Egyszerű nézet

dc.contributor.author Csermely, Péter
dc.contributor.author Kunsic, N
dc.contributor.author Mendik, Péter
dc.contributor.author Kerestély, M
dc.contributor.author Faragó, T
dc.contributor.author Veres, Dániel
dc.contributor.author Tompa, Péter
dc.date.accessioned 2020-05-08T09:58:20Z
dc.date.available 2020-05-08T09:58:20Z
dc.date.issued 2020
dc.identifier 85078805590
dc.identifier.citation journalVolume=45;journalIssueNumber=4;journalTitle=TRENDS IN BIOCHEMICAL SCIENCES;pagerange=284-294;journalAbbreviatedTitle=TRENDS BIOCHEM SCI;
dc.identifier.uri http://repo.lib.semmelweis.hu//handle/123456789/8228
dc.identifier.uri doi:10.1016/j.tibs.2019.12.005
dc.description.abstract Molecular processes of neuronal learning have been well described. However, learning mechanisms of non-neuronal cells are not yet fully understood at the molecular level. Here, we discuss molecular mechanisms of cellular learning, including conformational memory of intrinsically disordered proteins (IDPs) and prions, signaling cascades, protein translocation, RNAs [miRNA and long noncoding RNA (lncRNA)], and chromatin memory. We hypothesize that these processes constitute the learning of signaling networks and correspond to a generalized Hebbian learning process of single, non-neuronal cells, and we discuss how cellular learning may open novel directions in drug design and inspire new artificial intelligence methods. © 2020 The Authors
dc.format.extent 284-294
dc.relation.ispartof urn:issn:0968-0004
dc.title Learning of Signaling Networks: Molecular Mechanisms
dc.type Journal Article
dc.date.updated 2020-03-13T08:47:28Z
dc.language.rfc3066 en
dc.rights.holder NULL
dc.identifier.mtmt 31177422
dc.contributor.department SE/AOK/I/Orvosi Vegytani, Molekuláris Biológiai és Patobiokémiai Intézet
dc.contributor.institution Semmelweis Egyetem


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