Author

K. Kohlhoff

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Biophysics· 2 Bioengineering· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:deep learning· 2 Research Focus:protein folding & dynamics· 2 Assay:structural biology· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

C. Camilloni· 2 shared G. T. Heller· 2 shared M. Vendruscolo· 2 shared T. Löhr· 2 shared A. Kiraly· 1 shared D. Ardila· 1 shared R. Iyer· 1 shared S. Baradwaj· 1 shared

Activity by Year

2018-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBiophysics

C. Camilloni

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A small molecule stabilises the disordered native state of the Alzheimer's Aβ peptide

T. Löhr, K. Kohlhoff, G. T. Heller, C. Camilloni, M. Vendruscolo

This study identifies a small molecule that stabilizes the disordered native state of the Alzheimer's Aβ42 peptide through dynamic binding involving π-stacking and entropic effects.

bioRxiv · Nov 10, 2021 · PreprintBiophysics

A kinetic ensemble of the Alzheimer's Aβ peptide

T. Löhr, K. Kohlhoff, G. T. Heller, C. Camilloni, M. Vendruscolo

This study develops a Markov state model to determine the kinetic ensemble of Aβ42, revealing fast inter-state transitions and predominantly unfolded or short-lived partially-folded states.

bioRxiv · May 7, 2020Biophysics

Predicting Brain Age Using Structural Neuroimaging and Deep Learning

Y. Varatharajah, S. Baradwaj, A. Kiraly, D. Ardila, R. Iyer, S. Shetty, K. Kohlhoff

This paper develops deep learning models to predict brain age from structural MRI data and finds that predicted brain age differs between cognitively impaired and healthy individuals of similar chronological age.

bioRxiv · Dec 17, 2018 · PreprintBioengineering