메뉴 건너뛰기




Volumn 45, Issue 2-3, 2009, Pages 91-96

Computational intelligence and machine learning in bioinformatics

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; COMPUTATIONAL INTELLIGENCE; DIMERIZATION; DRUG PROTEIN BINDING; EDITORIAL; ENZYME ACTIVE SITE; ENZYME INHIBITION; FUZZY SYSTEM; GENE EXPRESSION PROFILING; GENE SEQUENCE; GENOME ANALYSIS; GENOMICS; HUMAN; HYPOTHESIS; INFORMATION RETRIEVAL; MACHINE LEARNING; MASS SPECTROMETRY; MATHEMATICAL MODEL; NONHUMAN; PRIORITY JOURNAL; PROTEIN EXPRESSION; PROTEIN STRUCTURE; PROTEOMICS; SEQUENCE ALIGNMENT; STRUCTURE ANALYSIS; SUPPORT VECTOR MACHINE; TECHNIQUE; TRANSCRIPTOMICS;

EID: 61449205119     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2008.08.014     Document Type: Editorial
Times cited : (13)

References (51)
  • 1
    • 61449134052 scopus 로고    scopus 로고
    • Goble C, Stevens R. State of the nation in data integration for bioinformatics. Journal of Biomedical Informatics, available on line at http://www.sciencedirect.com; in press [accessed 5.08.08].
    • Goble C, Stevens R. State of the nation in data integration for bioinformatics. Journal of Biomedical Informatics, available on line at http://www.sciencedirect.com; in press [accessed 5.08.08].
  • 3
    • 61449179156 scopus 로고    scopus 로고
    • editors. Computational intelligence techniques in bioinformatics (special issue)
    • Cios K, Mamitsuka H, Nagashina T, editors. Computational intelligence techniques in bioinformatics (special issue). Artificial Intelligence in Medicine 2005;35(1-2).
    • (2005) Artificial Intelligence in Medicine , vol.35 , Issue.1-2
    • Cios, K.1    Mamitsuka, H.2    Nagashina, T.3
  • 5
    • 11144273669 scopus 로고
    • The perceptron, a probabilistic model for information storage and organization in the brain
    • Rosenblatt F. The perceptron, a probabilistic model for information storage and organization in the brain. Psychological Review 65 (1958) 386-408
    • (1958) Psychological Review , vol.65 , pp. 386-408
    • Rosenblatt, F.1
  • 6
    • 0020480251 scopus 로고
    • Use of the perceptron algorithm to distinguish translation initiation sites in E. coli
    • Stormo G., Scheider T., Gold L., and Ehrenfeuch A. Use of the perceptron algorithm to distinguish translation initiation sites in E. coli. Nucleic Acid Research 10 (1986) 2997-3011
    • (1986) Nucleic Acid Research , vol.10 , pp. 2997-3011
    • Stormo, G.1    Scheider, T.2    Gold, L.3    Ehrenfeuch, A.4
  • 9
    • 2942521197 scopus 로고    scopus 로고
    • Recent advances in gene structure prediction
    • Brent M., and Guigo R. Recent advances in gene structure prediction. Current Opinion in Structural Biology 14 3 (2004) 264-272
    • (2004) Current Opinion in Structural Biology , vol.14 , Issue.3 , pp. 264-272
    • Brent, M.1    Guigo, R.2
  • 12
    • 34547282694 scopus 로고    scopus 로고
    • Machine learning for regulatory analysis and transcription factor target prediction in yeast
    • Holloway D., Kon M., and DeLisi C. Machine learning for regulatory analysis and transcription factor target prediction in yeast. Systems and Synthetic Biology 1 1 (2007) 25-46
    • (2007) Systems and Synthetic Biology , vol.1 , Issue.1 , pp. 25-46
    • Holloway, D.1    Kon, M.2    DeLisi, C.3
  • 13
    • 3543106033 scopus 로고    scopus 로고
    • Genome-wide identification of genes likely to be involved in human genetic diseases
    • Lopez-Bigas N., and Ouzounis C. Genome-wide identification of genes likely to be involved in human genetic diseases. Nucleic Acid Research 32 10 (2004) 3108-3114
    • (2004) Nucleic Acid Research , vol.32 , Issue.10 , pp. 3108-3114
    • Lopez-Bigas, N.1    Ouzounis, C.2
  • 14
    • 19544392545 scopus 로고    scopus 로고
    • Prediction of the phenotypic effects of non synonymous single nucleotide polymorphisms using structural and evolutionary information
    • Bao L., and Cui Y. Prediction of the phenotypic effects of non synonymous single nucleotide polymorphisms using structural and evolutionary information. Bioinformatics 21 5 (2005) 2185-2190
    • (2005) Bioinformatics , vol.21 , Issue.5 , pp. 2185-2190
    • Bao, L.1    Cui, Y.2
  • 15
    • 0036920495 scopus 로고    scopus 로고
    • Prediction of the phenotypic effects of non synonymous single nucleotide polymorphisms using structural and evolutionary information
    • Fogel G., Porto W., and Weekes D. Prediction of the phenotypic effects of non synonymous single nucleotide polymorphisms using structural and evolutionary information. Nucleic Acid Research 30 23 (2002) 5310-5317
    • (2002) Nucleic Acid Research , vol.30 , Issue.23 , pp. 5310-5317
    • Fogel, G.1    Porto, W.2    Weekes, D.3
  • 16
    • 13844302126 scopus 로고    scopus 로고
    • High density linkage disequilibrium mapping using models of haplotype block variations
    • Greenspan G., and Geiger D. High density linkage disequilibrium mapping using models of haplotype block variations. Bioinformatics 20 S1 (2004) 137-144
    • (2004) Bioinformatics , vol.20 , Issue.SUPPL.1 , pp. 137-144
    • Greenspan, G.1    Geiger, D.2
  • 17
    • 2942541354 scopus 로고    scopus 로고
    • Feature selection for splice site prediction: a new method using EDA-based feature ranking
    • Saeys Y., Degroeve S., Aeyels D., Rouze P., and Van de Peer Y. Feature selection for splice site prediction: a new method using EDA-based feature ranking. BMC Bioinformatics 5 64 (2004)
    • (2004) BMC Bioinformatics , vol.5 , Issue.64
    • Saeys, Y.1    Degroeve, S.2    Aeyels, D.3    Rouze, P.4    Van de Peer, Y.5
  • 18
    • 0642368712 scopus 로고    scopus 로고
    • Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases
    • Ritchie M., White B.C., Parker J.S., Hahn L.W., and Moore J.H. Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics 4 28 (2003)
    • (2003) BMC Bioinformatics , vol.4 , Issue.28
    • Ritchie, M.1    White, B.C.2    Parker, J.S.3    Hahn, L.W.4    Moore, J.H.5
  • 20
    • 39149140433 scopus 로고    scopus 로고
    • Tmbpro: secondary structure, beta-contact and tertiary structure prediction of transmembrane beta-barrel proteins
    • Randall A., Cheng J., Sweredoski M., and Baldi P. Tmbpro: secondary structure, beta-contact and tertiary structure prediction of transmembrane beta-barrel proteins. Bioinformatics 24 4 (2008) 513-520
    • (2008) Bioinformatics , vol.24 , Issue.4 , pp. 513-520
    • Randall, A.1    Cheng, J.2    Sweredoski, M.3    Baldi, P.4
  • 22
    • 33645323768 scopus 로고    scopus 로고
    • Hierarchical multi-label prediction of gene function
    • Barutcuoglu Z., Schapire R., and Troyanskaya O. Hierarchical multi-label prediction of gene function. Bioinformatics 22 7 (2006) 830-836
    • (2006) Bioinformatics , vol.22 , Issue.7 , pp. 830-836
    • Barutcuoglu, Z.1    Schapire, R.2    Troyanskaya, O.3
  • 23
    • 0036300562 scopus 로고    scopus 로고
    • A Bayesian network model for protein fold and remote homologue recognition
    • Raval A., Ghahramani Z., and Wild D. A Bayesian network model for protein fold and remote homologue recognition. Bioinformatics 18 6 (2002) 788-801
    • (2002) Bioinformatics , vol.18 , Issue.6 , pp. 788-801
    • Raval, A.1    Ghahramani, Z.2    Wild, D.3
  • 24
    • 0041719954 scopus 로고    scopus 로고
    • Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners
    • Pollastri G., and Baldi P. Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners. Bioinformatics 18 S1 (2002) 62-70
    • (2002) Bioinformatics , vol.18 , Issue.SUPPL.1 , pp. 62-70
    • Pollastri, G.1    Baldi, P.2
  • 25
    • 0347093598 scopus 로고    scopus 로고
    • Prediction of protein subcellular localization using fuzzy k-nn method
    • Huang Y., and Li Y. Prediction of protein subcellular localization using fuzzy k-nn method. Bioinformatics 20 1 (2004) 21-28
    • (2004) Bioinformatics , vol.20 , Issue.1 , pp. 21-28
    • Huang, Y.1    Li, Y.2
  • 26
    • 29244448340 scopus 로고    scopus 로고
    • Microarray data analysis: from disarray to consolidation and consensus
    • Allison D., Cui X., Page G., and Sabripour M. Microarray data analysis: from disarray to consolidation and consensus. Nature Review Genetics 7 1 (2006) 55-65
    • (2006) Nature Review Genetics , vol.7 , Issue.1 , pp. 55-65
    • Allison, D.1    Cui, X.2    Page, G.3    Sabripour, M.4
  • 27
    • 33744513688 scopus 로고    scopus 로고
    • Microarray analysis in drug discovery and clinical applications
    • Wang S., and Cheng Q. Microarray analysis in drug discovery and clinical applications. Methods in Molecular Biology 316 (2006) 49-65
    • (2006) Methods in Molecular Biology , vol.316 , pp. 49-65
    • Wang, S.1    Cheng, Q.2
  • 28
    • 33750946376 scopus 로고    scopus 로고
    • Functional interpretation of microarray experiments
    • Dopazo J. Functional interpretation of microarray experiments. OMICS 3 10 (2006)
    • (2006) OMICS , vol.3 , Issue.10
    • Dopazo, J.1
  • 29
    • 0036489046 scopus 로고    scopus 로고
    • Comparison of discrimination methods for the classification of tumors using gene expression data
    • Dudoit S., Fridlyand J., and Speed T. Comparison of discrimination methods for the classification of tumors using gene expression data. JASA 97 457 (2002) 77-87
    • (2002) JASA , vol.97 , Issue.457 , pp. 77-87
    • Dudoit, S.1    Fridlyand, J.2    Speed, T.3
  • 30
    • 37649001659 scopus 로고    scopus 로고
    • An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer
    • Lee Z. An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer. Artificial Intelligence in Medicine 42 1 (2008) 81-93
    • (2008) Artificial Intelligence in Medicine , vol.42 , Issue.1 , pp. 81-93
    • Lee, Z.1
  • 31
    • 25144456056 scopus 로고    scopus 로고
    • Computational cluster validation in post-genomic data analysis
    • Handl J., Knowles J., and Kell D. Computational cluster validation in post-genomic data analysis. Bioinformatics 21 15 (2005) 3201-3215
    • (2005) Bioinformatics , vol.21 , Issue.15 , pp. 3201-3215
    • Handl, J.1    Knowles, J.2    Kell, D.3
  • 32
    • 0036500993 scopus 로고    scopus 로고
    • Systems biology: a brief overview
    • Kitano H. Systems biology: a brief overview. Science 295 5560 (2002) 1662-1664
    • (2002) Science , vol.295 , Issue.5560 , pp. 1662-1664
    • Kitano, H.1
  • 34
    • 0842288337 scopus 로고    scopus 로고
    • Inferring cellular networks using probabilistic graphical models
    • Friedman N. Inferring cellular networks using probabilistic graphical models. Science 303 (2004) 799-805
    • (2004) Science , vol.303 , pp. 799-805
    • Friedman, N.1
  • 35
    • 13244268322 scopus 로고    scopus 로고
    • A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases
    • Green M., and Karp P. A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinformatics 5 76 (2004)
    • (2004) BMC Bioinformatics , vol.5 , Issue.76
    • Green, M.1    Karp, P.2
  • 37
    • 61449222042 scopus 로고    scopus 로고
    • Formulating and testing hypothesis in functional genomics
    • Dopazo J. Formulating and testing hypothesis in functional genomics. Artificial Intelligence in Medicine 45 (2009) 97-107
    • (2009) Artificial Intelligence in Medicine , vol.45 , pp. 97-107
    • Dopazo, J.1
  • 38
    • 0034069495 scopus 로고    scopus 로고
    • Gene ontology: tool for the unification of biology
    • The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nature Genetics 25 (2000) 25-29
    • (2000) Nature Genetics , vol.25 , pp. 25-29
    • The Gene Ontology Consortium1
  • 39
    • 0033982936 scopus 로고    scopus 로고
    • Kegg: Kyoto encyclopedia of genes and genomes
    • Kanehisa M., and Goto S. Kegg: Kyoto encyclopedia of genes and genomes. Nucleic Acid Research 28 (2000) 27-30
    • (2000) Nucleic Acid Research , vol.28 , pp. 27-30
    • Kanehisa, M.1    Goto, S.2
  • 40
    • 61449229277 scopus 로고    scopus 로고
    • Virtual genetic coding and time series analysis for alternative splicing prediction in C Elegans
    • Ceccarelli M., and Maratea A. Virtual genetic coding and time series analysis for alternative splicing prediction in C Elegans. Artificial Intelligence in Medicine 45 (2009) 109-115
    • (2009) Artificial Intelligence in Medicine , vol.45 , pp. 109-115
    • Ceccarelli, M.1    Maratea, A.2
  • 41
    • 61449195299 scopus 로고    scopus 로고
    • Detecting conserved coding genomic regions through signal processing of nucleotide substitution patterns
    • Re M., and Pavesi G. Detecting conserved coding genomic regions through signal processing of nucleotide substitution patterns. Artificial Intelligence in Medicine 45 (2009) 117-123
    • (2009) Artificial Intelligence in Medicine , vol.45 , pp. 117-123
    • Re, M.1    Pavesi, G.2
  • 42
    • 61449137850 scopus 로고    scopus 로고
    • Modeling adaptive kernels from probabilistic phylogenic trees
    • Nicotra L., and Micheli A. Modeling adaptive kernels from probabilistic phylogenic trees. Artificial Intelligence in Medicine 45 (2009) 125-134
    • (2009) Artificial Intelligence in Medicine , vol.45 , pp. 125-134
    • Nicotra, L.1    Micheli, A.2
  • 44
    • 61449159874 scopus 로고    scopus 로고
    • Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors
    • Okun O., and Priisalu H. Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors. Artificial Intelligence in Medicine 45 (2009) 151-162
    • (2009) Artificial Intelligence in Medicine , vol.45 , pp. 151-162
    • Okun, O.1    Priisalu, H.2
  • 45
    • 61449090610 scopus 로고    scopus 로고
    • Evaluating gene selection methods through artificial and real gene expression data
    • Muselli M., Costacurta M., and Ruffino F. Evaluating gene selection methods through artificial and real gene expression data. Artificial Intelligence in Medicine 45 (2009) 163-171
    • (2009) Artificial Intelligence in Medicine , vol.45 , pp. 163-171
    • Muselli, M.1    Costacurta, M.2    Ruffino, F.3
  • 46
    • 61449214257 scopus 로고    scopus 로고
    • Fuzzy ensemble clustering based on random projections for DNA microarray data analysis
    • Avogadri R., and Valentini G. Fuzzy ensemble clustering based on random projections for DNA microarray data analysis. Artificial Intelligence in Medicine 45 (2009) 173-183
    • (2009) Artificial Intelligence in Medicine , vol.45 , pp. 173-183
    • Avogadri, R.1    Valentini, G.2
  • 48
    • 61449154481 scopus 로고    scopus 로고
    • Computational proteomics analysis of binding mechanisms and molecular signatures of the HIV-1 protease drugs
    • Verkhivker G. Computational proteomics analysis of binding mechanisms and molecular signatures of the HIV-1 protease drugs. Artificial Intelligence in Medicine 45 (2009) 197-206
    • (2009) Artificial Intelligence in Medicine , vol.45 , pp. 197-206
    • Verkhivker, G.1
  • 49
    • 61449254409 scopus 로고    scopus 로고
    • Adaptive bandwidth selection for biomarker discovery in mass spectrometry
    • Fischer B., Roth V., and Buhmann J. Adaptive bandwidth selection for biomarker discovery in mass spectrometry. Artificial Intelligence in Medicine 45 (2009) 207-214
    • (2009) Artificial Intelligence in Medicine , vol.45 , pp. 207-214
    • Fischer, B.1    Roth, V.2    Buhmann, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.