Unifying theories in high-dimensional biophysics: approaches, challenges and opportunities
| dc.contributor.author | Bauer, Marianne | en_US |
| dc.contributor.author | LEELAVATI NARLIKAR et al. | en_US |
| dc.contributor.department | Dept. of Data Science | en_US |
| dc.date.accessioned | 2026-04-17T11:11:51Z | |
| dc.date.available | 2026-04-17T11:11:51Z | |
| dc.date.issued | 2026-03 | en_US |
| dc.description.abstract | Across 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 participants | en_US |
| dc.identifier.citation | npj systems biology and applications, 12, 43. | en_US |
| dc.identifier.issn | 2056-7189 | en_US |
| dc.identifier.sourcetitle | npj systems biology and applications | en_US |
| dc.identifier.uri | https://doi.org/10.1038/s41540-026-00680-9 | |
| dc.identifier.uri | http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/10879 | |
| dc.language.iso | en | en_US |
| dc.publication.originofpublisher | Foreign | en_US |
| dc.publisher | Springer Nature | en_US |
| dc.subject | Biophysics | en_US |
| dc.subject | Computational biology and bioinformatics | en_US |
| dc.subject | Mathematics and computing | en_US |
| dc.subject | Neuroscience | en_US |
| dc.subject | Systems biology | en_US |
| dc.subject | 2026-APR-WEEK2 | en_US |
| dc.subject | TOC-APR-2026 | en_US |
| dc.subject | 2026 | en_US |
| dc.title | Unifying theories in high-dimensional biophysics: approaches, challenges and opportunities | en_US |
| dc.type | Article | en_US |