Artificial intelligence (AI) models based on deep learning now represent the state of the art
for making functional predictions in genomics research. However, the underlying basis on …

Uncovering the cis-regulatory code that governs when and how much each gene is
transcribed in a given genome and cellular state remains a central goal of biology. Here, we …

The advent of large language models (LLMs) and their adoption by the legal community has
given rise to the question: what types of legal reasoning can LLMs perform? To enable …

The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

Biomedical data are becoming increasingly multimodal and thereby capture the underlying
complex relationships among biological processes. Deep learning (DL)-based data fusion …

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

Learning decent representations from unlabeled time-series data with temporal dynamics is
a very challenging task. In this paper, we propose an unsupervised Time-Series …

The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …

Proteomics plays a vital role in biomedical research in the post-genomic era. With the
technological revolution and emerging computational and statistic models, proteomic …

There has been a surge of interest in artificial intelligence and machine learning (AI/ML)-
based medical devices. However, it is poorly understood how and which AI/ML-based …