The transformer-based language models, including vanilla transformer, BERT and GPT-3,
have achieved revolutionary breakthroughs in the field of natural language processing …

Simple Summary The rapidly advancing field of deep learning, specifically transformer-
based architectures and attention mechanisms, has found substantial applicability in …

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal
phenotype and fitness,–. Constructing complete fitness landscapes, in which DNA …

Background and objective Both mass detection and segmentation in digital mammograms
play a crucial role in early breast cancer detection and treatment. Furthermore, clinical …

For prediction and real-time control tasks, machine-learning (ML)-based digital twins are
frequently employed. However, while these models are typically accurate, they are custom …

Motivation Single-cell RNA-sequencing (scRNA-seq) technologies provide an opportunity to
infer cell-specific gene regulatory networks (GRNs), which is an important challenge in …

Recent advancements in genomics, propelled by artificial intelligence, have unlocked
unprecedented capabilities in interpreting genomic sequences, mitigating the need for …

The rapid development of artificial intelligence (AI) technologies in the context of the vast
amount of collectable data obtained from high-throughput sequencing has led to an …

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including
healthcare, the adoption of the Transformers neural network architecture is rapidly changing …

A transformer is the foundational architecture behind large language models designed to
handle sequential data by using mechanisms of self‐attention to weigh the importance of …