Author

P. Di Achille

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

Biology Disciplines

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Bioengineering· 2 Biophysics· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Approach:deep learning· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

J. Kozloski· 2 shared J. Parikh· 2 shared V. Gurev· 2 shared A. Gomez· 1 shared A. Melville· 1 shared A. Noorani· 1 shared A. Yazdani· 1 shared C. A. Figueroa· 1 shared

Activity by Year

2017-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersBiophysics

J. Kozloski

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Author profile →

4 papers

Recent·Most cited

CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation

C. J. Arthurs, R. Khlebnikov, A. Melville, M. Mar?an, A. Gomez, D. Dillon-Murphy, F. Cuomo, M. Silva Vieira, J. Schollenberger, S. R. Lynch, C. Tossas-Betancourt, K. Iyer, S. Hopper, E. Livingston, P. Youssefi, , C. A. Figueroa

CRIMSON is an open-source software framework for cardiovascular computational haemodynamics, enabling 3D and reduced-order simulations from medical images to clinical applications.

bioRxiv · Oct 14, 2020Bioengineering

Model order reduction for left ventricular mechanics via congruency training

P. Di Achille, J. Parikh, S. Khamzin, O. Solovyova, J. Kozloski, V. Gurev

This paper introduces a model order reduction strategy for 3-D finite element models of ventricular mechanics using congruency training and linear transformations to improve computational efficiency.

bioRxiv · Jul 10, 2019Biophysics

Intrinsic structure of model-derived metrics for in silico proarrhytmic risk assessment identified by global sensitivity analysis

J. Parikh, P. Di Achille, J. Kozloski, V. Gurev

This paper uses global sensitivity analysis to evaluate model-derived metrics for predicting drug-induced TdP risk, finding that these metrics are not strongly correlated with EAD generation and can be explained by direct features.

bioRxiv · Apr 16, 2019 · PreprintPharmacology & Toxicology

Data-driven Modeling of Thrombus Size and Shape in Aortic Dissections: Role of Hemodynamics

A. Yazdani, H. Li, M. Bersi, P. Di Achille, J. Insley, J. Humphrey, G. Karniadakis

This study develops a data-driven particle-continuum model to predict thrombus formation in aortic dissections, showing that geometry and hemodynamics influence thrombus size and distribution.

bioRxiv · Sep 12, 2017Bioengineering