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Applying the Apriori-based Graph Mining Method to Mutagenesis Data Analysis
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The article is just lists. The reason for each list needs an introduction.
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129:. Mining the molecular graphs directly avoids this problem. So does the
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1786:- Java - Open source - Distributed mining - Benchmark algorithm library
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427:, International Joint Conference on Neural Networks 2005 (IJCNN'05),
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Discriminative Closed Fragment Mining and Perfect Extensions in MoFa
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5th International Workshop on Mining and Learning with Graphs, 2007
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Mining Connected Subgraph Mining Reducing the Number of Candidates
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Frequent Graph Mining and its Application to Molecular Databases
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29:
185:
kernels based on pharmacophores for 3D structure of molecules
1764:
1402:
Xiaohong Wang, Jun Huan, Aaron Smalter, Gerald Lushington,
883:
Data mining the yeast genome in a lazy functional language
367:
Optimal Assignment Kernels For Attributed Molecular Graphs
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Baskin, I. I.; V. A. Palyulin; N. S. Zefirov (1993). "".
1160:
Extension and parallelization of a graph-mining-algorithm
1063:
Mining Fragments with Fuzzy Chains in Molecular Databases
885:, Practical Aspects of Declarative Languages (PADL2003),
283:
Methods based on special architectures of neural networks
866:
A Fast Algorithm for Mining Frequent Connected Subgraphs
98:, or extracting and discovering patterns, as applied to
1061:
T. Meinl, C. Borgelt, M. R. Berthold, M. Philippsen,
1808:- Software for generating fragment-based descriptors
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Finding frequent substructures in chemical compounds
173:
the marginalized graph kernel between labeled graphs
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IEEE Transactions on Knowledge and Data Engineering
901:
IEEE Transactions on Knowledge and Data Engineering
1795:Molecule mining (advanced chemical expert systems)
1216:"SMIREP: predicting chemical activity from SMILES"
864:A. Inokuchi, T. Washio, K. Nishimura, H. Motoda,
693:"Small Molecule Subgraph Detector (SMSD) toolkit"
1775:Molecule mining (basic chemical expert systems)
257:ParMol (contains MoFa, FFSM, gSpan, and Gaston)
1194:gSpan: Graph-Based Substructure Pattern Mining
847:A. Inokuchi, T. Washio, T. Okada, H. Motoda,
118:, which has a long tradition in the field of
8:
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1145:C. Helma, Predictive Toxicology, CRC Press,
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133:which is preferable for vectorial mappings.
868:, IBM Research, Tokyo Research Laboratory,
1081:"Hybrid fragment mining with MoFa and FSG"
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757:"Small Molecule Subgraph Detector (SMSD)"
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425:Assignment Kernels For Chemical Compounds
102:. Since molecules may be represented by
79:Learn how and when to remove this message
27:Data mining for patterns in molecule data
1686:Schölkopf, B., K. Tsuda and J. P. Vert:
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1759:Small Molecule Subgraph Detector (SMSD)
1688:Kernel Methods in Computational Biology
1177:Optimizing gSpan for Molecular Datasets
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176:extensions of the marginalized kernel
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423:H. Fröhlich, J. K. Wegner, A. Zell,
365:H. Fröhlich, J. K. Wegner, A. Zell,
182:graph kernels based on tree patterns
1162:, Friedrich-Alexander-Universität,
1092:. Vol. 5. pp. 4559–4564.
1079:Meinl, T.; Berthold, M. R. (2004).
853:Journal of Computer Aided Chemistry
1697:R.O. Duda, P.E. Hart, D.G. Stork,
25:
1735:Handbook of Molecular Descriptors
1214:Karwath A., Raedt L. D. (2006).
34:
834:L. Dehaspe, H. Toivonen, King,
1719:, Cambridge University Press,
106:, this is strongly related to
1:
1733:R. Todeschini, V. Consonni,
1690:, MIT Press, Cambridge, MA,
562:10.1016/j.neunet.2005.07.009
521:Proceedings of the 21st ICML
192:Maximum common graph methods
1784:master thesis documentation
54:. The specific problem is:
1848:
1427:Doklady Akademii Nauk SSSR
1098:10.1109/ICSMC.2004.1401250
697:Journal of Cheminformatics
168:C++ (and R) implementation
50:to meet Knowledge (XXG)'s
1790:TU München - Kramer group
1701:, John Wiley & Sons,
1641:10.1080/10629360601054313
1551:J. Chem. Inf. Comput. Sci
1488:J. Chem. Inf. Comput. Sci
1447:J. Chem. Inf. Comput. Sci
998:J. Chem. Inf. Comput. Sci
621:10.1007/s10994-008-5086-2
161:Optimal assignment kernel
1800:DMax Chemistry Assistant
772:J. Comput.-Aid. Mol. Des
311:Molecular Query Language
1827:Computational chemistry
1527:10.1023/A:1008368105614
1128:S. Nijssen, J. N. Kok.
792:10.1023/A:1008171016861
228:Molecular query methods
1699:Pattern Classification
881:A. Clare, R. D. King,
710:10.1186/1758-2946-1-12
664:10.1002/qsar.200510009
397:10.1002/qsar.200510135
112:structured data mining
1802:- commercial software
1410:Vol. 11 (Suppl 3):S8
958:10.1109/tkde.2005.127
336:partition coefficient
316:Chemical graph theory
1515:Applied Intelligence
1175:K. Jahn, S. Kramer,
923:10.1109/tkde.2004.33
164:Pharmacophore kernel
131:inverse QSAR problem
61:improve this article
1500:10.1021/ci00024a001
1387:Proc. of ECML--PKDD
1316:J. Chem. Inf. Model
784:2001JCAMD..15..173K
468:2006q.bio.....3006M
431:, 913-918. CiteSeer
1408:BMC Bioinformatics
855:, 2001;, 2, 87-92.
206:Subgraph Detector
94:is the process of
1770:Overview for 2006
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1459:10.1021/ci940128y
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1279:10.1021/mp060034z
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127:molecule topology
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