| InnateDB: facilitating systems-level analyses of the mammalian innate immune response DJ Lynn, GL Winsor, C Chan, N Richard, MR Laird, A Barsky, JL Gardy, ... Molecular Systems Biology 4 (1), 2008 | 415 | 2008 |
| Application of machine learning algorithms in plant breeding: predicting yield from hyperspectral reflectance in soybean M Yoosefzadeh-Najafabadi, HJ Earl, D Tulpan, J Sulik, M Eskandari Frontiers in plant science 11, 1-14, 2021 | 155 | 2021 |
| Stochastic local search algorithms for DNA word design DC Tulpan, HH Hoos, AE Condon DNA Computing: 8th International Workshop on DNA-Based Computers, DNA8 …, 2003 | 135 | 2003 |
| MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures D Tulpan, S Leger, L Belliveau, A Culf, M Cuperlovic-Culf BMC Bioinformatics 12 (1), 400, 2011 | 128 | 2011 |
| Thermodynamically based DNA strand design D Tulpan, M Andronescu, SB Chang, MR Shortreed, A Condon, HH Hoos, ... Nucleic Acids Research 33 (15), 4951, 2005 | 79 | 2005 |
| Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data JL Ellis, M Jacobs, J Dijkstra, H Van Laar, JP Cant, D Tulpan, N Ferguson Animal 14 (S2), s223-s237, 2020 | 67 | 2020 |
| Hybrid randomised neighbourhoods improve stochastic local search for DNA code design DC Tulpan, HH Hoos Advances in Artificial Intelligence: 16th Conference of the Canadian Society …, 2003 | 65 | 2003 |
| ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images Z Wang, S Shadpour, E Chan, V Rotondo, KM Wood, D Tulpan Journal of Animal Science 99 (2), skab022, 2021 | 58 | 2021 |
| Using hybrid artificial intelligence and evolutionary optimization algorithms for estimating soybean yield and fresh biomass using hyperspectral vegetation indices M Yoosefzadeh-Najafabadi, D Tulpan, M Eskandari Remote Sensing 13 (13), 2555, 2021 | 57 | 2021 |
| A review of traditional and machine learning methods applied to animal breeding S Nayeri, M Sargolzaei, D Tulpan Animal health research reviews 20 (1), 31-46, 2019 | 56 | 2019 |
| Exogenous Abscisic Acid and Gibberellic Acid Elicit Opposing Effects on Fusarium graminearum Infection in Wheat LM Buhrow, D Cram, D Tulpan, NA Foroud, MC Loewen Phytopathology 106 (9), 986-996, 2016 | 55 | 2016 |
| Free energy estimation of short DNA duplex hybridizations D Tulpan, M Andronescu, S Leger BMC Bioinformatics 11 (1), 105, 2010 | 54 | 2010 |
| Analysis of MAPK and MAPKK gene families in wheat and related Triticeae species RK Goyal, D Tulpan, N Chomistek, D González-Peña Fundora, C West, ... BMC genomics 19, 1-26, 2018 | 53 | 2018 |
| A thermodynamic approach to designing structure-free combinatorial DNA word sets MR Shortreed, SB Chang, DG Hong, M Phillips, B Campion, DC Tulpan, ... Nucleic Acids Research 33 (15), 4965, 2005 | 50 | 2005 |
| Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits M Yoosefzadeh-Najafabadi, D Tulpan, M Eskandari Plos one 16 (4), e0250665, 2021 | 49 | 2021 |
| Non-intrusive patient monitoring of Alzheimer's disease subjects using wireless sensor networks M Avvenuti, C Baker, J Light, D Tulpan, A Vecchio 2009 World Congress on Privacy, Security, Trust and the Management of e …, 2009 | 43 | 2009 |
| In pursuit of a better broiler: growth, efficiency, and mortality of 16 strains of broiler chickens S Torrey, M Mohammadigheisar, MN Dos Santos, D Rothschild, ... Poultry Science 100 (3), 100955, 2021 | 42 | 2021 |
| Genome-wide association studies of soybean yield-related hyperspectral reflectance bands using machine learning-mediated data integration methods M Yoosefzadeh-Najafabadi, S Torabi, D Tulpan, I Rajcan, M Eskandari Frontiers in plant science 12, 777028, 2021 | 33 | 2021 |
| Machine-learning-based genome-wide association studies for uncovering QTL underlying soybean yield and its components M Yoosefzadeh-Najafabadi, M Eskandari, S Torabi, D Torkamaneh, ... International Journal of Molecular Sciences 23 (10), 5538, 2022 | 32 | 2022 |
| Thermodynamic post-processing versus GC-content pre-processing for DNA codes satisfying the Hamming distance and reverse-complement constraints D Tulpan, DH Smith, R Montemanni IEEE/ACM Transactions on Computational Biology and Bioinformatics 11 (2 …, 2014 | 29 | 2014 |
Serge LegerNational Research Council在 nrc.gc.ca 的电子邮件经过验证
