Publications

Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, and Christian L. Müller. Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (cs.LG), (math.NA), (physics.data-an), Analysis Analysis, Computer Data FOS: Learning Machine Mathematics, Numerical Physical Probability Statistics and information sciences sciences,] URL

André Petermann, Martin Junghanns, Stephan Kemper, Kevin Gómez, Niklas Teichmann, and Erhard Rahm. Graph Mining for Complex Data Analytics. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 1316--1319, December 2016. [PUMA: Data Intelligence Mining;Business algorithms;Conferences;Graph analysis;Data and design mining;Business;Algorithm models;Libraries;Partitioning]

David Nam, Julius Chapiro, Valerie Paradis, Tobias Paul Seraphin, and Jakob Nikolas Kather. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: AI, Artificial CNN, Communications DICOM, Diagnosis; Digital HCC, Imaging Individual ML, MVI, Medicine; NAFLD, NASH, Prognosis Reporting TACE, TRIPOD, Transparent WSIs, a and artificial carcinoma; chemoembolisation; convolutional data deep diagnostic disease; fatty for hepatocellular images; imaging; in integration intelligence; invasion; learning; liver machine microvascular model multimodal multivariable network; neural non-alcoholic of or prediction slide steatohepatitis; support system; transarterial whole]

Daniel Ayala, Inma Hernández, David Ruiz, and Erhard Rahm. Multi-source dataset of e-commerce products with attributes for property matching. Data Brief, (41)107884:107884, Elsevier BV, April 2022. [PUMA: Data Ontology; Property engineering; integration; matching]

Pascal Kerschke, Holger H Hoos, Frank Neumann, and Heike Trautmann. Automated algorithm selection: Survey and perspectives. Evol. Comput., (27)1:3--45, MIT Press, 2019. [PUMA: Automated algorithm analysis; approaches; automated combinatorial configuration; continuous data exploratory feature-based landscape learning; machine metalearning optimisation; selection; streams.;]

Diego Esteves, Anisa Rula, Aniketh Janardhan Reddy, and Jens Lehmann. Toward Veracity Assessment in RDF Knowledge Bases: An Exploratory Analysis. J. Data and Information Quality, (9)3Association for Computing Machinery, New York, NY, USA, February 2018. [PUMA: DeFacto, analysis benchmark, checking, data data, exploratory fact linked quality, trustworthiness,] URL

Katja Hoffmann, Katja Cazemier, Christoph Baldow, Silvio Schuster, Yuri Kheifetz, Sibylle Schirm, Matthias Horn, Thomas Ernst, Constanze Volgmann, Christian Thiede, Andreas Hochhaus, Martin Bornhäuser, Meinolf Suttorp, Markus Scholz, Ingmar Glauche, Markus Loeffler, and Ingo Roeder. Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology. BMC Med. Inform. Decis. Mak., (20)1:28, February 2020. [PUMA: Clinical Computer Data Haematology; Individual Mathematical Model-based Routine Support decision-making; management; modelling; optimization; planning; simulation; system therapy treatment workflow;]

Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, and Olaf Wolkenhauer. ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets. Pattern Recognition, (147):110138, 2024. [PUMA: Convex GAN, Imbalanced LoRAS, Tabular data data, learning, space] URL

Akshay Akshay, Mitali Katoch, Navid Shekarchizadeh, Masoud Abedi, Ankush Sharma, Fiona C Burkhard, Rosalyn M Adam, Katia Monastyrskaya, and Ali Hashemi Gheinani. Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience, (13)January 2024. [PUMA: AutoML; analysis; classification data learning; machine problems; visualization]

Markus Wolfien, Najia Ahmadi, Kai Fitzer, Sophia Grummt, Kilian-Ludwig Heine, Ian-C Jung, Dagmar Krefting, Andreas Kühn, Yuan Peng, Ines Reinecke, Julia Scheel, Tobias Schmidt, Paul Schmücker, Christina Schüttler, Dagmar Waltemath, Michele Zoch, and Martin Sedlmayr. Ten topics to get started in medical informatics research. J. Med. Internet Res., (25):e45948, July 2023. [PUMA: clinical communication; data data; digital health health; informatics; interdisciplinary medical research]

Najia Ahmadi, Michele Zoch, Patricia Kelbert, Richard Noll, Jannik Schaaf, Markus Wolfien, and Martin Sedlmayr. Methods used in the development of common data models for health data: Scoping review. JMIR Med. Inform., (11):e45116, August 2023. [PUMA: ; Data Development Healthcare Healthcare; Informatics Informatics; Interoperability Interoperability; Medical Observational Outcomes Partnership Partnership; Process Process; Repositories Repositories; Standardized Suggestive common data data; electronic elements elements; harmonisation harmonisation; health involvement model model; record record; stakeholder]