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 …

Techniques for and challenges in reconstructing 3D genome structures from 2D chromosome conformation capture data

Z Li, S Portillo-Ledesma, T Schlick - Current Opinion in Cell Biology, 2023 - Elsevier
Chromosome conformation capture technologies that provide frequency information for
contacts between genomic regions have been crucial for increasing our understanding of …

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

F Ozcelik, MS Dundar, AB Yildirim, G Henehan… - Functional & integrative …, 2024 - Springer
Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular
medicine, as in many other fields. The growth in patient data, identification of new diseases …

Graph representation learning in biomedicine

MM Li, K Huang, M Zitnik - arXiv preprint arXiv:2104.04883, 2021 - arxiv.org
Biomedical networks (or graphs) are universal descriptors for systems of interacting
elements, from molecular interactions and disease co-morbidity to healthcare systems and …

Comparative study on chromatin loop callers using Hi-C data reveals their effectiveness

HMAM Chowdhury, T Boult, O Oluwadare - BMC bioinformatics, 2024 - Springer
Background Chromosome is one of the most fundamental part of cell biology where DNA
holds the hierarchical information. DNA compacts its size by forming loops, and these …

Chromatic Differentiation of Functional Mappings of the Composition of Nucleic Acids

IV Stepanyan, MY Lednev - Symmetry, 2023 - mdpi.com
Color visualization of the DNA of diverse living beings can help in the exploration of the
issue of chromatic differentiation of functional mappings of the nucleotide composition of …

[HTML][HTML] GHOST: Graph-based higher-order similarity transformation for classification

E Battistella, M Vakalopoulou, N Paragios, É Deutsch - Pattern Recognition, 2024 - Elsevier
Exploring and identifying a good feature representation to describe high-dimensional
datasets is a challenge of prime importance. However, plenty of feature selection techniques …

GHOST: Graph Higher-Order Similarity Transformation for Classification

E Battistella, M Vakalopoulou, N Paragios… - Available at SSRN …, 2022 - papers.ssrn.com
Exploring and identifying a good feature representation to describe high-dimensional
datasets is a challenge of prime importance. However, plenty of feature selection techniques …

[PDF][PDF] 3D Chromosome Structure Reconstruction Using Graph Convolutional Neural Networks

V Hovenga, J Kalita, O Oluwadare - biomlearn.uccs.edu
Chromosome conformation capture (3C) is a method of measuring chromosome topology in
terms of loci interaction1. The Hi-C method is a derivative of 3C that allows for genome-wide …