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dc.contributor.authorBauer, Marianneen_US
dc.contributor.authorLEELAVATI NARLIKAR et al.en_US
dc.date.accessioned2026-04-17T11:11:51Z
dc.date.available2026-04-17T11:11:51Z
dc.date.issued2026-03en_US
dc.identifier.citationnpj systems biology and applications, 12, 43.en_US
dc.identifier.issn2056-7189en_US
dc.identifier.urihttps://doi.org/10.1038/s41540-026-00680-9en_US
dc.identifier.urihttp://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/10879
dc.description.abstractAcross biological subdisciplines, the last decade has seen an explosion of high-dimensional datasets. At the ICTS workshop ‘Unifying Theories in High-Dimensional Biophysics’, we discussed whether this high dimensionality poses a challenge or an opportunity for theoretically describing, understanding and predicting biological systems. We discussed methods, models and frameworks that can be used for this purpose. This Comment summarizes our discussions from the perspectives of individual participantsen_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.subjectBiophysicsen_US
dc.subjectComputational biology and bioinformaticsen_US
dc.subjectMathematics and computingen_US
dc.subjectNeuroscienceen_US
dc.subjectSystems biologyen_US
dc.subject2026-APR-WEEK2en_US
dc.subjectTOC-APR-2026en_US
dc.subject2026en_US
dc.titleUnifying theories in high-dimensional biophysics: approaches, challenges and opportunitiesen_US
dc.typeArticleen_US
dc.contributor.departmentDept. of Data Scienceen_US
dc.identifier.sourcetitlenpj systems biology and applicationsen_US
dc.publication.originofpublisherForeignen_US
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