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      A novel method for predicting protein subcellular Localization based on pseudo amino acid composition = A novel method for predicting protein subcellular Localization based on pseudo amino acid composition

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      https://www.riss.kr/link?id=A82503505

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      다국어 초록 (Multilingual Abstract)

      In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis ...

      In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis (PCA) is employed to extract essential features. Finally, the Elman Recurrent Neural Network (RNN) is used as a classifier to identify the protein sequences. The results demonstrate that the proposed approach is effective and practical. [BMB reports 2010; 43(10): 670-676]

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      참고문헌 (Reference)

      1 Zeng, Y., "Using the augmented chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach" 259 : 366-372, 2009

      2 Ding, Y. S., "Using chou’s pseudo amino acid composition to predict subcellular localization of apoptosis proteins: an approach with immune genetic algorithm-based ensemble classifier" 29 : 1887-1892, 2008

      3 Imai, K., "SOSUI-GramN: high performance prediction for sub-cellular localization of proteins in Gram-negative bacteria" 2 : 417-421, 2008

      4 Cedano, J., "Relation between amino acid composition and cellular location of proteins" 266 : 594-600, 1997

      5 Hinton, G., "Reducing the dimensionality of data with neural networks" 313 : 504-507, 2006

      6 Gardy, J., "Psortb v. 2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis" 21 : 617-623, 2005

      7 Shen, H. B., "PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition" 373 : 386-388, 2007

      8 Szafron, D., "Proteome Analyst: custom predictions with explanations in a web-based tool for highthroughput proteome annotations" 32 : 365-371, 2004

      9 Jolliffe, I., "Principal component analysis, Second Edition" Springer-Verlag 29-43, 2002

      10 Yeung, K. Y., "Principal component analysis for clustering gene expression data" 17 : 763-774, 2001

      1 Zeng, Y., "Using the augmented chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach" 259 : 366-372, 2009

      2 Ding, Y. S., "Using chou’s pseudo amino acid composition to predict subcellular localization of apoptosis proteins: an approach with immune genetic algorithm-based ensemble classifier" 29 : 1887-1892, 2008

      3 Imai, K., "SOSUI-GramN: high performance prediction for sub-cellular localization of proteins in Gram-negative bacteria" 2 : 417-421, 2008

      4 Cedano, J., "Relation between amino acid composition and cellular location of proteins" 266 : 594-600, 1997

      5 Hinton, G., "Reducing the dimensionality of data with neural networks" 313 : 504-507, 2006

      6 Gardy, J., "Psortb v. 2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis" 21 : 617-623, 2005

      7 Shen, H. B., "PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition" 373 : 386-388, 2007

      8 Szafron, D., "Proteome Analyst: custom predictions with explanations in a web-based tool for highthroughput proteome annotations" 32 : 365-371, 2004

      9 Jolliffe, I., "Principal component analysis, Second Edition" Springer-Verlag 29-43, 2002

      10 Yeung, K. Y., "Principal component analysis for clustering gene expression data" 17 : 763-774, 2001

      11 Chou, K. C., "Prediction of protein cellular attributes using pseudo-amino acid composition" 43 : 246-255, 2001

      12 Yu, C. S., "Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions" 13 : 1402-1406, 2004

      13 Shen, H. B., "Predicting protein subnuclear location with optimized evidence-theoretic k-nearest classifier and pseudo amino acid composition" 337 : 752-756, 2005

      14 Huang, W., "Predicting protein subnuclear localization using GO-amino-acid composition features" 98 : 73-79, 2009

      15 Ma, J. W., "Predicting protein subcellular locations for gram-negative bacteria using neural networks ensemble" 114-120, 2009

      16 Bishop, C., "Pattern recognition and machine learning" Springer 225-284, 2006

      17 Dehling, H., "Parking on a random tree" 133 : 151-157, 2008

      18 Bhasin, M., "PSLpred: prediction of subcellular localization of bacterial proteins" 21 : 2522-2524, 2005

      19 Shen, H. B., "Nuc-PLoc: a new web- server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM" 20 : 561-567, 2007

      20 Yousef, M., "Naive Bayes for microRNA target predictions-machine learning for microRNA targets" 23 : 2987-2992, 2007

      21 Hoffmann, H., "Kernel pca for novelty detection" 40 : 863-874, 2007

      22 Yang J., "Kernel ICA: an alternative formulation and its application to face recognition" 38 : 1784-1787, 2005

      23 Shi, X. H., "Improved elman networks and applications for controlling ultrasonic motors" 18 : 603-629, 2004

      24 Elman, J., "Finding structure in time" 14 : 179-211, 1990

      25 Shen, H. B., "Ensemble classifier for protein fold pattern recognition" 22 : 1717-1722, 2006

      26 Nakashima, H., "Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies" 238 : 54-61, 1994

      27 Witten, I., "Data Mining: practical machine learning tools and techniques, Second Edition" Morgan Kaufmann Publishers 189-283, 2005

      28 Chou, K. C., "Cell-ploc: a package of web servers for predicting subcellular localization of proteins in various organisms" 3 : 153-162, 2008

      29 Chou, K. C., "Cell-PLoc: a package of web servers for predicting subcellular localization of proteins in various organisms" 3 : 153-162, 2008

      30 Sangsoo Kim, "Bioinformatics in the Post-genome Era" 한국생화학분자생물학회 37 (37): 75-82, 2004

      31 Glory, E., "Automated subcellular location determination and high-throughput microscopy" 12 : 7-16, 2007

      32 Glory, E., "Automated subcellular location determination and high-throughput microscopy" 12 : 7-16, 2007

      33 Lei, Z., "An SVM-based system for predicting protein subnuclear localizations" 6 : 291-298, 2005

      34 Shen, H. B., "A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0" 394 : 269-274, 2009

      35 Chou, K. C., "A novel approach to predicting protein structural classes in a (20-1)-d amino acid composition space" 21 : 319-344, 1995

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      학술지등록 한글명 : BMB reports
      외국어명 : BMB reports
      2024 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2021-01-01 평가 등재학술지 선정 (해외등재 학술지 평가) KCI등재
      2020-12-01 평가 등재후보로 하락 (해외등재 학술지 평가) KCI등재후보
      2013-07-17 학술지명변경 한글명 : BMB reports -> BMB Reports
      외국어명 : BMB reports -> BMB Reports
      KCI등재
      2011-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2009-09-21 학회명변경 한글명 : 대한생화학ㆍ분자생물학회 -> 생화학분자생물학회
      영문명 : Korean Society Of Medical Biochemistry And Molecular Biology -> Korean Society Of Biochemistry And Molecular Biology
      KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2007-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2002-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1999-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 2.76 0.5 1.94
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      1.45 1.12 0.646 0.12
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