Scaling tree-based automated machine learning to biomedical big data with a feature set selector TT Le, W Fu, JH Moore Bioinformatics 36 (1), 250-256, 2020 | 463 | 2020 |
Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction DK Wells, MM van Buuren, KK Dang, VM Hubbard-Lucey, KCF Sheehan, ... Cell 183 (3), 818-834. e13, 2020 | 431 | 2020 |
Characterization of a novel chicken muscle disorder through differential gene expression and pathway analysis using RNA-sequencing MF Mutryn, EM Brannick, W Fu, WR Lee, B Abasht BMC genomics 16 (1), 399, 2015 | 327 | 2015 |
ATF4 couples MYC-dependent translational activity to bioenergetic demands during tumour progression F Tameire, II Verginadis, NM Leli, C Polte, CS Conn, R Ojha, ... Nature cell biology 21 (7), 889-899, 2019 | 229 | 2019 |
PMLB v1. 0: an open-source dataset collection for benchmarking machine learning methods JD Romano, TT Le, W La Cava, JT Gregg, DJ Goldberg, P Chakraborty, ... Bioinformatics 38 (3), 878-880, 2022 | 113 | 2022 |
Investigating the parameter space of evolutionary algorithms M Sipper, W Fu, K Ahuja, JH Moore BioData Mining 11 (1), 2, 2018 | 98 | 2018 |
A genome-wide detection of copy number variations using SNP genotyping arrays in swine J Wang, J Jiang, W Fu, L Jiang, X Ding, JF Liu, Q Zhang BMC genomics 13, 1-10, 2012 | 98 | 2012 |
Linkage disequilibrium in crossbred and pure line chickens W Fu, JCM Dekkers, WR Lee, B Abasht Genetics Selection Evolution 47, 1-12, 2015 | 65 | 2015 |
Immunological ignorance is an enabling feature of the oligo-clonal T cell response to melanoma neoantigens GP Linette, M Becker-Hapak, ZL Skidmore, ML Baroja, C Xu, J Hundal, ... Proceedings of the National Academy of Sciences 116 (47), 23662-23670, 2019 | 60 | 2019 |
Bayesian methods for estimating GEBVs of threshold traits CL Wang, XD Ding, JY Wang, JF Liu, WX Fu, Z Zhang, ZJ Yin, Q Zhang Heredity 110 (3), 213-219, 2013 | 58 | 2013 |
Genome‐wide association studies for hematological traits in swine JY Wang, YR Luo, WX Fu, X Lu, JP Zhou, XD Ding, JF Liu, Q Zhang Animal Genetics 44 (1), 34-43, 2013 | 54 | 2013 |
A genome-wide association study identifies two novel promising candidate genes affecting Escherichia coli F4ab/F4ac susceptibility in swine WX Fu, Y Liu, X Lu, XY Niu, XD Ding, JF Liu, Q Zhang PLoS One 7 (3), e32127, 2012 | 52 | 2012 |
Detection of genomic signatures of recent selection in commercial broiler chickens W Fu, WR Lee, B Abasht BMC genetics 17, 1-10, 2016 | 36 | 2016 |
A system for accessible artificial intelligence RS Olson, M Sipper, WL Cava, S Tartarone, S Vitale, W Fu, ... Genetic programming theory and practice XV, 121-134, 2018 | 35 | 2018 |
Genome‐wide association study for pigmentation traits in Chinese Holstein population Y Fan, P Wang, W Fu, T Dong, C Qi, L Liu, G Guo, C Li, X Cui, S Zhang, ... Animal genetics 45 (5), 740-744, 2014 | 35 | 2014 |
A Multiple-SNP Approach for Genome-Wide Association Study of Milk Production Traits in Chinese Holstein Cattle M Fang, W Fu, D Jiang, Q Zhang, D Sun, X Ding, J Liu PLOS ONE 9 (8), e99544, 2014 | 34 | 2014 |
Genome-wide association study for T lymphocyte subpopulations in swine X Lu, WX Fu, YR Luo, XD Ding, JP Zhou, Y Liu, JF Liu, Q Zhang BMC genomics 13, 1-10, 2012 | 34 | 2012 |
TPOT-NN: Augmenting tree-based automated machine learning with neural network estimators JD Romano, TT Le, W Fu, JH Moore Genetic Programming and Evolvable Machines 22 (2), 207-227, 2021 | 27 | 2021 |
Genome-wide association study for cytokines and immunoglobulin G in swine X Lu, JF Liu, WX Fu, JP Zhou, YR Luo, XD Ding, Y Liu, Q Zhang PLoS One 8 (10), e74846, 2013 | 26 | 2013 |
Gene silencing of porcine MUC13 and ITGB5: candidate genes towards Escherichia coli F4ac adhesion C Zhou, Z Liu, Y Liu, W Fu, X Ding, J Liu, Y Yu, Q Zhang PLoS One 8 (7), e70303, 2013 | 24 | 2013 |