Data-driven models for predicting intrinsically disordered protein polymer physics directly from composition or sequence Academic Article

abstract

• A new representation for intrinsically disordered protein sequences is developed for usage in both classical and deep learning machine learning models.

authors

• Mittal, Jeetain
• Tabor, Daniel

published proceedings

• MOLECULAR SYSTEMS DESIGN & ENGINEERING
• Mol Syst Des Eng

altmetric score

• 0.25

author list (cited authors)

• Chao, T., Rekhi, S., Mittal, J., & Tabor, D.

citation count

• 3

complete list of authors

• Chao, Tzu-Hsuan||Rekhi, Shiv||Mittal, Jeetain||Tabor, Daniel P

publication date

• September 2023

publisher

• Royal Society of Chemistry (RSC)  Publisher

published in

• Molecular Systems Design && Engineering  Journal

keywords

• 34 Chemical Sciences
• 3403 Macromolecular And Materials Chemistry
• Bioengineering

PubMed Central ID

• 38222029

Digital Object Identifier (DOI)

• 10.1039/D3ME00053B
• 10.1039/d3me00053b

start page

• 1146

end page

• 1155

volume

• 8

issue

• 9

URL

• http://dx.doi.org/10.1039/d3me00053b