Graph representation learning in biomedicine and healthcare

MM Li, K Huang, M Zitnik - Nature Biomedical Engineering, 2022 - nature.com
Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

[HTML][HTML] Machine learning methods for exploring sequence determinants of 3D genome organization

M Yang, J Ma - Journal of molecular biology, 2022 - Elsevier
In higher eukaryotic cells, chromosomes are folded inside the nucleus. Recent advances in
whole-genome map** technologies have revealed the multiscale features of 3D genome …

Integrating long-range regulatory interactions to predict gene expression using graph convolutional networks

J Bigness, X Loinaz, S Patel, E Larschan… - Journal of …, 2022 - liebertpub.com
Long-range regulatory interactions among genomic regions are critical for controlling gene
expression, and their disruption has been associated with a host of diseases. However …

Evidence for the role of transcription factors in the co-transcriptional regulation of intron retention

F Ullah, S Jabeen, M Salton, ASN Reddy, A Ben-Hur - Genome Biology, 2023 - Springer
Background Alternative splicing is a widespread regulatory phenomenon that enables a
single gene to produce multiple transcripts. Among the different types of alternative splicing …

Finding needles in the haystack: Strategies for uncovering noncoding regulatory variants

Y Chen, MI Paramo, Y Zhang, L Yao… - Annual Review of …, 2023 - annualreviews.org
Despite accumulating evidence implicating noncoding variants in human diseases,
unraveling their functionality remains a significant challenge. Systematic annotations of the …

Prediction of gene co-expression from chromatin contacts with graph attention network

K Zhang, C Wang, L Sun, J Zheng - Bioinformatics, 2022 - academic.oup.com
Motivation The technology of high-throughput chromatin conformation capture (Hi-C) allows
genome-wide measurement of chromatin interactions. Several studies have shown …

[HTML][HTML] Uncovering the relationship between tissue-specific TF-DNA binding and chromatin features through a transformer-based model

Y Zhang, Y Liu, Z Wang, M Wang, S **ong, G Huang… - Genes, 2022 - mdpi.com
Chromatin features can reveal tissue-specific TF-DNA binding, which leads to a better
understanding of many critical physiological processes. Accurately identifying TF-DNA …

Artificial intelligence and deep learning algorithms for epigenetic sequence analysis: A review for epigeneticists and AI experts

M Tahir, M Norouzi, SS Khan, JR Davie… - Computers in Biology …, 2024 - Elsevier
Epigenetics encompasses mechanisms that can alter the expression of genes without
changing the underlying genetic sequence. The epigenetic regulation of gene expression is …

Graph Neural Networks for Z-DNA prediction in Genomes

A Voytetskiy, A Herbert… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Deep learning methods have been successfully applied to the tasks of predicting functional
genomic elements such as histone marks, transcriptions factor binding sites, non-B DNA …

A scalable tool for analyzing genomic variants of humans using knowledge graphs and graph machine learning

S Prasanna, A Kumar, D Rao, EJ Simoes… - Frontiers in Big …, 2025 - frontiersin.org
Advances in high-throughput genome sequencing have enabled large-scale genome
sequencing in clinical practice and research studies. By analyzing genomic variants of …