Ilias Tagkopoulos
Ilias Tagkopoulos
Professor, Computer Science and Genome Center, University of California, Davis
Verified email at - Homepage
Cited by
Cited by
An Arabidopsis gene regulatory network for secondary cell wall synthesis
M Taylor-Teeples, L Lin, M De Lucas, G Turco, TW Toal, A Gaudinier, ...
Nature 517 (7536), 571-575, 2015
Predictive behavior within microbial genetic networks
I Tagkopoulos, YC Liu, S Tavazoie
science 320 (5881), 1313-1317, 2008
From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system
E Gultepe, JP Green, H Nguyen, J Adams, T Albertson, I Tagkopoulos
Journal of the American Medical Informatics Association 21 (2), 315-325, 2014
Evolutionary potential, cross‐stress behavior and the genetic basis of acquired stress resistance in Escherichia coli
M Dragosits, V Mozhayskiy, S Quinones‐Soto, J Park, I Tagkopoulos
Molecular systems biology 9 (1), 643, 2013
Benzalkonium chlorides: uses, regulatory status, and microbial resistance
B Merchel Piovesan Pereira, I Tagkopoulos
Applied and environmental microbiology 85 (13), e00377-19, 2019
Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
M Kim, N Rai, V Zorraquino, I Tagkopoulos
Nature communications 7 (1), 1-12, 2016
Transcriptional network analysis identifies BACH1 as a master regulator of breast cancer bone metastasis
Y Liang, H Wu, R Lei, RA Chong, Y Wei, X Lu, I Tagkopoulos, SY Kung, ...
Journal of Biological Chemistry 287 (40), 33533-33544, 2012
An integrative, multi‐scale, genome‐wide model reveals the phenotypic landscape of Escherichia coli
J Carrera, R Estrela, J Luo, N Rai, A Tsoukalas, I Tagkopoulos
Molecular systems biology 10 (7), 735, 2014
From data to optimal decision making: a data-driven, probabilistic machine learning approach to decision support for patients with sepsis
A Tsoukalas, T Albertson, I Tagkopoulos
JMIR medical informatics 3 (1), e3445, 2015
Data integration and predictive modeling methods for multi-omics datasets
M Kim, I Tagkopoulos
Molecular omics 14 (1), 8-25, 2018
A synthetic biology approach to self-regulatory recombinant protein production in Escherichia coli
M Dragosits, D Nicklas, I Tagkopoulos
Journal of biological engineering 6 (1), 1-10, 2012
Application of machine learning in rheumatic disease research
KJ Kim, I Tagkopoulos
The Korean journal of internal medicine 34 (4), 708, 2019
SBROME: a scalable optimization and module matching framework for automated biosystems design
L Huynh, A Tsoukalas, M Köppe, I Tagkopoulos
ACS synthetic biology 2 (5), 263-273, 2013
Kinetic characterization of 100 glycoside hydrolase mutants enables the discovery of structural features correlated with kinetic constants
DA Carlin, RW Caster, X Wang, SA Betzenderfer, CX Chen, VM Duong, ...
PloS one 11 (1), e0147596, 2016
The genetic and transcriptional basis of short and long term adaptation across multiple stresses in Escherichia coli
V Zorraquino, M Kim, N Rai, I Tagkopoulos
Molecular Biology and Evolution 34 (3), 707-717, 2017
Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles
M Kim, V Zorraquino, I Tagkopoulos
PLoS computational biology 11 (3), e1004127, 2015
Preliminary techno-economic assessment of animal cell-based meat
D Risner, F Li, JS Fell, SA Pace, JB Siegel, I Tagkopoulos, ES Spang
Foods 10 (1), 3, 2020
Automatic design of synthetic gene circuits through mixed integer non-linear programming
L Huynh, J Kececioglu, M Köppe, I Tagkopoulos
PloS one 7 (4), e35529, 2012
The computational diet: a review of computational methods across diet, microbiome, and health
A Eetemadi, N Rai, BMP Pereira, M Kim, H Schmitz, I Tagkopoulos
Frontiers in Microbiology 11, 393, 2020
Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning
R Bradley, I Tagkopoulos, M Kim, Y Kokkinos, T Panagiotakos, J Kennedy, ...
Journal of veterinary internal medicine 33 (6), 2644-2656, 2019
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