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]
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
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]
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]
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]
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]
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
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
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]
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]
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]
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: disease; prediction; disease learning; Network; oligogenic Diseases clinical digenic rare machine topic_lifescience Undiagnosed UDN;]
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]
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: convolutional (SHM) monitoring learning; neural networks; rotors; health dense composites; connected structural composite machine fully]
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]
Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (physics.data-an), Analysis, Probability Data sciences, Numerical Statistics FOS: Machine Physical sciences Analysis (cs.LG), Learning (math.NA), Mathematics, and Computer information] URL
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]