Network biology: understanding the cell's functional organization

By Albert-László Barabási, Zoltn Oltvai. 2004

8,030 citations recorded by the source corpus.

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Citations in the corpus

Topological analysis and interactive visualization of biological networks and protein structures (2012, methodology): “40,70).”
Artificial intelligence in cancer target identification and drug discovery (2022, methodology): “…by single omics studies, we have to employ arti ﬁ cial intelligence biology analysis to effectively integrate multiple omics data and tackle the complexity of cancer that arises from interactions between genes and their products 16,17 and improve our understanding of carcinogenesis 23 – 29 .”
Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network (2013, background): “Indeed, many complex systems can be represented as networks, ranging from biochemical systems, through the Internet and the World Wide Web, to various social systems [1–7].”
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (2019, neutral): “Graph-structured data is ubiquitous across application domains ranging from chemo-and bioinformatics (Barabasi & Oltvai, 2004; Stokes et al., 2020) to image (Simonovsky & Ko-modakis, 2017) and social network analysis (Easley & Klein-berg, 2010).”
Mining coherent dense subgraphs across massive biological networks for functional discovery (2005, background): “Studying the building principles of biological networks could potentially revolutionize our view of biology and disease pathologies (Barabasi and Oltvai, 2004).”
Metabolomics by numbers: acquiring and understanding global metabolite data. (2004, background): “A paradigm shift from metabolic pathways to networks and neighbourhoods There has been a shift from mental constructs involving metabolic pathways to those based on metabolic networks and neighbourhoods [ 37 ,38], and many would argue that the ‘Boehringer’ metabolic pathways map needs to be updated ”
Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020, background): “…such as social networks (Easley et al., 2010), knowledge graphs (Nickel et al., 2015), molecular graphs (Wu et al., 2018), and biological networks (Barabasi & Oltvai, 2004), as well as for modeling 3D objects (Si-monovsky & Komodakis, 2017), manifolds (Bronstein et al., 2017), and source code…”
Robustness: mechanisms and consequences (2009, background): “Global network topology features, including the extent to which a network is organized with strong central hubs, might also affect robustness [46]; however a combination of topological and dynamical properties likely will be more informative [47].”