Siri of the cell: what biology could learn from the iPhone.

By Anne-Ruxandra Carvunis, Trey Ideker. 2014

25 citations recorded by the source corpus.

Modern genomics is very efficient at mapping genes and gene networks, but how to transform these maps into predictive models of the cell remains unclear. Recent progress in computer science, embodied by intelligent agents such as Siri, inspires an approach for moving from networks to multiscale models able to predict a range of cellular phenotypes and answer biological questions.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Predicting multicellular function through multi-layer tissue networks (2017, background): “• Finally,thehierarchyisanaturalwaytorepresentandmodelbiological systems spanning many different biological scales (Carvunis and Ideker, 2014; Greene et al. , 2015; Yu et al. , 2016).”
Using deep learning to model the hierarchical structure and function of a cell (2018, background): “Such hierarchies form a natural bridge from variations in genotype, at the scale of nucleotides and genes, to variations in phenotype, at the scale of cells and organisms 12,13 .”
Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems (2016, background): “For instance, intelligent systems like Apple’s Siri and IBM’s Watson carry out logical reasoning using a large collection of world knowledge represented by ontologies (Carvunis and Ideker, 2014).”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “lations [21, 22, 23] and also cover a wide spectrum of timescales and species.”