Machine Learning and Data Mining
Andreas Maunz received his diploma (german equivalent to M. Sc.) in computer science from Albert-Ludwigs University Freiburg in 2007, and received his doctoral degree from Technical University Munich in 2013. He is currently a research informatics scientist at Roche‘s main site in Basel (CH).
Research Interests: Data Mining and Machine Learning (e.g. Clustering, large classifier systems, matrix factorization), Structured Data Mining (e.g. association rules, graph mining), and Inferential Statistics (e.g. models of biological processes with quantification of uncertainty).
Current Research: Modeling and simulation in anti-cancer drug combinations, scientific workflow systems in Data Warehouse environments, Data Mining interfaces for mutational data.
Systems level: Linux (Shell, Daemons), full virtualization and paravirtualization (kvm), distributed computing.
- 2011: Article in Special Issue of „Machine Learning” (Springer-Verlag), see publications
- 2007: Appearance among the top third of Freiburg CS graduates in 2007.
- 2003 – 2007: Scholarship granted by Hans-Böckler Stiftung.
Research Project Activities
International research projects:
- 2011 – 2013: Toxicological Risk Assessment in the food industry.
Research collaboration with Nestlé SA (Vevey, Switzerland) for
structure-based modeling chronic toxicity and carcinogenicity (TD50).
Activity: Responsible for leading the complete modeling in the project
using web technology and statistical learners in R.
Publication: submitted and currently under review.
- 2008 – 2011: Modelling of adverse chemical properties in pre-clinical screening.
Research Project “OpenTox” of the Seventh Framework Programme of the
European Union – development of an interoperable programming frameworks
for predictive toxicology.
Activity: design, testing and implementation of data mining and
prediction modules using Ruby web services and diverse modelling algorithms.
- 2005 – 2010 Research Project “Sens-it-iv” of the Seventh Framework Programme of the European Union – Development of “in vitro” Alternatives to Animal Testing in the risk assessment of allergy pathogens.
Activity: implementation and operation of an inductive database for experimental data, using Ruby on Rails and statistical toolboxes.
National research projects:
- 2011 – 2013: Formation of chemical categories for the prediction of repeated-dose toxicity: Project “REACH” of the German Federal Ministry of Education and Research
Activity: Implementation of a statistical toolbox using R.
Dissemination: See workshops
- Machine Learning Journal (2014)
- KDD Conference (2014, 2011)ECML/PKDD Conference (2010)
- ICDM Conference (2010)
- Journal of Intelligent Information Systems (2012)
- Journal of Chemical Information and Modelling (2011)
Reliable and user friendly computer-based models in drug discovery and risk management are high priority for the pharmaceutical industry, especially with regard to endpoints which are not fully elucidated.
This constitutes a challenging field for computer scientists due to large amounts of structured data. There are many possible representations for chemicals and their properties that lend themselves to data mining tasks. Creating inductive databases implies solutions to sophisticated problems, such as graph mining, pattern recognition, and multivariate modeling with dimensionality reduction.
- LibBBRC for mining backbone refinement class representatives [API, Supporting Website]
- LibLAST for latent structure pattern mining, [API, Supporting Website]
- Ready-to-use appliance with both BBRC and LAST, based on VirtualBox.
- Multi-View Clustering (Bickel und Scheffer, ICDM 2004) in R (also available from CRAN).
- Maunz A, Gilsdorf M, Fournier S, Blumenröhr C, Horstmöller R, Schmiedle J. “Visual Analysis of Gene Interaction Networks using Hive Plots”, VIZBI 2015, the 6th international meeting on Visualizing Biological Data, Broad Institute of MIT and Harvard (March 25-27 2015)
- Vorgrimmler D, Rautenberg M, Gütlein, M, Maunz A, Gebele D, and Helma C. “Lazar – A Modular Predictive Toxicology Framework”, OpenTox Euro 2013 – Innovation in Predictive Toxicology, Johannes Gutenberg University of Mainz (30 September – 2 October 2013)
- Helma C, Maunz A: “Vorstellung des Tools Lazar (Lazy Structure-Activity Relationships)”, Read-Across und Grouping zur Füllung von Datenlücken unter REACH, DGPT Jahrestagung 2013, Halle/Saale (05. – 07. März 2013)
- Maunz A: “Elaborate Graph Mining: Exploiting Structural Invariants and Latent Information in Graph Databases to Predict REACH-Relevant Endpoints”, presented at the OpenTox 2011 conference, Summer 2011, Technical University Munich (9-13 August 2011)
- Maunz A, Helma C, Cramer T, and Kramer S. “Latent Structure Pattern Mining”, presented at the eCheminfo Community of Practice InterAction Meeting, Summer 2010, Oxford University, Oxford, UK (01-05 August 2010).
- Maunz A, Helma C: “New Lazar Developments”, presented at the eCheminfo Community of Practice InterAction Meeting, Autumn 2008, Bryn Mawr College, Philadelphia (13-17 October 2008).
- Maunz A: “Instance-based Regression Models for Quantitative Biological Activities using Support Vector Machines and Multilinear Models”, presented at the Scarlet Workshop on in silico methods for Carcinogenicity and Mutagenicity, Milano (April 2008).
- Batke M, Bitsch A, Gundert-Remy U, Guetlein M, Helma C, Kramer S, Maunz A, Partosch F, Seeland M, and Stahlmann R. Multi-Label-Classification to predict Repeated Dose Toxicity in the Context of REACH. In Naunyn-Schmiedebergs Archives of Pharmacology, volume 387, pages S45–S45. Springer 233 Spring St, New York, NY 10013 USA, 2014.
- Batke M, Bitsch A, Gundert-Remy U, Guetlein M, Helma C, Kramer S, Maunz A, Partosch F, Seeland M, and Stahlmann R. Development of Chemical Categories by Optimized Clustering Strategies. In Naunyn-Schmiedebergs Archives of Pharmacology, volume 387, pages S73–S73. Springer 233 Spring St, New York, NY 10013 USA, 2014.
- Batke M, Gundert-Remy U, Helma C, Kramer S, Kleppe-Nordqvist S, Maunz A, Partosch F, Seeland M, and Bitsch A. New Strategies for the Generation of Chemical Categories under REACH. In Naunyn-Schmiedebergs Archives of Pharmacology, volume 386, pages S6–S6. Springer 233 Spring St, New York, NY 10013 USA, 2013.
Items not available as download may be obtained upon request.
- Lo Piparo E, Maunz A, Helma C, Vorgrimmler D, Schilter B. (2014) “Automated and Reproducible Read-Across like Models for Predicting Carcinogenic Potency” Regulatory Toxicology and Pharmacology 70(1). [bib]
- Batke M, Bitsch A, Gundert-Remy U, Guetlein M, Helma Ch, Kramer S, Maunz A, Partosch F, Seeland M, Stahlmann R. (2013). “New Strategies to develop Chemical Categories in the Context of REACH-Work in progress” Toxicology Letters, 221:S84. [bib]
- Maunz A, Gütlein M, Rautenberg M, Vorgrimmler D, Gebele D, Helma C (2013). “Lazar: A Modular Predictive Toxicology Framework” Frontiers in Pharmacology 4(38). [bib, (Open Access)]
- Suenderhauf, C, Hammann, F, Maunz, A, Helma, C, and Huwyler, J (2011).
“Combinatorial QSAR Modeling of Human Intestinal Absorption”
Molecular Pharmaceutics, 8(1):213-224. [bib]
- Hardy, B, Douglas, N, Helma, C, Rautenberg, M, Jeliazkova, N, Jeliazkov, V, Nikolova, I, Benigni, R, Tcheremenskaia, O, Kramer, S, Girschick, T, Buchwald, F, Wicker, J, Karwath, A, Gütlein, M, Maunz, A, Sarimveis, H, Melagraki, G, Afantitis, A, Sopasakis, P, Gallagher, D, Poroikov, V, Filimonov, D, Zakharov, A, Langunin, A, Gloriozova, T, Novikov, S, Skvortsova, N, Druzhilovsky, D, Chawla, S, Gosh, I, Ray, S, Patel, H, and Escher, S (2010).
“Collaborative Development of Predictive Toxicology Applications”
Journal of Cheminformatics, 2(7). [bib, pdf (Open Acces)]
- Maunz, A and Helma, C (2010).
“Prediction of Toxic Effects of Pharmaceutical Agents”
In: Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery, ed. by Konstantin V. Balakin, Sean Ekins. Wiley, New York, NY, USA, chap. 5, pp. 145-176. (ISBN: 978-0-470-19608-3). [abstract, bib]
- Hammann, F, Gutmann, H, Jecklin, U, Maunz, A, Helma, C, and Drewe, J (2009).
“Development of Decision Tree Models for Substrates, Inhibitors, and Inducers of p-Glycoprotein”
Current Drug Metabolism, 10(4):339-346. [bib]
- Maunz, A and Helma, C (2008).
“Prediction of Chemical Toxicity with Local Support Vector Regression and Activity-Specific Kernels”
SAR and QSAR in Environmental Research, 19(5-6):413-431. [pdf, bib]
- Seeland M, Maunz A, Karwath A, Kramer S. “Extracting Information from Support Vector Machines for Pattern-Based Classification” In: Proceedings of the 29th Symposium On Applied Computing, SAC ’14, pages 129–135, New York, NY, USA, 2014. ACM. [pdf, bib]
- Maunz A, Vorgrimmler D, Helma C. “Out-of-Bag Discriminative Graph Mining” In: Proceedings of the 28th Symposium On Applied Computing, SAC ’13, pages 109–114, New York, NY, USA, 2013. ACM. [pdf, bib]
- Maunz A, Helma C, and Kramer S (2011).
“Efficient Mining for Structurally Diverse Subgraph Patterns in Large Molecular Databases”
In: Machine Learning, vol. 83(2), pp. 193-218, Springer Netherlands. [pdf, bib]
- Maunz A, Helma C, Cramer T, and Kramer S (2010).
“Latent Structure Pattern Mining”
In: Balcazar, Jose; Bonchi,Francesco; Gionis, Aristides; Sebag, Michele: ECML/PKDD 2010: Machine Learning and Knowledge Discovery in Databases, vol. 6322, pp. 353-368, Berlin / Heidelberg, Springer. [pdf, poster pdf, bib, Supporting Website]
- Maunz A, Helma C, and Kramer S (2009).
“Large Scale Graph Mining using Backbone Refinement Classes”
In: KDD ’09: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 617-626, New York, NY, USA, ACM.
[pdf, bib, Supporting Website]
© ACM, 2009. This is the author’s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in KDD ’09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 617-626, 2009, http://doi.acm.org/10.1145/1557019.1557089.
- Maunz A: “A Quantitative Extension to the Lazar Algorithm for the Prediction of Chemical Properties.” [pdf diplomarbeit.pdf].
- Maunz A: “On the Number of Backbone Refinement Classes”, derivation and proof of a formula for counting the number of BBRCs in a perfect binary tree. Comparison to the total number of subtrees [pdf bbrc-no.pdf].
- Maunz A: “On the Co-Occurrence and Diversity of Backbone Refinement Classes”, euclidean embedding of BBRC features and instances in 2D as well as feature similarity alike to ORIGAMI approach [pdf bbrc-rep.pdf].
- Maunz A: “Support Vectors and the Margin in a Nutshell”,
brief outline of support vector theory and the
concept of margin. (see ch. 1,2 and 7 of
Schölkopf and Smola, 2002) [pdf sv-margin.pdf].
Selected Talks and Tutorials
- A. Maunz, C. Helma, and S. Kramer: Large Scale Graph Mining using Backbone Refinement Classes. In 7th International Workshop on Mining and Learning with Graphs, Leuven, Belgium (02-04 July 2009). The submitted abstract was one of eight (out of 21) that were selected for oral presentation at the conference. Following the conference, the work was selected for publication in the joint MLG/SRL/ILP workshop special issue of the Machine Learning Journal (among five other works out of 83).
Short Scientific CV
I studied computer science in Freiburg from 2001 to 2007 and graduated in June 2007. During this time I worked as an assistant at the Machine Learning Lab. My Thesis treated the quantitative prediction of toxicological properties of small molecules (eg, carcinogenicity, mutagenicity).
In 2009, I became a Ph.D. student at the Bioinformatics Department I/12 at Technical University Munich, chair of Prof. Dr. Stefan Kramer. In this phase I have been publishing more than ten technical articles in conferences and journals, including the two largest international data mining conferences (KDD, ECML / PKDD) and in a special edition of the Machine Learning Journal (Springer Verlag).
From 2011 to 2012, I led a research collaboration with Nestlé SA for structure-based prediction of chronic toxicity and carcinogenicity where I could gather experience in the food industry. The resulting statistical models are used productively by Nestlé for establishing levels of safety concern, and for regulatory purposes with the Swiss authorities. They were also made available publicly as web services. The work was published in 2014 in Regulatory Toxicology and Pharmacology.
In December 2013 I received my doctoral degree from Technical University Munich for my thesis “Graph Mining methods for Predictive Toxicology” with grade “magna cum laude” (very good). During 2013 and throughout most of 2014 I worked at Oncotest GmbH (Freiburg) on the computer-based synergy determination of drug combinations in preclinical screenings of anti-cancer agents, on visualization in Data Mining workflows, and on Data Mining interfaces for mutational data at.
- Related to Publications:
- Other material: