Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions - Samadi, Moein E.; Mirzaieazar, Hedieh; Mitsos, Alexander; Schuppert, Andreas (BMC bioinformatics, 2024)
Keywords: Algorithms; Binary data; Bioinformatics; Biomedical and Life Sciences; Biomedical data; Biomedical materials; Boxes; Classification; Comparative analysis; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data mining; Datasets; Evaluation; Extrapolation; Hybrid mechanistic/data-driven modeling; Identification; Information management; Integration; Knowledge; Learning algorithms; Life Sciences; Machine learning; Max-cut problem; Microarrays; Modelling; Noise tolerance; Noise-tolerant classification; Overfitting; Python (Programming language); Regularization; Software; Supervised learning; Variables; Germany; Binary data; Noise-tolerant classification; Hybrid mechanistic/data-driven modeling; Max-cut problem; Overfitting