| Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models D Heckmann, CJ Lloyd, N Mih, Y Ha, DC Zielinski, ZB Haiman, ... Nature communications 9 (1), 1-10, 2018 | 179 | 2018 |
| Sybil–efficient constraint-based modelling in R G Gelius-Dietrich, AA Desouki, CJ Fritzemeier, MJ Lercher BMC systems biology 7, 1-8, 2013 | 140 | 2013 |
| CycleFreeFlux: efficient removal of thermodynamically infeasible loops from flux distributions AA Desouki, F Jarre, G Gelius-Dietrich, MJ Lercher Bioinformatics 31 (13), 2159-2165, 2015 | 68 | 2015 |
| Flux balance analysis with or without molecular crowding fails to predict two thirds of experimentally observed epistasis in yeast D Alzoubi, AA Desouki, MJ Lercher Scientific reports 9 (1), 11837, 2019 | 14 | 2019 |
| Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nat Commun. 2018; 9 (1): 5252 D Heckmann, CJ Lloyd, N Mih, Y Ha, DC Zielinski, ZB Haiman, ... | 13 | |
| Alleles of a gene differ in pleiotropy, often mediated through currency metabolite production, in E. coli and yeast metabolic simulations D Alzoubi, AA Desouki, MJ Lercher Scientific Reports 8 (1), 17252, 2018 | 8 | 2018 |
| Algorithms for improving the predictive power of flux balance analysis A Desouki | 8 | 2016 |
| Ranking on very large knowledge graphs AA Desouki, M Röder, AC Ngonga Ngomo Proceedings of the 30th ACM Conference on Hypertext and Social Media, 163-171, 2019 | 6 | 2019 |
| SYNTHG: Mimicking RDF Graphs Using Tensor Factorization AA Desouki, F Conrads, M Röder, ACN Ngomo ICSC, 2021 | 3 | 2021 |
| ORCA-a benchmark for data web crawlers M Röder, G de Souza, D Kuchelev, AA Desouki, ACN Ngomo 2021 IEEE 15th International Conference on Semantic Computing (ICSC), 272-279, 2021 | 1 | 2021 |
| topFiberM: Scalable and Efficient Boolean Matrix Factorization AA Desouki, M Röder, ACN Ngomo arXiv preprint arXiv:1903.10326, 2019 | 1 | 2019 |
| rBMF: Boolean Matrix Factorization AA Desouki https://cran.r-project.org/web/packages/rBMF/index.html, 2020 | | 2020 |
| Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nat. Commun. 9, 5252 D Heckmann, CJ Lloyd, N Mih, Y Ha, DC Zielinski, ZB Haiman, ... | | 2018 |
| RDFTensor-package: Different Tensor Factorization (Decomposition) Techniques for RDF data AA Desouki | | 2018 |
| sybilccFBA: Cost Constrained FLux Balance Analysis: MetabOlic Modeling with ENzyme kineTics (MOMENT) AA Desouki https://CRAN.R-project.org/package=sybilccFBA, 2015 | | 2015 |
| sybilEFBA: Using Gene Expression Data to Improve Flux Balance Analysis Predictions A Amer Desouki http://cran.r-project.org/package=sybilEFBA, 2015 | | 2015 |
| A NEW COMPRESSION ALGORITHM FOR DNA SEQUENCES USING DISCRETE WAVELET TRANSFORM EAMA Desouki Alexandria University, 2006 | | 2006 |
| 2021 IEEE 15th International Conference on Semantic Computing (ICSC)| 978-1-7281-8899-7/21/$31.00© 2021 IEEE| DOI: 10.1109/ICSC50631. 2021.00082 GO Adebayo, V Agarwal, YC Alegre, L Alfano, D Alvarez-Coello, G Aniba, ... | | |
| 6 Manuscripts G Gelius-Dietrich, AA Desouki, CJ Fritzemeier, MJ Lercher Environmental Adaptation of Bacteria: Insights from Genome-Scale Metabolic …, 0 | | |
Martin J. LercherProfessor of Computational Cell Biology, Heinrich Heine University DüsseldorfAdresse e-mail validée de hhu.de
