Machine Learning and Data Mining

Andreas Maunz received his diploma (german equivalent to M. Sc.) in computer science in 2007, and his doctoral degree in 2013. He is a senior principal scientist in research and early development at Roche‘s main site in Basel (CH).

Research:
Data mining (clustering, pattern detection, signal processing) and machine learning (gradient boosting, deep learning), and inferential statistics (parametric models with quantification of uncertainty). Clinical trial data, biostatistics, data-driven workflows for quantitative assessment of data quality.

Practical applications:
Machine Learning on images and image-derived data (particularly deep learning), image processing (graph cut methods, kernels), advanced statistical models.

Technical Platforms: Long-term experience in R (Package authoring, knowledge of major statistical packages), Python (keras, tensorflow, numpy), as well as C/C++, Javascript (D3), SQL (Oracle) and document DBs (mongodb). Systems level: Linux (Shells, Pipelines, POSIX), high performance computing (clusters).

Teaching

• 2018: SABS Graduate Program, Oxford University: “Deep Learning in the Life Sciences”

Awards

• 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.
• 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.

Conference Posters

• T. Peto, A. Maunz, E. v. Schulthess, K. Patel, I. Bachmeier, IL. Jones, Y. Cohen, K. Gibson, JR. Willis, C. Glittenberg, S. Fauser“Automated Segmentation of Hyperreflective Foci in DMO Shows Greater Volume Reduction With Faricimab vs Aflibercept in the Phase 3 YOSEMITE and RHINE Trials ”, The Royal College of Ophthalmologists Annual Congress, Oxford, UK, May 2024
• S. Yu, B. Titz, M. Parian-Scherb, I. Bachmeier, P. Enders, L. Altay, M. Koss, SA. Shahrezaei, J. Garweg, C. Glittenberg, C. Cukras, A. Maunz, S. Fauser “Hypertransmission defect phenotypes and functional associations in eyes with neovascular age-related macular degeneration”, ARVO Annual Meeting, Seattle (WA), USA, May 2024
• M. Parian-Scherb, S. Yu, I. Bachmeier, P. Enders, L. Altay, M. Koss, SA. Shahrezaei, J. Garweg, C. Glittenberg, C. Cukras, S. Fauser, A. Maunz “Slab selection for effective en face OCT segmentation of hypertransmission defects in neovascular age-related macular degeneration”, ARVO Annual Meeting, Seattle (WA), USA, May 2024
• A. Maunz, M. Parian-Scherb, Y. Cohen, I. Bachmeier, S. Yu, K. Gibson, F. Mar, Z. Xue, C. Glittenberg “Deep Learning based hard exudate segmentation using SD-OCT in the ALTIMETER study”, ARVO Annual Meeting, Seattle (WA), USA, May 2024
• I. Bachmeier, S. Yu, C. Glittenberg, A. Maunz, S. Fauser “Model for resolution of subretinal hyperreflective material in neovascular age-related macular degeneration using deep learning image segmentation”, ARVO Annual Meeting, Seattle (WA), USA, May 2024
• R. Goldberg, M. Amador, C. Dinah, K. Gibson, C. Glittenberg, A. Maunz,, O. O'Leary, E. Rahimi, D. Uschner “Greater reduction in hard exudates with faricimab vs aflibercept in patients with DME: biomarker results from the phase 3 YOSEMITE/RHINE trials”, ARVO Annual Meeting, Seattle (WA), USA, May 2024
• A. Maunz, E. v. Schulthess, K. Patel, U. Chakravarthy, I. Bachmeier, IL. Jones, Y. Cohen, K. Gibson, J. Willis, C. Glittenberg, S. Fauser “Automated Segmentation of Hyperreflective Foci in Diabetic Macular Edema shows Greater Volume Reduction by Faricimab vs Aflibercept in Phase 3 YOSEMITE and RHINE”, ARVO Annual Meeting, New Orleans (LA), USA (April 20–27, 2023)
• H. Lu, A. Maunz. “OCT Segmentation of retinal fluids using Deep Learning”, ARVO Imaging in the Eye Meeting, New Orleans (LA), USA, April 2023.
• A. Maunz, IL. Jones, Y. Cohen, H. Lu, I. Bachmeier, S. Yu, E. v. Schulthess, C. Glittenberg “Automated Segmentation of Hyperreflective Foci on SD-OCT in DME patients using Deep Learning”, ARVO Annual Meeting, New Orleans (LA), USA (April 20–27, 2023)
• A. Gomariz, H. Lu, T. Albrecht, A. Maunz, F. Benmansour, J. Luu, A. Valcarel, D. Ferrara, O. Goksel. “Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation”, Medical Image Computing and Computer Assisted Interventions (MICCAI), Singapore, (September 18-22, 2022)
• A. Maunz, IL. Jones, J Hernandez-Sanchez, B. Garcia Armendariz, L. Barras, S. Yu. “Machine Learning Approaches to Automated Prediction of Fibrosis Development in Neovascular Age-Related Macular Degeneration Using Optical Coherence Tomography Images”, ARVO Annual Meeting, Denver (CO), USA (May 1–4, 2022)
• A. Neubert, A. Maunz, S. Yu, TL. von Strauss, A. Wenzel, J. Dai. “Machine Learning Approach to Identify Future Treatment Requirement in Pretreated nAMD Patients Based on OCT Images”, ARVO Annual Meeting, Denver (CO), USA (May 1–4, 2022)
• Y. Li, H. Lu, T. Albrecht, A. Maunz, A. Neubert, F. Benmansour, A. Gomariz, J. Luu, D. Ferrara. “Deep Learning to Segment Retinal Layer Disruption on Optical Coherence Tomography in Neovascular Age-Related Macular Degeneration”, ARVO Annual Meeting, Denver (CO), USA (May 1–4, 2022)
• A. Gomariz, H. Lu, Y. Li, T. Albrecht, A. Maunz, F. Benmansour, J. Luu, O. Goksel, D. Ferrara. “A Unified Deep Learning Approach for OCT Segmentation From Different Devices and Retinal Diseases”, ARVO Annual Meeting, Denver (CO), USA (May 1–4, 2022)
• IL. Jones, A. Maunz, T. Albrecht, H. Lu, Y. Li, F. Benmansour, J. Sahni, M. Gliem „Development and External Validation of a Machine Learning Model for Predicting Response to anti-VEGF Treatment in Patients with neovascular AMD“, ARVO Annual Meeting, Baltimore (MD), USA (May 03 – 07, 2020).
• A. Maunz, F. Benmansour, Y. Li, T. Albrecht, YP. Zhang, F. Arcadu, Y. Zheng, S. Madhusudan, J. Sahni „Diagnostic Accuracy of a Machine-Learning Algorithm to Detect and Classify Choroidal Neovascularization based on SD-OCT in Neovascular Age-Related Macular Degeneration (nAMD)“, ARVO Annual Meeting, Baltimore (MD), USA (May 03 – 07, 2020).
• J. Dai, A. Thalhammer, M. Kawczynski, N. Anegondi, X. Wang, A. Maunz, T. Bengtsson, S. Gao, J. Willis „Accurately Identify nAMD Patients with Low Anti-VEGF Treatment Need by Deep Learning“, ARVO Annual Meeting, Baltimore (MD), USA (May 03 – 07, 2020).
• J. Sahni, A. Maunz, F. Arcadu, YP. Zhang. Y. Li, T. Albrecht, A. Thalhammer, F. Benmansour. „Machine Learning Approach to Predict Response to anti-VEGF Treatment in Patients with Neovascular Age-Related Macular Degeneration using SD-OCT“, ARVO Annual Meeting, Vancouver, Canada (April 28 – May 02, 2019).
• F. Arcadu, F. Benmansour, A. Maunz, J. Willis, M. Prunotto, Z. Haskova. „Deep Learning Algorithm for Patient-Level Prediction of Diabetic Retinopathy Response to Vascular Endothelial Growth Factor Inhibition“, ARVO Annual Meeting, Vancouver, Canada (April 28 – May 02, 2019).
• F. Arcadu, J. Willis, A. Maunz, J. Michon, Z. Haskova, F. Benmansour. „Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs“, 42nd Annual Macula Society Meeting, ARVO Annual Meeting, Vancouver, Canada (April 28 – May 02, 2019).
• F. Arcadu, F. Benmansour, A. Maunz, J. Michon, Z. Haskova, D. McClintock, J. Willis, M. Prunotto. „Automated Image Quality Evaluation of Color Fundus Photographs Using Deep Learning Architecture“, ARVO Imaging in the Eye Conference 2018, Honolulu (HI), USA (April 28, 2018)
• Maunz A, Ulrich E, Sternberger L, Blumenroehr C. Custom Scientific Visualizations in TIBCO Spotfire for Better Informed Decisions“, 15th Annual Bio-IT World Conference & Expo, Boston, USA, April 5-7, 2016.
• Amrein K, Vercruysse M, Prunotto M, Wolf L, Schmucki R, Racek T, Clausen I, Blum Marti R, Araujo Del Rosario A, Benmansour F, Maunz A, Jensen Zoffmann S. „Mode of action characterization of antibiotics using expression profile fingerprint“, Modern Phenotypic drug development, the path forward (Keystone Symposium), Big Sky, Montana, USA, April 2—6, 2016.
• Maier A, Dhar S, Maunz A, Zeitouni B, Peille A-L, Giesemann T, Fiebig HH. „High-throughput analysis of 3D tumor colony formation of primary cell suspensions derived from xenografts to identify  efficacy of anti-tumor agents in single agent or combination therapy“, 27th AACR-NCI-EORTC
  International Conference on Molecular Targets and Cancer Therapeutics, Boston, MA, USA, November 5-9, 2015, abstract B70
• 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).

Conference Talks

• M. Ip, R. Goldberg, M. Amador, C. Dinah, K. Gibson, C. Glittenberg, A. Maunz, O. O'Leary, E. Rahimy, D. Uschner. “Impact of faricimab vs aflibercept on hard exudates in patients with DME: Results from the phase 3 YOSEMITE/RHINE trials”, Annual Pacific Retina Club 2024, Los Angeles (CA), USA, June 2024
• Y. Wang, D. Hibar, A. Maunz,, T. Albrecht, K. Gibson, JR. Willis, D. Chang, KM. Litts. “Impact of Early Intraretinal Fluid Reduction (IRF) on One-Year Outcomes in Diabetic Macular Edema (DME)”, ARVO Annual Meeting, Seattle (WA), USA, May 2024
• M. Ip, R. Goldberg, M. Amador, C. Dinah, K. Gibson, C. Glittenberg, A. Maunz,, O. O'Leary, E. Rahimi, D. Uschner. “Impact of faricimab vs aflibercept on hard exudates in patients with DME: Results from the phase 3 YOSEMITE/RHINE trials, ”Annual Pacific Retina Club 2024, Los Angeles (CA), USA, June 2024
• Y. Wang, D. Hibar, A. Maunz,, T. Albrecht, K. Gibson, JR. Willis, D. Chang, KM. Litts. “Impact of Early Intraretinal Fluid Reduction (IRF) on One-Year Outcomes in Diabetic Macular Edema (DME)”, ARVO Annual Meeting, Seattle (WA), USA, May 2024
• JA. Wells, A. Maunz,, K. Patel, E. v. Schulthess, T. Wang, J. Willis, K. Gibson. “Biomarkers for vascular Stability, including Inflammation and Fibrosis, demonstrate the benefit of dual ANG-2/VEGF-A inhibution with Faricimab in Phase 3 Trials in DME”, Club Jules Gonin, Palma de Mallorca, Spain, May 2024.
• U. Chakravarthy, R. Singh, A. Maunz,, E. v. Schulthess, K. Patel, I. Bachmeier, IL. Jones, Y. Cohen, K. Gibson, J. Willis, C. Glittenberg, S. Fauser. “Greater Volume Reduction of Hyperreflective Foci in DME with Faricimab vs Aflibercept in Phase 3 YOSEMITE/RHINE”, Macula Society, Palm Springs (CA), USA, Feb 2024.
• S. Szeto, K. Patel, T. Wang, J. Willis, K. Gibson, F. Mar, M. Amador, E. v. Schulthess, A. Maunz, , C. Glittenberg. “Biomarkers for Vascular Stability Demonstrate the Benefit of Dual Ang-2/VEGF-A Inhibition With Faricimab in Phase 3 Trials in DME”, Asia Pacific Academy of Ophthalmology Congress, Feb 2024.
• J. Graff, K. Patel, T. Wang, J. Willis, K. Gibson, F. Mar, M. Amador, E. v. Schulthess, A. Maunz, C. Glittenberg. “Biomarkers for vascular stability and inflammation demonstrate the benefit of dual Ang-2/VEGF-A inhibition with faricimab Phase 3 trials in DME”, Hawaiian Eye and Retina, Jan 2024.
• C. Tan, MBBS, A. Maunz,, K. Patel, E. v. Schulthess, T. Wang, J. Willis. “Biomarkers for Vascular Stability Demonstrate the Benefit of Dual Ang-2/VEGF-A Inhibition With Faricimab in Phase 3 Trials in DME”, Asian-Pacific Vitreo-Retina Society (APVRS) 2023.
• R. Singh, U. Chakravarty, A. Maunz, E. v. Schulthess, K. Patel, I. Bachmeier, I. Jones, Y. Cohen, K. Gibson, C. Glittenberg, S. Fauser. “Automated Segmentation of Hyperreflective Foci in DME: Greater Volume Reduction With Faricimab in Phase 3 YOSEMITE/RHINE”, American Academy Ophthalmology (AAO), 127th annual meeting, Nov 2023.
• U. Chakravarthy, A. Maunz,, E. v. Schulthess, K. Patel, I. Bachmeier, I. Jones, Y. Cohen, K. Gibson, J. Willis, C. Glittenberg, S. Fauser. “Automated Segmentation of Hyperreflective Foci in DME Shows Greater Volume Reduction in Eyes Treated With Faricimab Compared to Aflibercept in the Phase 3 YOSEMITE and RHINE Clinical Trials”, EURETINA 2023, Amsterdam, The Netherlands.
• E. v. Schulthess, A. Maunz, U. Chakravarty, N. Holekamp, D. Pauleikhoff, R. Singh, K. Patel, I. Bachmeier, S. Yu, I. Jones, Y. Cohen, K. Gibson, J. Willis, C. Glittenberg, S. Fauser. “Greater Reduction in Hyperreflective Foci with Faricimab Compared to Aflibercept in patients with DME”, EVER Meeting, Valencia, Spain, October 26-28, 2023.
• P. Ferrone, M. Amador, K. Gibson, C. Glittenberg, F. Mar, A. Maunz, K. Patel, E. v. Schulthess, T. Wang, P. Westenskow, J. Willis. “Biomarkers for vascular stability, including leakage, inflammation, and fibrosis, demonstrate the benefit of dual Ang-2/VEGF-A inhibition with faricimab in Phase 3 trials in DME”, Retina Society, New York, USA, October 11-14, 2023.
• A. Kotecha, C. Glittenberg, A. Maunz, Z. Haskova, N. Jain, M. Singer. “Morph-interpolation visualizations of the effect of monthly intravitreal injections of faricimab 6.0 mg in patients with diabetic macular edema from the BOULEVARD trial”, ARVO Annual Meeting, New Orleans (LA), USA, April 2023.
• C. Glittenberg, A. Maunz, E. v. Schulthess, I. Bachmeier, I. Jones, Y. Cohen, S. Fauser. “3D Meshing and Unbiased Compute Unified Device Architecture Ray-Traced Contour Rendering of Convolutional Neural Network Segmentations of Dense Optical-Coherence Volumes in DME And nAMD”, ARVO Imaging in the Eye Meeting, New Orleans (LA), USA, April 2023.
• A. Maunz. “What makes AI interesting for clinical development?” ProRetina Congress 2023, Potsdam, Germany (March 31 – April 1, 2023)
• A. Maunz, IL. Jones, J. Hernandez-Sanchez, B. Garcia Armendariz, L. Barras, S. Yu. “Machine Learning to Predict Fibrosis Development in Neovascular Age-Related Macular Degeneration”, Ophthalmic AI Summit, website, talk recording (June 18, 2022)
• A. Maunz, IL. Jones, J. Dai. “Development and Validation of a Machine Learning Model for Predicting 3-Month Best-Corrected Visual Acuity After Monthly Ranibizumab Treatment in Patients With Neovascular AMD”, ARVO Virtual Meeting (May 1–7, 2021)
• A. Maunz. “Machine Learning to Predict Response to Treatment in eye Diseases using Tomography Scans of the Retina”, Biodata World Congress (November 9-12, 2020)
• F. Arcadu, F. Benmansour, A. Maunz, J. Willis, M. Prunotto, Z. Haskova. „Deep Learning Algorithm to Predict Diabetic Retinopathy Progression on the Individual Patient Level“, ARVO Annual Meeting, Vancouver, Canada (April 28 – May 02, 2019).
• A. Adamis, F. Arcadu, F. Benmansour, A. Maunz, J. Michon, Z. Haskova, D. McClintock, M. Prunotto, J. Willis. „Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus  Photographs“, 42nd Annual Macula Society Meeting, Bonita Springs (FL), USA (February 13-16, 2019).
• S. Zoffmann, A. Maunz, L. Wolf, F. Benmansour, M. Vercruysse, K. Amrein, M. Burcin, M. Prunotto. „Machine Learning Powered Antibiotics Phenotypic Drug Discovery“, Keystone Symposium on Phenotypic Drug Discovery, Breckenridge (CO), USA (March 03-07, 2019).
• Maunz A, Wolf L, Jensen Zoffmann S, Prunotto M,  Amrein K, Vercruysse M, Blum Marti R, Zhu S, Ding  H, Benmansour F. „Automating a high content screening assay“, 15th Annual Bio-IT World
  Conference & Expo, Boston, USA, April 5-7, 2016.
• 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).
• 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 that were selected for oral presentation (21 accepted in total) 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).
• 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).

Publications

• E. v. Schulthess, A. Maunz, U. Chakravarthy, N. Holekamp, D. Pauleikoff, K. Patel, I. Bachmeier, S. Yu, Y. Cohen, M. Parian-Scherb, IL. Jones, K. Gibson, JR. Willis, C. Glittenberg, RP. Sing, S. Fauser "Intraretinal Hyper-Reflective Foci Are Almost Universally Present and Co-Localize With Intraretinal Fluid in Diabetic Macular Edema" Investigative Ophthalmology & Visual Science May 2024, Vol.65, 26.
• A. Maunz, L. Barras, M. G. Kawczynski, J. Dai, A. Y. Lee, R. F. Spaide, J. Sahni, D. Ferrara “Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration” Ophthalmology Science 2023, 100319.
• A. Maunz, F. Benmansour, Y. Li, T. Albrecht, YP. Zhang, F. Arcadu, Y. Zheng, S. Madhusudhan, J. Sahni “Accuracy of a Machine Learning Algorithm to Detect and Classify Choroidal Neovascularization on Spectral-Domain Optical Coherence Tomography” Journal of Personalized Medicine 6, 11, Jun 2021.
  
• M. Zarbin, L. Hill, A. Maunz, M. Gliem, I. Stoylov “Anti-VEGF-resistant subretinal fluid is Associated with Better Vision and Reduced Risk of Macular Atrophy” British Journal of Ophthalmology Published Online First: 26 May 2021.
• F. Arcadu, F. Benmansour, A. Maunz, J. Willis, Z. Haskova, M. Prunotto “Deep learning algorithm predicts diabetic retinopathy progression in individual patients”. npj Digital Medicine 2, 92, Sep 2019.
• F. Arcadu, J. Willis, A. Maunz, J. Michon, Z. Haskova, M. Prunotto, F. Benmansour. “Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs”. Investigative Ophthalmology and Visual Science, 60:852–857, Mar 2019.
• S. Zoffmann, M. Vercruysse, F. Benmansour, A. Maunz, L. Wolf, R. Blum Marti, T. Heckel, H. Ding, H. Truong, M. Prummer, R. Schmucki, C. Mason, K. Bradley, A. I. Jacob, C. Lerner, A. Araujo del Rosario, M. Burcin, K. Amrein, and M. Prunotto. “Machine Learning-Powered Antibiotics Phenotypic Drug Discovery”. Nature Scientific Reports, 9:5013, 2019.
• A. Moisan, M. Gubler, J. D. Zhang, Y. Tessier, K. Dumong Erichsen, S. Sewing, R. Gerard, B. Avignon, S. Huber, F. Benmansour, X. Chen, R. Villasenor, A. Braendli-Baiocco, M. Festag, A. Maunz, T. Singer, F. Schuler, and A. B. Roth. “Inhibition of EGF Uptake by Nephrotoxic Antisense Drugs In Vitro and Implications for Preclinical Safety Profiling”. Molecular Therapy Nucleic Acids, 6:89–105, Mar 2017.
• Batke M, Gütlein M, Partosch F, Gundert-Remy U, Helma C, Kramer S, Maunz A, Seeland M, and Bitsch A. “Innovative Strategies to Develop  Chemical Categories using a Combination of Structural and Toxicological Properties” Frontiers in Pharmacology, 7:321, 2016.
• 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).
• Seeland M, Maunz A, Karwath A, Kramer S.  “Extracting Information from Support Vector  Machines for Pattern-Based Classification” Proceedings of the 29th Symposium On Applied Computing, SAC ’14, pages 129–135, New York, NY,  USA, 2014. ACM.
• Maunz A “Graph Mining Methods for Predictive Toxicology” Dissertation Technische Universität München, 2013. [pdf]
• Maunz A, Vorgrimmler D, Helma C. “Out-of-Bag  Discriminative Graph Mining” Proceedings of the  28th Symposium On Applied Computing, SAC ’13, 109–114, New York, NY, USA, 2013. ACM.
• Maunz A, Gütlein M, Rautenberg M, Vorgrimmler D, Gebele D, Helma C. “Lazar: A Modular Predictive Toxicology Framework” Frontiers in Pharmacology 4:38, 2013. [pdf]
• Batke M, Bitsch A, Gundert-Remy U, Guetlein M, Helma Ch, Kramer S, Maunz A, Partosch F, Seeland M, Stahlmann R. “New Strategies to develop Chemical Categories in the Context of REACH-Work in progress” Toxicology Letters, 221:84, 2013.
• Maunz A, Helma C, and Kramer S. “Efficient Mining for Structurally Diverse Subgraph Patterns in Large Molecular Databases” Machine Learning, 83:2, 193-218, Springer Netherlands, 2011.
• Maunz A, Helma C, Cramer T, and Kramer S. “Latent Structure Pattern Mining” ECML/PKDD 2010: Machine Learning and Knowledge Discovery in Databases, 6322, 353-368, Springer Berlin / Heidelberg, 2011.
• Suenderhauf, C, Hammann, F, Maunz, A, Helma, C, and Huwyler, J. “Combinatorial QSAR Modeling of Human Intestinal Absorption” Molecular Pharmaceutics, 8(1):213-224, 2011.
• 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. “Collaborative Development of Predictive Toxicology Applications” Journal of Cheminformatics, 2:7, 2010. [pdf]
• Maunz A, Helma C, and Kramer S (2009). “Large Scale Graph Mining using Backbone Refinement Classes” KDD ’09: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 617-626, New York,  NY, USA, ACM. 
  
• Hammann F, Gutmann H, Jecklin U, Maunz A, Helma C, and Drewe J. “Development of Decision Tree Models for Substrates, Inhibitors, and Inducers of p-Glycoprotein” Current Drug Metabolism, 10:4, 339-346, 2009.
• Maunz A and Helma C. “Prediction of Toxic Effects of Pharmaceutical Agents” 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, 2009.
• Maunz A and Helma C. “Prediction of Chemical Toxicity with Local Support Vector Regression and Activity-Specific Kernels” SAR and QSAR in Environmental Research, 19(5-6):413-431, 2008.

Unpublished Material

• 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).

Links

• Related to Publications:
  • “Virtuelle Versuchskaninchen”, in Deutschlandradio about Lazar [html].
  • “Mehr Tierversuche durch Chemikalienrichtlinie Reach”, TV report in 3sat about animal testing and alternative test methods in the EU, featuring an interview with Christoph Helma. [html (flash player needed)].
• Other material:
  • “Machine Learning”, course by Andrew Ng in Stanford University 2009 [html].
  • “Graph Mining and Graph Kernels”, video lecture by Karsten Borgwardt and Xifeng Yan in KDD ’08 [html].