Unifying theories in high-dimensional biophysics: approaches, challenges and opportunities
Marianne Bauer, Akshit Goyal, Sidhartha Goyal, Gautam Reddy, Shaon Chakrabarti, Michael M. Desai, William Gilpin, Jacopo Grilli, Kabir Husain, Sanjay Jain, Mohit Kumar Jolly, Kyogo Kawaguchi, Aneta Koseska, Milo Lin, Leelavati Narlikar, Simone Pigolotti, Archishman Raju
TL;DR
This paper discusses how high-dimensional data in biophysics can be used to better understand and predict biological systems.
Contribution
The paper provides a summary of discussions on unifying theories for high-dimensional biophysics from multiple perspectives.
Findings
High-dimensional datasets are common across biological subdisciplines.
Participants explored methods and models for theoretical understanding of biological systems.
The paper highlights both challenges and opportunities in high-dimensional biophysics.
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.
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Taxonomy
TopicsBioinformatics and Genomic Networks · Machine Learning in Bioinformatics · Environmental Monitoring and Data Management