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 | 566 | 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 | 322 | 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 | 293 | 2018 |
Fifty years of graph matching, network alignment and network comparison F Emmert-Streib, M Dehmer, Y Shi Information sciences 346, 180-197, 2016 | 289 | 2016 |
Inferring the conservative causal core of gene regulatory networks G Altay, F Emmert-Streib BMC systems biology 4, 1-13, 2010 | 221 | 2010 |
Statistical modelling of molecular descriptors in QSAR/QSPR K Varmuza, M Dehmer, D Bonchev Wiley Online Library, 2012 | 199 | 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 | 165 | 2022 |
Harnessing naturally randomized transcription to infer regulatory relationships among genes LS Chen, F Emmert-Streib, JD Storey Genome biology 8, 1-13, 2007 | 161 | 2007 |
Networks for systems biology: conceptual connection of data and function F Emmert-Streib, M Dehmer IET systems biology 5 (3), 185-207, 2011 | 157 | 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 | 154 | 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 | 147 | 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 | 140 | 2009 |
Bagging statistical network inference from large-scale gene expression data R de Matos Simoes, F Emmert-Streib PloS one 7 (3), e33624, 2012 | 139 | 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 | 136 | 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 | 132 | 2009 |
Structural analysis of complex networks M Dehmer Springer Science & Business Media, 2010 | 130 | 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 | 126 | 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 | 120 | 2014 |
Analysis of microarray data: a network-based approach F Emmert-Streib, M Dehmer Vch Verlagsgesellschaft Mbh, 2008 | 119* | 2008 |
Understanding statistical hypothesis testing: The logic of statistical inference F Emmert-Streib, M Dehmer Machine Learning and Knowledge Extraction 1 (3), 945-962, 2019 | 117 | 2019 |