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David Heckmann - Publications

Affiliations: 
2019 University of California, San Diego, La Jolla, CA 
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10 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2021 Sastry AV, Hu A, Heckmann D, Poudel S, Kavvas E, Palsson BO. Independent component analysis recovers consistent regulatory signals from disparate datasets. Plos Computational Biology. 17: e1008647. PMID 33529205 DOI: 10.1371/journal.pcbi.1008647  0.766
2020 Heckmann D, Campeau A, Lloyd CJ, Phaneuf PV, Hefner Y, Carrillo-Terrazas M, Feist AM, Gonzalez DJ, Palsson BO. Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers. Proceedings of the National Academy of Sciences of the United States of America. PMID 32873645 DOI: 10.1073/Pnas.2001562117  0.735
2020 Phaneuf PV, Yurkovich JT, Heckmann D, Wu M, Sandberg TE, King ZA, Tan J, Palsson BO, Feist AM. Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity. Bmc Genomics. 21: 514. PMID 32711472 DOI: 10.1186/S12864-020-06920-4  0.701
2020 Kavvas ES, Yang L, Monk JM, Heckmann D, Palsson BO. A biochemically-interpretable machine learning classifier for microbial GWAS. Nature Communications. 11: 2580. PMID 32444610 DOI: 10.1038/S41467-020-16310-9  0.667
2020 Mih N, Monk JM, Fang X, Catoiu E, Heckmann D, Yang L, Palsson BO. Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes. Bmc Bioinformatics. 21: 162. PMID 32349661 DOI: 10.1186/S12859-020-3505-Y  0.768
2019 Yang L, Mih N, Anand A, Park JH, Tan J, Yurkovich JT, Monk JM, Lloyd CJ, Sandberg TE, Seo SW, Kim D, Sastry AV, Phaneuf P, Gao Y, Broddrick JT, ... ... Heckmann D, et al. Cellular responses to reactive oxygen species are predicted from molecular mechanisms. Proceedings of the National Academy of Sciences of the United States of America. PMID 31270234 DOI: 10.1073/Pnas.1905039116  0.535
2018 Heckmann D, Zielinski DC, Palsson BO. Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates. Nature Communications. 9: 5270. PMID 30532008 DOI: 10.1038/S41467-018-07649-1  0.651
2018 Heckmann D, Lloyd CJ, Mih N, Ha Y, Zielinski DC, Haiman ZB, Desouki AA, Lercher MJ, Palsson BO. Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nature Communications. 9: 5252. PMID 30531987 DOI: 10.1038/S41467-018-07652-6  0.739
2018 Kavvas ES, Catoiu E, Mih N, Yurkovich JT, Seif Y, Dillon N, Heckmann D, Anand A, Yang L, Nizet V, Monk JM, Palsson BO. Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nature Communications. 9: 4306. PMID 30333483 DOI: 10.1038/S41467-018-06634-Y  0.691
2017 Kleinmanns JA, Schatlowski N, Heckmann D, Schubert D. BLISTER Regulates Polycomb-Target Genes, Represses Stress-Regulated Genes and Promotes Stress Responses in . Frontiers in Plant Science. 8: 1530. PMID 28955347 DOI: 10.3389/fpls.2017.01530  0.745
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