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Network Bioscience.

In the last decade, the very nature of biological research has changed as large-scale data arrive at torrential force and it has ushered in a new era of Bioscience; but also this high dimensional big data is being used to support inference of various types and multiplicities of hypotheses about the extant relationships among the “variables” being measured. The typical current example in the biomedical field is sequencing data (in various forms: DNA sequencing, RNA sequencing, ATAC using sequencing, etc.). Another kind of data currently collected is proteomic data, often with the goal of producing protein-protein interaction networks (PPI networks). Yet another is data about the metabolome of a biological system. Moreover recently, also phenotypic data, data on diseases, symptoms, patients, etc., are being collected at nation-wide level thus giving us another source of highly related (causal) “big data.” From these kinds of data, biologists and bioinformaticians, can make many inferences, and, more often than not, such inferences now reuse several notions, theories, and tools from the field of network science. Network science has accelerated a deep and successful trend in research that influences a range of disciplines like mathematics, graph theory, physics, statistics, data science, and computer science (just to name a few), and adapts the relevant techniques and insights to address relevant but disparate social, biological, technological questions. Most of the data kinds just mentioned naturally lend themselves to a network analysis. The network model is a key viewpoint leading to the uncovering of mesoscale phenomena, thus providing an essential bridge between the observable phenotypes and omics underlying mechanisms. Moreover, network analysis is a powerful hypothesis generation tool guiding the scientific cycle of data gathering, data interpretation, hypothesis generation, and hypothesis testing. The papers contained in the present research topic—Network Bioscience—are examples of how network and graph analysis can be used to elucidate various aspect of biological systems from metabolic regimes, to phenotype-genotype linking, to relationships assessment among diverse omics data for therapy design, to functional submodule identification in a gene network for cancer studies.


Autori esterni: Marco Antoniotti (Universita' degli Studi di Milano Bicocca), Bud Mishra (NYU)
Autori IIT:

Tipo: Curatela (di monografia, atti, rivista)
Area di disciplina: Computer Science & Engineering

File: NetBiosc_2019-v2-2019-09-30.pdf

Attività: Biologia computazionale