| Year |
Citation |
Score |
| 2020 |
Yu JS, Bagheri N. Agent-Based Models Predict Emergent Behavior of Heterogeneous Cell Populations in Dynamic Microenvironments. Frontiers in Bioengineering and Biotechnology. 8: 249. PMID 32596213 DOI: 10.3389/Fbioe.2020.00249 |
0.336 |
|
| 2020 |
Bernasek SM, Peláez N, Carthew RW, Bagheri N, Amaral LAN. Fly-QMA: Automated analysis of mosaic imaginal discs in Drosophila. Plos Computational Biology. 16: e1007406. PMID 32126077 DOI: 10.1371/Journal.Pcbi.1007406 |
0.308 |
|
| 2020 |
Donahue PS, Draut JW, Muldoon JJ, Edelstein HI, Bagheri N, Leonard JN. The COMET toolkit for composing customizable genetic programs in mammalian cells. Nature Communications. 11: 779. PMID 32034124 DOI: 10.1038/S41467-019-14147-5 |
0.302 |
|
| 2019 |
Xue AY, Yu AM, Lucks JB, Bagheri N. DUETT quantitatively identifies known and novel events in nascent RNA structural dynamics from chemical probing data. Bioinformatics (Oxford, England). PMID 31389563 DOI: 10.1093/Bioinformatics/Btz449 |
0.49 |
|
| 2019 |
Finkle JD, Bagheri N. Hybrid analysis of gene dynamics predicts context specific expression and offers regulatory insights. Bioinformatics (Oxford, England). PMID 30994899 DOI: 10.1093/Bioinformatics/Btz256 |
0.304 |
|
| 2019 |
Yamankurt G, Berns EJ, Xue A, Lee A, Bagheri N, Mrksich M, Mirkin CA. Exploration of the nanomedicine-design space with high-throughput screening and machine learning. Nature Biomedical Engineering. 3: 318-327. PMID 30952978 DOI: 10.1038/S41551-019-0351-1 |
0.504 |
|
| 2019 |
Muldoon JJ, Yu JS, Fassia MK, Bagheri N. Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants. Bioinformatics (Oxford, England). PMID 30932143 DOI: 10.1093/Bioinformatics/Btz105 |
0.34 |
|
| 2018 |
Finkle JD, Wu JJ, Bagheri N. Windowed Granger causal inference strategy improves discovery of gene regulatory networks. Proceedings of the National Academy of Sciences of the United States of America. PMID 29440433 DOI: 10.1073/Pnas.1710936115 |
0.482 |
|
| 2017 |
Hartfield RM, Schwarz KA, Muldoon JJ, Bagheri N, Leonard JN. Multiplexing engineered receptors for multiparametric evaluation of environmental ligands. Acs Synthetic Biology. PMID 28771312 DOI: 10.1021/Acssynbio.6B00279 |
0.33 |
|
| 2017 |
Xue AY, Szymczak LC, Mrksich M, Bagheri N. Machine learning on SAMDI mass spectrometry signal to noise ratio improves peptide array designs. Analytical Chemistry. PMID 28719743 DOI: 10.1021/Acs.Analchem.7B01728 |
0.481 |
|
| 2016 |
Yu JS, Xue AY, Redei EE, Bagheri N. A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder. Translational Psychiatry. 6: e931. PMID 27779627 DOI: 10.1038/Tp.2016.198 |
0.503 |
|
| 2015 |
Ciaccio MF, Chen VC, Jones RB, Bagheri N. The DIONESUS algorithm provides scalable and accurate reconstruction of dynamic phosphoproteomic networks to reveal new drug targets. Integrative Biology : Quantitative Biosciences From Nano to Macro. 7: 776-91. PMID 26057728 DOI: 10.1039/C5Ib00065C |
0.696 |
|
| 2014 |
Duncan MT, Shin S, Wu JJ, Mays Z, Weng S, Bagheri N, Miller WM, Shea LD. Dynamic transcription factor activity profiles reveal key regulatory interactions during megakaryocytic and erythroid differentiation. Biotechnology and Bioengineering. 111: 2082-94. PMID 24853077 DOI: 10.1002/Bit.25262 |
0.576 |
|
| 2014 |
Ciaccio MF, Finkle JD, Xue AY, Bagheri N. A systems approach to integrative biology: an overview of statistical methods to elucidate association and architecture. Integrative and Comparative Biology. 54: 296-306. PMID 24813462 DOI: 10.1093/Icb/Icu037 |
0.688 |
|
| 2012 |
Han Q, Bagheri N, Bradshaw EM, Hafler DA, Lauffenburger DA, Love JC. Polyfunctional responses by human T cells result from sequential release of cytokines. Proceedings of the National Academy of Sciences of the United States of America. 109: 1607-12. PMID 22160692 DOI: 10.1073/Pnas.1117194109 |
0.472 |
|
| 2011 |
Bagheri N, Shiina M, Lauffenburger DA, Korn WM. A dynamical systems model for combinatorial cancer therapy enhances oncolytic adenovirus efficacy by MEK-inhibition. Plos Computational Biology. 7: e1001085. PMID 21379332 DOI: 10.1371/Journal.Pcbi.1001085 |
0.486 |
|
| 2010 |
Shiina M, Bagheri N, Lauffenburger D, Korn WM. Abstract 3494: Experimentally validated modeling for optimizing therapeutic combinations of oncolytic adenoviruses and MEK inhibitor Cancer Research. 70: 3494-3494. DOI: 10.1158/1538-7445.Am10-3494 |
0.469 |
|
| 2010 |
Hildebrandt S, Bagheri N, Gunawan R, Mirsky H, Shoemaker J, Taylor S, Petzold L, Doyle FJ. Systems Analysis for Systems Biology Systems Biomedicine. 249-272. DOI: 10.1016/B978-0-12-372550-9.00010-9 |
0.674 |
|
| 2008 |
Bagheri N, Lawson MJ, Stelling J, Doyle FJ. Modeling the Drosophila melanogaster circadian oscillator via phase optimization. Journal of Biological Rhythms. 23: 525-37. PMID 19060261 DOI: 10.1177/0748730408325041 |
0.504 |
|
| 2008 |
Bagheri N, Stelling J, Doyle FJ. Circadian phase resetting via single and multiple control targets. Plos Computational Biology. 4: e1000104. PMID 18795146 DOI: 10.1371/Journal.Pcbi.1000104 |
0.493 |
|
| 2008 |
Bagheri N, Taylor SR, Meeker K, Petzold LR, Doyle FJ. Synchrony and entrainment properties of robust circadian oscillators. Journal of the Royal Society, Interface / the Royal Society. 5: S17-28. PMID 18426774 DOI: 10.1098/Rsif.2008.0045.Focus |
0.505 |
|
| 2007 |
Bagheri N, Stelling J, Doyle FJ. Quantitative performance metrics for robustness in circadian rhythms. Bioinformatics (Oxford, England). 23: 358-64. PMID 17158515 DOI: 10.1093/Bioinformatics/Btl627 |
0.521 |
|
| 2007 |
Bagheri N, Stelling J, Doyle FJ. Circadian phase entrainment via nonlinear model predictive control International Journal of Robust and Nonlinear Control. 17: 1555-1571. DOI: 10.1002/Rnc.1209 |
0.475 |
|
| 2006 |
Doyle FJ, Gunawan R, Bagheri N, Mirsky H, To TL. Circadian rhythm: A natural, robust, multi-scale control system Computers and Chemical Engineering. 30: 1700-1711. DOI: 10.1016/J.Compchemeng.2006.05.029 |
0.613 |
|
| Show low-probability matches. |
