메뉴 건너뛰기




Volumn 8, Issue 12, 2013, Pages

Efficient modeling and active learning discovery of biological responses

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COMPUTER MODEL; COMPUTER PREDICTION; COMPUTER SIMULATION; DRUG SCREENING; DRUG TARGETING; EXPERIMENTATION; GENE EXPRESSION; LEARNING ALGORITHM; PROBABILITY;

EID: 84892906484     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0083996     Document Type: Article
Times cited : (28)

References (27)
  • 2
    • 0037079054 scopus 로고    scopus 로고
    • Computational systems biology
    • doi:10.1038/nature01254. PubMed: 12432404
    • Kitano H (2002) Computational systems biology. Nature 420: 206-210. doi:10.1038/nature01254. PubMed: 12432404.
    • (2002) Nature , vol.420 , pp. 206-210
    • Kitano, H.1
  • 3
    • 5044227742 scopus 로고    scopus 로고
    • The evolution of molecular biology into systems biology
    • doi:10.1038/nbt1020. PubMed: 15470464
    • Westerhoff HV, Palsson BO (2004) The evolution of molecular biology into systems biology. Nat Biotechnol 22: 1249-1252. doi:10.1038/nbt1020. PubMed: 15470464.
    • (2004) Nat Biotechnol , vol.22 , pp. 1249-1252
    • Westerhoff, H.V.1    Palsson, B.O.2
  • 4
    • 84862510972 scopus 로고    scopus 로고
    • Large-scale prediction and testing of drug activity on side-effect targets
    • PubMed: 22722194
    • Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J et al. (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature 486: 361-367. PubMed: 22722194.
    • (2012) Nature , vol.486 , pp. 361-367
    • Lounkine, E.1    Keiser, M.J.2    Whitebread, S.3    Mikhailov, D.4    Hamon, J.5
  • 5
    • 77956477049 scopus 로고    scopus 로고
    • Drug profiling: Knowing where it hits
    • doi:10.1016/j.drudis.2010.06.006. PubMed: 20601095
    • Merino A, Bronowska AK, Jackson DB, Cahill DJ (2010) Drug profiling: knowing where it hits. Drug Discov Today 15: 749-756. doi:10.1016/j.drudis.2010. 06.006. PubMed: 20601095.
    • (2010) Drug Discov Today , vol.15 , pp. 749-756
    • Merino, A.1    Bronowska, A.K.2    Jackson, D.B.3    Cahill, D.J.4
  • 6
    • 79956124247 scopus 로고    scopus 로고
    • An active role for machine learning in drug development
    • doi:10.1038/nchembio.576. PubMed: 21587249
    • Murphy RF (2011) An active role for machine learning in drug development. Nat Chem Biol 7: 327-330. doi:10.1038/nchembio.576. PubMed: 21587249.
    • (2011) Nat Chem Biol , vol.7 , pp. 327-330
    • Murphy, R.F.1
  • 8
    • 9444277556 scopus 로고    scopus 로고
    • PAC Bounds for Multi-armed Bandit and Markov Decision Processes
    • J KivinenR Sloan. Springer Berlin / Heidelberg
    • Even-Dar E, Mannor S, Mansour Y (2002) PAC Bounds for Multi-armed Bandit and Markov Decision Processes. In: J KivinenR Sloan. Computational Learning Theory. Springer Berlin / Heidelberg. pp. 193-209.
    • (2002) Computational Learning Theory , pp. 193-209
    • Even-Dar, E.1    Mannor, S.2    Mansour, Y.3
  • 9
    • 1242285091 scopus 로고    scopus 로고
    • Active Sampling for Class Probability Estimation and Ranking
    • doi: 10.1023/B:MACH.0000011806.12374.c3
    • Saar-Tsechansky M, Provost F (2004) Active Sampling for Class Probability Estimation and Ranking. Mach Learn 54: 153-178. doi: 10.1023/B:MACH.0000011806. 12374.c3.
    • (2004) Mach Learn , vol.54 , pp. 153-178
    • Saar-Tsechansky, M.1    Provost, F.2
  • 11
    • 70349671213 scopus 로고    scopus 로고
    • Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning
    • PubMed: 19756158
    • Danziger SA, Baronio R, Ho L, Hall L, Salmon K et al. (2009) Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning. PLoS Comput Biol 5: e1000498. PubMed: 19756158.
    • (2009) PLoS Comput Biol , vol.5
    • Danziger, S.A.1    Baronio, R.2    Ho, L.3    Hall, L.4    Salmon, K.5
  • 12
    • 0037365194 scopus 로고    scopus 로고
    • Active learning with support vector machines in the drug discovery process
    • doi:10.1021/ci025620t. PubMed: 12653536
    • Warmuth MK, Liao J, Rätsch G, Mathieson M, Putta S et al. (2003) Active learning with support vector machines in the drug discovery process. J Chem Inf Comput Sci 43: 667-673. doi:10.1021/ci025620t. PubMed: 12653536.
    • (2003) J Chem Inf Comput Sci , vol.43 , pp. 667-673
    • Warmuth, M.K.1    Liao, J.2    Rätsch, G.3    Mathieson, M.4    Putta, S.5
  • 13
    • 44449101219 scopus 로고    scopus 로고
    • Virtual screening system for finding structurally diverse hits by active learning
    • doi:10.1021/ci700085q. PubMed: 18351729
    • Fujiwara Y, Yamashita Y, Osoda T, Asogawa M, Fukushima C et al. (2008) Virtual screening system for finding structurally diverse hits by active learning. J Chem Inf Model 48: 930-940. doi:10.1021/ci700085q. PubMed: 18351729.
    • (2008) J Chem Inf Model , vol.48 , pp. 930-940
    • Fujiwara, Y.1    Yamashita, Y.2    Osoda, T.3    Asogawa, M.4    Fukushima, C.5
  • 14
    • 10044229345 scopus 로고    scopus 로고
    • Active learning with support vector machine applied to gene expression data for cancer classification
    • doi:10.1021/ci049810a. PubMed: 15554662
    • Liu Y (2004) Active learning with support vector machine applied to gene expression data for cancer classification. J Chem Inf Comput Sci 44: 1936-1941. doi:10.1021/ci049810a. PubMed: 15554662.
    • (2004) J Chem Inf Comput Sci , vol.44 , pp. 1936-1941
    • Liu, Y.1
  • 15
    • 75149162532 scopus 로고    scopus 로고
    • Active learning for human protein-protein interaction prediction
    • doi:10.1186/1471-2105-11-S1-S57. PubMed: 20122232
    • Mohamed TP, Carbonell JG, Ganapathiraju MK (2010) Active learning for human protein-protein interaction prediction. BMC Bioinformatics 11 Suppl 1: S57. doi:10.1186/1471-2105-11-S1-S57. PubMed: 20122232.
    • (2010) BMC Bioinformatics , vol.11 , Issue.SUPPL. 1
    • Mohamed, T.P.1    Carbonell, J.G.2    Ganapathiraju, M.K.3
  • 17
  • 19
    • 33749335282 scopus 로고    scopus 로고
    • The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease
    • doi: 10.1126/science.1132939. PubMed: 17008526
    • Lamb J, Crawford ED, Peck D, Modell JW, Blat IC et al. (2006) The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science 313: 1929-1935. doi: 10.1126/science.1132939. PubMed: 17008526.
    • (2006) Science , vol.313 , pp. 1929-1935
    • Lamb, J.1    Crawford, E.D.2    Peck, D.3    Modell, J.W.4    Blat, I.C.5
  • 20
    • 84892371238 scopus 로고    scopus 로고
    • Broad Institute
    • (2013) Connectivity Map. Broad Institute.
    • (2013) Connectivity Map
  • 21
    • 78249285918 scopus 로고    scopus 로고
    • Lower Bounds on Learning Random Structures with Statistical Queries
    • M HutterF StephanV VovkT Zeugmann. Springer Berlin / Heidelberg
    • Angluin D, Eisenstat D, Kontorovich L, Reyzin L (2010) Lower Bounds on Learning Random Structures with Statistical Queries. In: M HutterF StephanV VovkT Zeugmann. Algorithmic Learning Theory. Springer Berlin / Heidelberg. pp. 194-208.
    • (2010) Algorithmic Learning Theory , pp. 194-208
    • Angluin, D.1    Eisenstat, D.2    Kontorovich, L.3    Reyzin, L.4
  • 22
    • 0242712751 scopus 로고    scopus 로고
    • Global Optimization and Constraint Satisfaction: The Branch-and-Reduce Approach
    • Sahinidis NV (2003) Global Optimization and Constraint Satisfaction: The Branch-and-Reduce Approach. Lect Notes. Comp Sci 2861: 1-16.
    • (2003) Lect Notes. Comp Sci , vol.2861 , pp. 1-16
    • Sahinidis, N.V.1
  • 23
    • 0346856929 scopus 로고    scopus 로고
    • Characterizing selection bias using experimental data
    • doi:10.2307/2999630
    • Heckman J, Ichimura H, Smith J, Todd P (1998) Characterizing selection bias using experimental data. Econometrica 66: 1017-1098. doi:10.2307/2999630.
    • (1998) Econometrica , vol.66 , pp. 1017-1098
    • Heckman, J.1    Ichimura, H.2    Smith, J.3    Todd, P.4
  • 24
    • 71049116435 scopus 로고    scopus 로고
    • Exact Matrix Completion via Convex
    • doi:10.1007/s10208-009-9045-5
    • Candes EJ, Recht B (2009) Exact Matrix Completion via Convex. Optimization - Found Comput Math 9: 717-772. doi:10.1007/s10208-009-9045-5.
    • (2009) Optimization - Found Comput Math , vol.9 , pp. 717-772
    • Candes, E.J.1    Recht, B.2
  • 25
    • 3242717928 scopus 로고    scopus 로고
    • Recovering the missing components in a large noisy low-rank matrix: Application to SFM
    • doi: 10.1109/TPAMI.2004.52
    • Chen P, Suter D (2004) Recovering the missing components in a large noisy low-rank matrix: application to SFM. Pattern Analysis and Machine Intelligence, IEEE Transactions On 26: 1051-1063. doi: 10.1109/TPAMI.2004.52.
    • (2004) Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.26 , pp. 1051-1063
    • Chen, P.1    Suter, D.2
  • 26
    • 0034339545 scopus 로고    scopus 로고
    • Multiple imputation for missing data: A cautionary tale
    • doi: 10.1177/0049124100028003003
    • Allison P (2000) Multiple imputation for missing data: A cautionary tale. Sociological Methods and Research 28: 301-309. doi: 10.1177/ 0049124100028003003.
    • (2000) Sociological Methods and Research , vol.28 , pp. 301-309
    • Allison, P.1
  • 27
    • 0017133178 scopus 로고
    • Inference and missing data
    • doi:10.1093/biomet/63.3.581
    • Rubin DB (1976) Inference and missing data. Biometrika 63: 581-592. doi:10.1093/biomet/63.3.581.
    • (1976) Biometrika , vol.63 , pp. 581-592
    • Rubin, D.B.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.