Frank Emmert-Streib
Frank Emmert-Streib
Professor, Tampere University, Predictive Society and Data Analytics Lab
Verified email at - Homepage
Cited by
Cited by
An introductory review of deep learning for prediction models with big data
F Emmert-Streib, Z Yang, H Feng, S Tripathi, M Dehmer
Frontiers in Artificial Intelligence 3, 4, 2020
Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks
F Emmert-Streib, M Dehmer, B Haibe-Kains
Frontiers in cell and developmental biology 2, 38, 2014
A review of connectivity map and computational approaches in pharmacogenomics
A Musa, LS Ghoraie, SD Zhang, G Glazko, O Yli-Harja, M Dehmer, ...
Briefings in bioinformatics 19 (3), 506-523, 2018
Fifty years of graph matching, network alignment and network comparison
F Emmert-Streib, M Dehmer, Y Shi
Information sciences 346, 180-197, 2016
Inferring the conservative causal core of gene regulatory networks
G Altay, F Emmert-Streib
BMC systems biology 4, 1-13, 2010
Statistical modelling of molecular descriptors in QSAR/QSPR
K Varmuza, M Dehmer, D Bonchev
Wiley Online Library, 2012
Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
A Holzinger, M Dehmer, F Emmert-Streib, R Cucchiara, I Augenstein, ...
Information Fusion 79, 263-278, 2022
Harnessing naturally randomized transcription to infer regulatory relationships among genes
LS Chen, F Emmert-Streib, JD Storey
Genome biology 8, 1-13, 2007
Networks for systems biology: conceptual connection of data and function
F Emmert-Streib, M Dehmer
IET systems biology 5 (3), 185-207, 2011
Statistical inference and reverse engineering of gene regulatory networks from observational expression data
F Emmert-Streib, GV Glazko, G Altay, R de Matos Simoes
Frontiers in genetics 3, 8, 2012
Named entity recognition and relation detection for biomedical information extraction
N Perera, M Dehmer, F Emmert-Streib
Frontiers in cell and developmental biology 8, 673, 2020
Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets
GV Glazko, F Emmert-Streib
Bioinformatics 25 (18), 2348-2354, 2009
Bagging statistical network inference from large-scale gene expression data
R de Matos Simoes, F Emmert-Streib
PloS one 7 (3), e33624, 2012
Pathway analysis of expression data: deciphering functional building blocks of complex diseases
F Emmert-Streib, GV Glazko
PLoS computational biology 7 (5), e1002053, 2011
On entropy-based molecular descriptors: Statistical analysis of real and synthetic chemical structures
M Dehmer, K Varmuza, S Borgert, F Emmert-Streib
Journal of chemical information and modeling 49 (7), 1655-1663, 2009
Structural analysis of complex networks
M Dehmer
Springer Science & Business Media, 2010
Revealing differences in gene network inference algorithms on the network level by ensemble methods
G Altay, F Emmert-Streib
Bioinformatics 26 (14), 1738-1744, 2010
Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets
Y Rahmatallah, F Emmert-Streib, G Glazko
Bioinformatics 30 (3), 360-368, 2014
Understanding statistical hypothesis testing: The logic of statistical inference
F Emmert-Streib, M Dehmer
Machine Learning and Knowledge Extraction 1 (3), 945-962, 2019
Analysis of microarray data: a network-based approach
F Emmert-Streib, M Dehmer
Vch Verlagsgesellschaft Mbh, 2008
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