, Gábor E. Tusnády,István Simon, Sujun Hua, Katalin deFays, Christophe
    • Jennifer L. Gardy, Cory Spencer, Ke Wang, Martin Ester1, Gábor E. Tusnády,István Simon, Sujun Hua, Katalin deFays, Christophe
  • 14
    • 0034710981 scopus 로고    scopus 로고
    • Localizing proteins in the cell from their phylogenetic profiles
    • Marcotte,E.M. et al. Localizing proteins in the cell from their phylogenetic profiles. Proc. Natl Acad. Sci. USA, 2000, 97, 12115-12120.
    • (2000) Proc. Natl Acad. Sci. USA , vol.97 , pp. 12115-12120
    • Marcotte, E.M.1
  • 15
    • 0037224288 scopus 로고    scopus 로고
    • Predicting protein cellular localization using a domain projection method
    • Richard Mott, Jörg Schultz, Peer Bork and Chris P. Ponting.Predicting Protein Cellular Localization Using a Domain Projection Method.Genome Research, 2002,12:1168-1174.
    • (2002) Genome Research , vol.12 , pp. 1168-1174
    • Mott, R.1    Schultz, J.2    Bork, P.3    Ponting, C.P.4
  • 16
    • 0032077533 scopus 로고    scopus 로고
    • Using neural networks for prediction of the subcellular location of proteins
    • Reinhardt,A. and Hubbard,T. Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res., 1998, 26, 2230-2236.
    • (1998) Nucleic Acids Res. , vol.26 , pp. 2230-2236
    • Reinhardt, A.1    Hubbard, T.2
  • 17
    • 0032938624 scopus 로고    scopus 로고
    • Prediction of protein subcellular locations using Markov chain models
    • Yuan,Z. Prediction of protein subcellular locations using Markov chain models. FEBS Lett., 1999, 451, 23-26.
    • (1999) FEBS Lett. , vol.451 , pp. 23-26
    • Yuan, Z.1
  • 18
    • 33847080827 scopus 로고    scopus 로고
    • Gpos-PLoc: An ensemble classifier for predicting subcellular localization of Gram-positive bacterial
    • Shen, H.B. and Chou K.C. Gpos-PLoc: an ensemble classifier for predicting subcellular localization of Gram-positive bacterial. Protein Engineering, Design & Selection, 2007, vol. 20 no. 1 pp. 39-46.
    • (2007) Protein Engineering, Design and Selection , vol.20 , Issue.1 , pp. 39-46
    • Shen, H.B.1    Chou, K.C.2
  • 19
    • 2942624227 scopus 로고    scopus 로고
    • Review advances in the prediction of protein targeting signals
    • Gisbert Schneider and Uli Fechner.Review Advances in the prediction of protein targeting signals. Proteomics , 2004, 4, 1571-1580.
    • (2004) Proteomics , vol.4 , pp. 1571-1580
    • Schneider, G.1    Fechner, U.2
  • 20
    • 70450149059 scopus 로고    scopus 로고
    • An automated combination of Kernels for predicting protein subcellular localization
    • workshop on Machine Learning in Computational Biology
    • Alexander Zien and Cheng Soon Ong.An Automated Combination of Kernels for Predicting Protein Subcellular Localization. NIPS , 2007, workshop on Machine Learning in Computational Biology.
    • (2007) NIPS
    • Zien, A.1    Ong, C.S.2
  • 21
    • 84863161940 scopus 로고    scopus 로고
    • Image categorization by learning and reasoning with regions
    • Y. Chen and J. Z.Wang). Image categorization by learning and reasoning with regions. Journal of Machine Learning Research, 2004, 5:913-939.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 913-939
    • Chen, Y.1    Wang, J.Z.2
  • 25
    • 56649091109 scopus 로고    scopus 로고
    • Multiple instance learning allows MHC class II epitope predictions across alleles
    • Springer
    • Nico Pfeifer and Oliver Kohlbacher. Multiple Instance Learning Allows MHC Class II Epitope Predictions Across Alleles.Springer, 2008,LNBI 5251, pp. 210-221.
    • (2008) LNBI , vol.5251 , pp. 210-221
    • Pfeifer, N.1    Kohlbacher, O.2
  • 26
    • 7444219637 scopus 로고    scopus 로고
    • Logistic regression and boosting for labeled bags of instances
    • H. Dai, R. Srikant,and C. Zhang, editors. Springer, Berlin
    • X. Xu and E. Frank. Logistic regression and boosting for labeled bags of instances. In H. Dai, R. Srikant,and C. Zhang, editors, Lecture Notes in Artificial Intelligence 3056, 2004, pages 272-281. Springer, Berlin.
    • (2004) Lecture Notes in Artificial Intelligence , vol.3056 , pp. 272-281
    • Xu, X.1    Frank, E.2
  • 27
    • 0030649484 scopus 로고    scopus 로고
    • Solving the multiple instance problem with axis-parallel rectangles
    • T. G. Dietterich, R. H. Lathrop, and T. Lozano-Perez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 1997, 89(1-2):31-71
    • (1997) Artificial Intelligence , vol.89 , Issue.1-2 , pp. 31-71
    • Dietterich, T.G.1    Lathrop, R.H.2    Lozano-Perez, T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.