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Molecules

On this page algorithms are listed for which an article was published focussing at molecular applications only. Many algorithms are also listed in other sections.

AGM Mutagenesis Publication Year: 2001
Purpose: enumerate all frequent induced submolecules in a set of mutagenesis molecules, using a graph encoding and subgraph isomorphism.

  1. Akihiro Inokuchi, Takashi Washio, Takashi Okada, Hiroshi Motoda. Applying the Apriori-based Graph Mining Method to Mutagenesis Data Analysis. In: Journal of Computer Aided Chemistry, pages 87-92, 2001.
Gaston Publication Year: 2004
Purpose: enumerate all frequent induced submolecules in a set of molecules, using a graph encoding and subgraph isomorphism, with details about pre- and postprocessing.
  1. Siegfried Nijssen, Joost N. Kok. Frequent Graph Mining and its Application to Molecular Databases. In: Proceedings of the 2004 IEEE Conference on Systems, Man & Cybernetics (SMC2004), 2004.
MolFea Publication Years: 2001-2002
Purpose: enumerate linear fragments in molecules that satisfy constraints specified by the user, using version spaces.
  1. WEBPAGE
  2. Stefan Kramer, Luc De Raedt. Feature Construction with Version Spaces for Biochemical Applications. In: Proceedings of the 18th International Conference on Machine Learning (ICML2001), 2001.
  3. Stefan Kramer, Luc De Raedt, Christoph Helma. Molecular Feature Mining in HIV data. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2001), 2001.
  4. Luc De Raedt, Stefan Kramer. The level-wise version space algorithm and its application to molecular fragment finding. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI2001), 2001.
  5. Christoph Helma, Stefan Kramer, Luc De Raedt. The Molecular Feature Miner MolFea. In: Proceedings of the Beilstein-Institut Workshop, 2002.
  6. (Strongly related) Luc De Raedt, Manfred Jager, Sau Dan Lee, Heikki Mannila. A Theory of Inductive Query Answering. In: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM2002), 2002.
MoFa(MoSS) Publication Years: 2002-2004
Purpose: enumerate all frequent fragments in molecules.
  1. WEBPAGE
  2. Christian Borgelt, Michael R. Berthold. Mining Molecular Fragments: Finding Relevant Substructures of Molecules. In: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM2002), 2002.
  3. Heiko Hofer, Christian Borgelt, Michael R. Berthold. Large Scale Mining of Molecular Fragments with Wildcards. In: Advances in Intelligent Data Analysis V, pages 380-389, 2003.
  4. Thorsen Meinl, Christian Borgelt, Michael R. Berthold, Michael Philippsen. Mining Fragments with Fuzzy Chains in Molecular Databases. In: Second International Workshop on Mining Graphs, Trees and Sequences (MGTS2004), 2004.
  5. Christian Borgelt, Thorsten Meinl, Michael R. Berthold. Advanced Pruning Strategies to Speed Up Mining Closed Molecular Fragments. In: Proceedings of the 2004 IEEE Conference on Systems, Man & Cybernetics (SMC2004), 2004.
  6. Thorsten Meinl, Christian Borgelt, Michael R. Berthold. Discriminative Closed Fragment Mining and Pefect Extensions in MoFa. In: Proceedings of the Second Starting AI Researchers' Symposium (STAIRS 2004), 2004.
  7. Thorsten Meinl, Michael R. Berthold. Hybrid Fragment Mining with MoFa and FSG. In: Proceedings of the 2004 IEEE Conference on Systems, Man & Cybernetics (SMC2004), 2004.
PolyFARM Publication Years: 2003
Purpose: enumerate all frequent descriptions of yeast genomes, using an encoding in Prolog.
  1. WEBPAGE
  2. Amanda Clare, Ross D. King. Data mining the yeast genome in a lazy functional language. In: Practical Aspects of Declarative Languages (PADL2003), 2003.
DMax Publication Year: 2006
Purpose: Classification of molecules through ILP
  1. Howard Y. Ando, Luc Dehaspe, Walter Luyten, Elke van Craenenbroeck, Henk Vandecasteele, Luc Van Meervelt. Discovering H-Bonding Rules in Crystals with Inductive Logic Programming. In: Molecular Pharmaceutics, 2006.
Warmr Mutagenesis Publication Years: 1998-2001
Purpose: enumerate all frequent submolecules in a set of mutagenesis molecules, using an encoding in Prolog and theta subsumption (subgraph homomorphism).
  1. WEBPAGE
  2. Luc Dehaspe, Hannu Toivonen, Ross D. King. Finding frequent substructures in chemical compounds. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD1998), pages 30-36, 1998.
  3. Ross D. King, Ashwin Srinivasan, Luc Dehaspe. Warmr: A Data Mining Tool for Chemical Data. In: Journal of Computer-Aided Molecular Design 15, pages 173-181, 2001.