Lothar Richter

Affiliations:
  • TU Munich, Department of Informatics, Bioinformatics & Computational Biology


According to our database1, Lothar Richter authored at least 11 papers between 2004 and 2014.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2014
PredictProtein - an open resource for online prediction of protein structural and functional features.
Nucleic Acids Res., 2014

2011
Predicting a small molecule-kinase interaction map: A machine learning approach.
J. Cheminformatics, 2011

2010
SINDBAD and SiQL: Overview, Applications and Future Developments.
Proceedings of the Inductive Databases and Constraint-Based Data Mining., 2010

2009
Finding Relational Associations in HIV Resistance Mutation Data.
Proceedings of the Inductive Logic Programming, 19th International Conference, 2009

2008
SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

An inductive database and query language in the relational model.
Proceedings of the EDBT 2008, 2008

2006
Learning a Predictive Model for Growth Inhibition from the NCI DTP Human Tumor Cell Line Screening Data: Does Gene Expression Make a Difference?
Proceedings of the Biocomputing 2006, 2006

Leveraging Chemical Background Knowledge for the Prediction of Growth Inhibition.
Proceedings of the Sixth IEEE International Symposium on BioInformatics and BioEngineering (BIBE 2006), 2006

2005
Inductive Databases in the Relational Model: The Data as the Bridge.
Proceedings of the Knowledge Discovery in Inductive Databases, 4th International Workshop, 2005

Analyzing microarray data using quantitative association rules.
Proceedings of the ECCB/JBI'05 Proceedings, Fourth European Conference on Computational Biology/Sixth Meeting of the Spanish Bioinformatics Network (Jornadas de BioInformática), Palacio de Congresos, Madrid, Spain, September 28, 2005

2004
Quantitative Association Rules Based on Half-Spaces: An Optimization Approach.
Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 2004


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