Colby T. Ford
Affiliations: | University of North Carolina, Charlotte, Charlotte, NC, United States |
Area:
Human Genetics, Genomics, Machine Learning, Applied Mathematics, Computer Science, Infectious DiseasesGoogle:
"Colby Ford"
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Publications
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Ford CT, Yasa S, Obeid K, et al. (2025) Large-scale computational modelling of H5 influenza variants against HA1-neutralising antibodies. Ebiomedicine. 114: 105632 |
Lee H, Lee KS, Hsu CH, et al. (2024) Reply to: Embracing the taxonomic and topological stability of phylogenomics. Scientific Reports. 14: 4094 |
Lee H, Lee KS, Hsu CH, et al. (2023) Phylogeny, ancestral ranges and reclassification of sand dollars. Scientific Reports. 13: 10199 |
Dieng CC, Ford CT, Lerch A, et al. (2023) Genetic variations of Plasmodium falciparum circumsporozoite protein and the impact on interactions with human immunoproteins and malaria vaccine efficacy. Infection, Genetics and Evolution : Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases. 110: 105418 |
Ford A, Kepple D, Williams J, et al. (2021) Gene Polymorphisms Among Geographical Isolates and the Potential as New Biomarkers for Gametocyte Detection. Frontiers in Cellular and Infection Microbiology. 11: 789417 |
Ford CT, Janies D. (2020) Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria. F1000research. 9: 62 |
Ford CT, Zenarosa GL, Smith KB, et al. (2020) Genetic capitalism and stabilizing selection of antimicrobial resistance genotypes in Escherichia coli. Cladistics : the International Journal of the Willi Hennig Society. 36: 348-357 |
Ford A, Kepple D, Abagero BR, et al. (2020) Whole genome sequencing of Plasmodium vivax isolates reveals frequent sequence and structural polymorphisms in erythrocyte binding genes. Plos Neglected Tropical Diseases. 14: e0008234 |
Wen J, Ford CT, Janies D, et al. (2020) A Parallelized Strategy for Epistasis Analysis Based on Empirical Bayesian Elastic Net Models. Bioinformatics (Oxford, England) |
Ford CT, Janies D. (2020) Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria F1000research. 9: 62 |