Publications

Frank Cichos, Santiago Mui�os Landin, and Ravi Pradip. Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter. In Yuebing Zheng, and Zilong Wu (Eds.), Intelligent Nanotechnology, 113--144, Elsevier, 2023. [PUMA: Active Feedback Machine Multi Optical Reinforcement agent control control, learning, particles, reinforcement] URL

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

Xiaoyu Yin, Dagmar Gromann, and Sebastian Rudolph. Neural machine translating from natural language to SPARQL. Future Generation Computer Systems, (117):510--519, 2021. [PUMA: Learning Machine Natural Neural SPARQL, Translation, knowledge language queries, structured] URL

Veronia Iskandar, Mohamed A. Abd El Ghany, and Diana Goehringer. NDP-RANK: Prediction and ranking of NDP systems performance using machine learning. Microprocessors and Microsystems, (96):104707, 2023. [PUMA: Design Machine Modeling, Near-data Prediction, exploration learning, processing, space] URL

Suryanarayana Maddu, Bevan L Cheeseman, Ivo F Sbalzarini, and Christian L Müller. Stability selection enables robust learning of differential equations from limited noisy data. Proc. Math. Phys. Eng. Sci., (478)2262:20210916, The Royal Society, June 2022. [PUMA: PAR differential equations; learning learning; machine proteins; regression; selection; sparse stability statistical theory]

Christopher Klapproth, Rituparno Sen, Peter F Stadler, Sven Findeiß, and Jörg Fallmann. Common features in lncRNA annotation and classification: A survey. Noncoding RNA, (7)4:77, MDPI AG, December 2021. [PUMA: classification coding extraction; feature learning lncRNA; machine problems; sequence;]

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]

Pascal Kerschke, and Heike Trautmann. Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning. Evol. Comput., (27)1:99--127, MIT Press, 2019. [PUMA: Automated algorithm analysis; black-box continuous exploratory landscape learning; machine optimization. optimization; selection; single-objective]

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.;]

Marie-Theres Huemer, Alina Bauer, Agnese Petrera, Markus Scholz, Stefanie M Hauck, Michael Drey, Annette Peters, and Barbara Thorand. Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study. J. Cachexia Sarcopenia Muscle, (12)4:1011--1023, Wiley, August 2021. [PUMA: Appendicular Body Fat Machine Muscle Proteomics fat index; learning; mass mass; muscle skeletal]

Souhrid Mukherjee, Joy D Cogan, John H Newman, John A Phillips, 3rd, Rizwan Hamid, Undiagnosed Diseases Network, Jens Meiler, and John A Capra. Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network. Am. J. Hum. Genet., (108)10:1946--1963, Elsevier BV, October 2021. [PUMA: Diseases Network; UDN; Undiagnosed clinical digenic disease disease; learning; machine oligogenic prediction; rare topic_lifescience]

Veronika Scholz, Peter Winkler, Andreas Hornig, Maik Gude, and Angelos Filippatos. Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks. Sensors (Basel), (21)6:2005, MDPI AG, March 2021. [PUMA: (SHM) composite composites; connected convolutional dense fully health learning; machine monitoring networks; neural rotors; structural]

Mariia Tkachenko, Claire Chalopin, Boris Jansen-Winkeln, Thomas Neumuth, Ines Gockel, and Marianne Maktabi. Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images. Cancers (Basel), (15)7April 2023. [PUMA: cancer cancer; classification; colorectal convolutional filter; hyperspectral imaging; learning; machine median networks; post-processing; pre-processing]

Praveen Vasudevan, Markus Wolfien, Heiko Lemcke, Cajetan Immanuel Lang, Anna Skorska, Ralf Gaebel, Anne-Marie Galow, Dirk Koczan, Tobias Lindner, Wendy Bergmann, Brigitte Mueller-Hilke, Brigitte Vollmar, Bernd Joachim Krause, Olaf Wolkenhauer, Gustav Steinhoff, and Robert David. CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation. Genome Med., (15)1:61, August 2023. [PUMA: Cell Immunocompromised; Machine Macrophages; Myocardial Single-cell infarction; learning; therapy; topic_lifescience]

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]

Ariel Iporre-Rivas, Dorothee Saur, Karl Rohr, Gerik Scheuermann, and Christina Gillmann. Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network. J. Med. Imaging (Bellingham), (10)4:044502, SPIE-Intl Soc Optical Eng, July 2023. [PUMA: graph imaging; learning; machine medical multi-modal networks; neural prediction stroke]

Akshay Akshay, Masoud Abedi, Navid Shekarchizadeh, Fiona C Burkhard, Mitali Katoch, Alex Bigger-Allen, Rosalyn M Adam, Katia Monastyrskaya, and Ali Hashemi Gheinani. MLcps: machine learning cumulative performance score for classification problems. Gigascience, (12)December 2022. [PUMA: Python classification evaluation evaluation; learning; machine model package; problems; score unified]

Mersedeh Sadeghi, Daniel Pöttgen, Patrick Ebel, and Andreas Vogelsang. Explaining the Unexplainable: The Impact of Misleading Explanations on Trust in Unreliable Predictions for Hardly Assessable Tasks. Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, 36–46, Association for Computing Machinery, New York, NY, USA, 2024. [PUMA: XAI, explainability, learning, machine trust] URL

Leonie Lampe, Hans-Jürgen Huppertz, Sarah Anderl-Straub, Franziska Albrecht, Tommaso Ballarini, Sandrine Bisenius, Karsten Mueller, Sebastian Niehaus, Klaus Fassbender, Klaus Fliessbach, Holger Jahn, Johannes Kornhuber, Martin Lauer, Johannes Prudlo, Anja Schneider, Matthis Synofzik, Jan Kassubek, Adrian Danek, Arno Villringer, Janine Diehl-Schmid, Markus Otto, Matthias L Schroeter, and FTLD Consortium Germany. Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging. NeuroImage Clin., (37)103320:103320, Elsevier BV, January 2023. [PUMA: Dementia; Diagnosis; MRI; Machine Neurodegeneration; Volumetry learning; topic_lifescience unit_test]