The 3D structure of the genome is an important mediator of gene expression. As phenotypic divergence is largely driven by gene regulatory variation, comparing genome 3D contacts across species can further understanding of the molecular basis of speci...
Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims t...
Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genom...
With the rapid growth of data driven by high-throughput sequencing technologies, genomics has entered an era characterized by big data, which presents significant challenges for traditional bioinformatics methods in handling complex data patterns. At...
MOTIVATION: Analysis of the omics data with the help of machine learning (ML) methods is limited by small sample sizes and a large number of variables. One possible approach to deal with such data is using algorithms for feature selection and reducin...
Spatial omics technologies have transformed biomedical research by providing detailed, spatially resolved molecular profiles that enhance our understanding of tissue structure and function at unprecedented levels. Artificial intelligence (AI) holds g...
Deep learning applications have had a profound impact on many scientific fields, including functional genomics. Deep learning models can learn complex interactions between and within omics data; however, interpreting and explaining these models can b...
With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the...
Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratificati...