Articles with public access mandates - Devika SubramanianLearn more
Available somewhere: 22
Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network
A Chattopadhyay, P Hassanzadeh, D Subramanian
Nonlinear Processes in Geophysics 27 (3), 373-389, 2020
Mandates: US National Science Foundation, US National Aeronautics and Space Administration
New predictive models of heart failure mortality using time-series measurements and ensemble models
D Subramanian, V Subramanian, A Deswal, DL Mann
Circulation: Heart Failure 4 (4), 456-462, 2011
Mandates: US National Institutes of Health
Model averaging strategies for structure learning in Bayesian networks with limited data
BM Broom, KA Do, D Subramanian
BMC bioinformatics 13, 1-18, 2012
Mandates: US National Institutes of Health
Predicting adverse drug-drug interactions with neural embedding of semantic predications
HA Burkhardt, D Subramanian, J Mower, T Cohen
AMIA Annual Symposium Proceedings 2019, 992, 2020
Mandates: US National Institutes of Health
Domain-driven models yield better predictions at lower cost than reservoir computers in Lorenz systems
R Pyle, N Jovanovic, D Subramanian, KV Palem, AB Patel
Philosophical Transactions of the Royal Society A 379 (2194), 20200246, 2021
Mandates: US National Science Foundation, US Department of Defense, US Office of the …
MEDRank: using graph-based concept ranking to index biomedical texts
JR Herskovic, T Cohen, D Subramanian, MS Iyengar, JW Smith, ...
International journal of medical informatics 80 (6), 431-441, 2011
Mandates: US National Institutes of Health
Classification-by-analogy: using vector representations of implicit relationships to identify plausibly causal drug/side-effect relationships
J Mower, D Subramanian, N Shang, T Cohen
AMIA annual symposium proceedings 2016, 1940, 2017
Mandates: US National Institutes of Health
Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications
J Mower, D Subramanian, T Cohen
Journal of the American Medical Informatics Association 25 (10), 1339-1350, 2018
Mandates: US National Institutes of Health
aer2vec: distributed representations of adverse event reporting system data as a means to identify drug/side-effect associations
J Portanova, N Murray, J Mower, D Subramanian, T Cohen
AMIA Annual Symposium Proceedings 2019, 717, 2020
Mandates: US National Institutes of Health
New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data
A Parikh, E Huang, C Dinh, B Zupan, A Kuspa, D Subramanian, ...
BMC bioinformatics 11, 1-10, 2010
Mandates: US National Institutes of Health
Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates
T Huang, Y Chu, S Shams, Y Kim, AV Annapragada, D Subramanian, ...
Journal of biomedical informatics 119, 103818, 2021
Mandates: US National Institutes of Health
Learning robust cell signalling models from high throughput proteomic data
M Koch, BM Broom, D Subramanian
International journal of bioinformatics research and applications 5 (3), 241-253, 2009
Mandates: US National Institutes of Health
Modeling individual differences in learning a navigation task
D Gordon, D Subramanian, M Haught, R Kobayashi, S Marshall
Proceedings of the Twentieth Annual Conference of the Cognitive Science …, 2022
Mandates: US Department of Defense
Complementing observational signals with literature-derived distributed representations for post-marketing drug surveillance
J Mower, T Cohen, D Subramanian
Drug safety 43, 67-77, 2020
Mandates: US National Institutes of Health
Graph-based signal integration for high-throughput phenotyping
JR Herskovic, D Subramanian, T Cohen, PA Bozzo-Silva, CF Bearden, ...
BMC bioinformatics 13, 1-7, 2012
Mandates: US National Institutes of Health
Augmenting aer2vec: Enriching distributed representations of adverse event report data with orthographic and lexical information
X Ding, J Mower, D Subramanian, T Cohen
Journal of biomedical informatics 119, 103833, 2021
Mandates: US National Institutes of Health
Cognitive modeling of action selection learning
DF Gordon, D Subramanian
Proceedings of the Eighteenth Annual Conference of the Cognitive Science …, 2019
Mandates: US Department of Defense
Improving pharmacovigilance signal detection from clinical notes with locality sensitive neural concept embeddings
J Mower, E Bernstam, H Xu, S Myneni, D Subramanian, T Cohen
AMIA Summits on Translational Science Proceedings 2022, 349, 2022
Mandates: US National Institutes of Health, Cancer Prevention Research Institute of …
Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning
D Subramanian, A Vittala, X Chen, C Julien, S Acosta, C Rusin, C Allen, ...
Journal of Clinical Medicine 12 (17), 5435, 2023
Mandates: US National Institutes of Health
Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative
T Bergquist, J Loomba, E Pfaff, F Xia, Z Zhao, Y Zhu, E Mitchell, ...
EBioMedicine 108, 2024
Mandates: Bill & Melinda Gates Foundation
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