| Learning optimal Bayesian networks: A shortest path perspective C Yuan, B Malone Journal of Artificial Intelligence Research 48, 23-65, 2013 | 218 | 2013 |
| Learning optimal Bayesian networks using A* search C Yuan, B Malone, X Wu IJCAI proceedings-international joint conference on artificial intelligence …, 2011 | 181 | 2011 |
| Empirical evaluation of scoring functions for Bayesian network model selection Z Liu, B Malone, C Yuan BMC bioinformatics 13 (Suppl 15), S14, 2012 | 148 | 2012 |
| m6A-mRNA methylation regulates cardiac gene expression and cellular growth V Kmietczyk, E Riechert, L Kalinski, E Boileau, E Malovrh, B Malone, ... Life science alliance 2 (2), 2019 | 125 | 2019 |
| Polycomb Group Gene OsFIE2 Regulates Rice (Oryza sativa) Seed Development and Grain Filling via a Mechanism Distinct from Arabidopsis BRR Nallamilli, J Zhang, H Mujahid, BM Malone, SM Bridges, Z Peng PLoS genetics 9 (3), e1003322, 2013 | 106 | 2013 |
| Bayesian prediction of RNA translation from ribosome profiling B Malone, I Atanassov, F Aeschimann, X Li, H Großhans, C Dieterich Nucleic acids research 45 (6), 2960-2972, 2017 | 89 | 2017 |
| Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs B Malone, B Simovski, C Moliné, J Cheng, M Gheorghe, H Fontenelle, ... Scientific reports 10 (1), 22375, 2020 | 83 | 2020 |
| Learning optimal bounded treewidth Bayesian networks via maximum satisfiability J Berg, M Järvisalo, B Malone Artificial Intelligence and Statistics, 86-95, 2014 | 76 | 2014 |
| Comparison of four ChIP-Seq analytical algorithms using rice endosperm H3K27 trimethylation profiling data BM Malone, F Tan, SM Bridges, Z Peng PloS one 6 (9), e25260, 2011 | 73 | 2011 |
| Monitoring cell-type–specific gene expression using ribosome profiling in vivo during cardiac hemodynamic stress S Doroudgar, C Hofmann, E Boileau, B Malone, E Riechert, AA Gorska, ... Circulation research 125 (4), 431-448, 2019 | 64 | 2019 |
| BERTMHC: improved MHC–peptide class II interaction prediction with transformer and multiple instance learning J Cheng, K Bendjama, K Rittner, B Malone Bioinformatics 37 (22), 4172-4179, 2021 | 63 | 2021 |
| An improved admissible heuristic for learning optimal Bayesian networks C Yuan, B Malone arXiv preprint arXiv:1210.4913, 2012 | 58 | 2012 |
| Improving the scalability of optimal Bayesian network learning with external-memory frontier breadth-first branch and bound search B Malone, C Yuan, EA Hansen, S Bridges arXiv preprint arXiv:1202.3744, 2012 | 58 | 2012 |
| Tightening bounds for Bayesian network structure learning X Fan, C Yuan, B Malone Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 57 | 2014 |
| Memory-efficient dynamic programming for learning optimal Bayesian networks B Malone, C Yuan, E Hansen Proceedings of the AAAI Conference on Artificial Intelligence 25 (1), 1057-1062, 2011 | 57 | 2011 |
| Finding Optimal Bayesian Network Structures with Constraints Learned from Data. X Fan, BM Malone, C Yuan UAI, 200-209, 2014 | 54 | 2014 |
| The proteogenomic mapping tool WS Sanders, N Wang, SM Bridges, BM Malone, YS Dandass, ... BMC bioinformatics 12, 1-7, 2011 | 48 | 2011 |
| Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction B Malone, K Kangas, M Järvisalo, M Koivisto, P Myllymäki Machine Learning 107, 247-283, 2018 | 39 | 2018 |
| Knowledge graph completion to predict polypharmacy side effects B Malone, A García-Durán, M Niepert International conference on data integration in the life sciences, 144-149, 2018 | 35 | 2018 |
| Evaluating anytime algorithms for learning optimal Bayesian networks B Malone, C Yuan arXiv preprint arXiv:1309.6844, 2013 | 35 | 2013 |