This profile is built from public research funding records (CIHR, NSERC and SSHRC) and PubMed. We have not imported them from a Concordia University directory, so their courses and email address may be missing. Find their university profile.
Research
Latest papers
Network analysis uncovers the communication structure of SARS-CoV-2 spike protein identifying sites for immunogen design.
iScience · 2023
Latest funding
- $125,000
The Sub-measurable is Not Unreal: Modeling and Communication of the Effect of COVID-19 on the Brain
SSHRC · 2022 · Principal investigator
- $25,000
In silico modeling of the dynamical assembly of cysteine-rich glycopeptides
NSERC · 2022 · Principal investigator
- $125,000
The Sub-measurable is Not Unreal: Modeling and Communication of the Effect of COVID-19 on the Brain
SSHRC · 2022 · Principal investigator
1 publications.
Network analysis uncovers the communication structure of SARS-CoV-2 spike protein identifying sites for immunogen design.
Manrique PD, Chakraborty S, Henderson R, Edwards RJ, Mansbach R, Nguyen K, Stalls V, Saunders C, Mansouri K, Acharya P, Korber B, Gnanakaran S
The Sub-measurable is Not Unreal: Modeling and Communication of the Effect of COVID-19 on the Brain
Principal investigators: Mansbach, Rachael
Keywords: network theory; computational modeling; biomedical imaging; persistent homology; patient advocacy; invisible illnesses; magnetic resonance imaging; multimedia; science journalism; deep learning
In silico modeling of the dynamical assembly of cysteine-rich glycopeptides
Principal investigators: Mansbach, Rachael RA
Keywords: molecular dynamics; multi-scale modeling; polymeric assembly; polymeric materials; protein aggregation
The Sub-measurable is Not Unreal: Modeling and Communication of the Effect of COVID-19 on the Brain
Principal investigators: Mansbach, Rachael
Keywords: network theory computational modeling biomedical imaging persistent homology patient advocacy invisible illnesses magnetic resonance imaging multimedia science journalism deep learning
A physics-based approach to artificial intelligence for understanding of biophysical search spaces
Principal investigators: Mansbach, Rachael
Keywords: protein folding; molecular dynamics; statistical mechanics; generative deep learning; interpretable learning; enhanced sampling; semi-supervised learning; constrained deep learning; active learning; peptide science
A physics-based approach to artificial intelligence for understanding of biophysical search spaces
Principal investigators: Mansbach, Rachael
Computational Physics/Biophysics
Principal investigators: Mansbach, Rachael
Keywords: molecular dynamics; statistical physics; deep learning; drug ; discovery; unsupervised learning; semi-supervised learning; ; dimensionality reduction; multi-scale modeling
From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.
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