Author

V. Contessoto

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

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:chromatin & epigenetic regulation· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:Hi-C· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:protein folding & dynamics· 1

Frequent Co-Authors

J. N. Onuchic· 3 shared M. Di Pierro· 2 shared A. B. Oliveira· 1 shared A. Hassan· 1 shared A. Wang· 1 shared E. G. Dodero Rojas· 1 shared E. L. Aiden· 1 shared J. Noel· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 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

3 shared papersBiophysics

J. N. Onuchic

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Shaping the Genome via Lengthwise Compaction, Phase Separation, and Lamina Adhesion

S. Brahmachari, V. Contessoto, M. Di Pierro, J. N. Onuchic

The paper develops a polymer model of the genome to explore how physical interactions shape genome architecture across species.

bioRxiv · Feb 28, 2022Biophysics

SMOG 2 and OpenSMOG: Extending the limits of structure-based models

A. B. Oliveira, V. Contessoto, A. Hassan, S. Byju, A. Wang, Y. Wang, E. G. Dodero Rojas, U. Mohanty, J. Noel, J. N. Onuchic, P. C. Whitford

This paper introduces SMOG 2 and OpenSMOG, extending structure-based models to handle large biomolecular systems with enhanced customization and integration with OpenMM.

bioRxiv · Aug 15, 2021Biophysics

Exploring Chromosomal Structural Heterogeneity AcrossMultiple Cell Lines

R. R. Cheng, V. Contessoto, E. L. Aiden, P. G. Wolynes, M. Di Pierro, J. N. Onuchic

The paper uses simulations and machine learning to predict chromosomal structures across cell lines, showing consistency with microscopy and revealing conformational transitions and compartment rearrangements.

bioRxiv · Mar 21, 2020Biophysics