12 papers · ranked by Valyu relevance
Fabio Cumbo, Kabir Dhillon, Jayadev Joshi, Davide Chicco + 2 more
Viral species classification is crucial for understanding viral evolution, epidemiology, and developing effective diagnostics and treatments. Traditional methods often rely on sequence similarity, which can be challenging for rapidly evolving viruses. Pangenomes, offering a comprehensive representation of species’…
Muhammad Umair, Young-Koo Lee
Graph data are pervasive worldwide, e.g., social networks, citation networks, and web graphs. A real-world graph can be huge and requires heavy computational and storage resources for processing. Various graph compression techniques have been presented to accelerate the processing time and utilize memory efficiently.…
Prathyush Poduval, Haleh Alimohamadi, Ali Zakeri, Farhad Imani + 3 more
'M. Hassan Najafi' 'Tony Givargis' 'Mohsen Imani'] Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their…
Luca Cappelletti, Tommaso Fontana, Elena Casiraghi, Vida Ravanmehr + 7 more
'Tiffany J. Callahan' 'Carlos Cano' 'Marcin P. Joachimiak' 'Christopher J. Mungall' 'Peter N. Robinson' 'Justin Reese' 'Giorgio Valentini'] Graph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise…
Alexis Bénichou, Jean-Baptiste Masson, Christian L. Vestergaard, Fabrizio De Vico Fallani
Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement…
Md Toki Tahmid, Tanjeem Azwad Zaman, Mohammad Saifur Rahman
Understanding complex graph-structured data is a cornerstone of modern research in fields like cheminformatics and bioinformatics, where molecules and biological systems are naturally represented as graphs. However, traditional graph neural networks (GNNs) often fall short by focusing mainly on node features while…
Jordan M. Eizenga, Adam M. Novak, Emily Kobayashi, Flavia Villani + 6 more
Pangenomics is a growing field within computational genomics. Many pangenomic analyses use bidirected sequence graphs as their core data model. However, implementing and correctly using this data model can be difficult, and the scale of pangenomic data sets can be challenging to work at. These challenges have impeded…
Van Thuy Hoang, Hyeon-Ju Jeon, Eun-Soon You, Yoewon Yoon + 3 more
Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional…
Peter Heringer, Daniel Doerr
Pangenome graphs offer a compact and comprehensive representation of genomic diversity, improving tasks such as variant calling, genotyping, and other downstream analyses. Although the underlying graph structures scale sublinearly with the number of haplotypes, the widely used GFA file format suffers from rapidly…
Zhiyuan Ding, Alex Baras
Recent advances in computation pathology have seen the development of various forms of foundational models that have enabled high-quality, generalpurpose feature extraction from tissue patches. However, most of these models are somewhat limited in their ability to capture cell-to-cell spatial relationships essential…
Xueyuan Chen, Shangzhe Li, Yanchun Liang
Due to the success observed in deep neural networks with contrastive learning, there has been a notable surge in research interest in graph contrastive learning, primarily attributed to its superior performance in graphs with limited labeled data. Within contrastive learning, the selection of a “view” dictates the…
Ian T. Hoffecker, Yunshi Yang, Giulio Bernardinelli, Pekka Orponen + 1 more
Barcoded DNA polony amplification techniques provide a means to impart a unique sequence identity onto specific locations of a surface wafer or chip. We describe a method whereby micro-scale spatial information such as the relative positions of biomolecules on a surface can be transferred to a sequence-based format and…