Iftach Nachman
Iftach Nachman
Assistant professor of biology, Tel Aviv University
Verified email at tauex.tau.ac.il - Homepage
TitleCited byYear
Using Bayesian networks to analyze expression data
N Friedman, M Linial, I Nachman, D Pe'er
Journal of computational biology 7 (3-4), 601-620, 2000
Tissue classification with gene expression profiles
A Ben-Dor, L Bruhn, N Friedman, I Nachman, M Schummer, Z Yakhini
Proceedings of the fourth annual international conference on Computational …, 2000
Learning bayesian network structure from massive datasets: the «sparse candidate «algorithm
N Friedman, I Nachman, D Peér
Proceedings of the Fifteenth conference on Uncertainty in artificial …, 1999
Inferring quantitative models of regulatory networks from expression data
I Nachman, A Regev, N Friedman
Bioinformatics 20 (suppl_1), i248-i256, 2004
Dynamic single-cell imaging of direct reprogramming reveals an early specifying event
ZD Smith, I Nachman, A Regev, A Meissner
Nature biotechnology 28 (5), 521, 2010
Dissecting timing variability in yeast meiosis
I Nachman, A Regev, S Ramanathan
Cell 131 (3), 544-556, 2007
Gaussian process networks
N Friedman, I Nachman
Proceedings of the Sixteenth conference on Uncertainty in artificial …, 2000
" Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks.
G Elidan, I Nachman, N Friedman
Journal of Machine Learning Research 8 (8), 2007
Aggregation of polyQ proteins is increased upon yeast aging and affected by Sir2 and Hsf1: novel quantitative biochemical and microscopic assays
A Cohen, L Ross, I Nachman, S Bar-Nun
PloS one 7 (9), e44785, 2012
Epigenetic predisposition to reprogramming fates in somatic cells
M Pour, I Pilzer, R Rosner, ZD Smith, A Meissner, I Nachman
EMBO reports 16 (3), 370-378, 2015
Event timing at the single-cell level
E Yurkovsky, I Nachman
Briefings in functional genomics 12 (2), 90-98, 2012
Ideal parent Structure learning for continuous variable networks
I Nachman, G Elidan, N Friedman
Proceedings of the 20th conference on Uncertainty in artificial intelligence …, 2004
Expression of Pseudomonas syringae type III effectors in yeast under stress conditions reveals that HopX1 attenuates activation of the high osmolarity glycerol MAP kinase pathway
D Salomon, E Bosis, D Dar, I Nachman, G Sessa
Microbiology 158 (11), 2859-2869, 2012
Bifunctional Carbon‐Dot‐WS2 Nanorods for Photothermal Therapy and Cell Imaging
S Nandi, SK Bhunia, L Zeiri, M Pour, I Nachman, D Raichman, ...
Chemistry–A European Journal 23 (4), 963-969, 2017
Integrated live imaging and molecular profiling of embryoid bodies reveals a synchronized progression of early differentiation
J Boxman, N Sagy, S Achanta, R Vadigepalli, I Nachman
Scientific reports 6, 31623, 2016
Using bayesian networks to analyze expression data
N Friedman, M Linial, I Nachman, D Pe'er
BRNI: Modular analysis of transcriptional regulatory programs
I Nachman, A Regev
BMC bioinformatics 10 (1), 155, 2009
Water-Transfer slows aging in Saccharomyces cerevisiae
A Cohen, E Weindling, E Rabinovich, I Nachman, S Fuchs, S Chuartzman, ...
PloS one 11 (2), e0148650, 2016
Control of relative timing and stoichiometry by a master regulator
Y Goldschmidt, E Yurkovsky, A Reif, R Rosner, A Akiva, I Nachman
PloS one 10 (5), e0127339, 2015
Probabilistic modeling of gene regulatory networks from data
I Nachman
Hebrew University of Jerusalem, 2004
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