Publications

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: problems; score unified learning; package; evaluation machine Python evaluation; model classification]

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: problems; visualization learning; data machine analysis; AutoML; classification]

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 agent Feedback particles, learning, Optical Reinforcement reinforcement Multi control control, Machine] URL

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: mass index; skeletal learning; Appendicular mass; Machine Proteomics muscle Fat fat Muscle Body]

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: stroke medical learning; neural networks; imaging; machine prediction multi-modal graph]

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: Near-data Modeling, exploration Design processing, learning, space Machine Prediction,] URL

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: feature-based learning; metalearning data analysis; optimisation; automated streams.; exploratory combinatorial continuous machine approaches; Automated selection; algorithm configuration; landscape]

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

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: problems; coding extraction; feature sequence; machine lncRNA; learning classification]

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: MRI; Neurodegeneration; learning; unit_test Diagnosis; Volumetry topic_lifescience Dementia; Machine]