Foundation models (FMs) are central to digital pathology, encoding histology images into dense embeddings for facilitating diagnostic classification, molecular alteration prediction, and clinical...

The emergence of genomic language models (gLMs) offers an unsupervised approach to learning a wide diversity of cis -regulatory patterns in the non-coding genome without requiring labels of functio...

The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene expression has introduced challenges in their evaluation and interpretation. Current evaluations align DNN...

Deep convolutional neural networks (CNNs) trained on regulatory genomic sequences tend to learn distributed feature representations across many filters, making it challenging to decipher biologically...