Andreas R. Dengel

This study benchmarks 35 AI classifiers across 153 cancer datasets to evaluate their performance in molecular subtype classification, identifying optimal data configurations and model types for different omics modalities and cancer types.

micro_sam finetunes Segment Anything into a light microscopy generalist and a cell/neurite-focused electron microscopy model, plus napari tools for interactive 2d, volumetric and tracking annotation.

The paper introduces a computational predictor, iLEC-DNA, for identifying leccDNA using sequence-derived features, outperforming existing methods across multiple species.

This systematic review analyzes the use of generative adversarial networks for image augmentation in cell microscopy, highlighting common practices, datasets, and challenges in performance metrics.

NTpred improves the identification of tyrosine nitration sites in protein sequences using advanced feature extraction and ensemble classifiers, achieving better performance than existing methods on benchmark datasets.

This paper introduces two deep learning predictors for YY1-mediated chromatin loop prediction, showing improved performance across cell lines and benchmark datasets.

The paper introduces a kmerRP2vec feature representation method combined with CNN and RNN to predict miRNA subcellular locations, outperforming existing methods on a public dataset.