Publications

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

Mersedeh Sadeghi, Daniel Pöttgen, Patrick Ebel, und 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, learning, explainability, machine trust] URL

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, und Robert David. CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation. Genome Med., (15)1:61, August 2023. [PUMA: Macrophages; learning; Myocardial topic_lifescience infarction; Immunocompromised; Cell therapy; Machine Single-cell]

Ariel Iporre-Rivas, Dorothee Saur, Karl Rohr, Gerik Scheuermann, und 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, Juli 2023. [PUMA: stroke medical learning; neural networks; imaging; machine prediction multi-modal graph]

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

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, und FTLD Consortium Germany. Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging. NeuroImage Clin., (37)103320:103320, Elsevier BV, Januar 2023. [PUMA: MRI; Neurodegeneration; learning; unit_test Diagnosis; Volumetry topic_lifescience Dementia; Machine]

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

Suryanarayana Maddu, Bevan L Cheeseman, Ivo F Sbalzarini, und 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, Juni 2022. [PUMA: PAR regression; statistical learning; learning sparse differential equations; machine proteins; selection; stability theory]

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

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

Souhrid Mukherjee, Joy D Cogan, John H Newman, John A Phillips, 3rd, Rizwan Hamid, Undiagnosed Diseases Network, Jens Meiler, und 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, Oktober 2021. [PUMA: disease; prediction; disease learning; Network; oligogenic Diseases clinical digenic rare machine topic_lifescience Undiagnosed UDN;]

Marie-Theres Huemer, Alina Bauer, Agnese Petrera, Markus Scholz, Stefanie M Hauck, Michael Drey, Annette Peters, und 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: mass index; skeletal learning; Appendicular mass; Machine Proteomics muscle Fat fat Muscle Body]

Veronika Scholz, Peter Winkler, Andreas Hornig, Maik Gude, und 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, März 2021. [PUMA: convolutional (SHM) monitoring learning; neural networks; rotors; health dense composites; connected structural composite machine fully]

Pascal Kerschke, und 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: learning; single-objective analysis; black-box exploratory optimization; continuous machine optimization. Automated selection; algorithm landscape]

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