Johann Gregor Mendel: the victory of statistics over human imagination
Language English Country England, Great Britain Media print-electronic
Document type Journal Article, Review, Research Support, Non-U.S. Gov't
Grant support
MUNI/A/1370/2022
Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
PubMed
36755104
PubMed Central
PMC9909140
DOI
10.1038/s41431-023-01303-1
PII: 10.1038/s41431-023-01303-1
Knihovny.cz E-resources
- MeSH
- Genetics * MeSH
- Imagination * MeSH
- Humans MeSH
- Promoter Regions, Genetic MeSH
- Research Design MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
In 2022, we celebrated 200 years since the birth of Johann Gregor Mendel. Although his contributions to science went unrecognized during his lifetime, Mendel not only described the principles of monogenic inheritance but also pioneered the modern way of doing science based on precise experimental data acquisition and evaluation. Novel statistical and algorithmic approaches are now at the center of scientific work, showing that work that is considered marginal in one era can become a mainstream research approach in the next era. The onset of data-driven science caused a shift from hypothesis-testing to hypothesis-generating approaches in science. Mendel is remembered here as a promoter of this approach, and the benefits of big data and statistical approaches are discussed.
See more in PubMed
Kampourakis K. Mendel and the Path to Genetics: Portraying Science as a Social Process. Sci Educ. 2013;22:293–324. doi: 10.1007/s11191-010-9323-2. DOI
Abbott S, Fairbanks DJ. Experiments on Plant Hybrids by Gregor Mendel. Genetics. 2016;204:407–22. doi: 10.1534/genetics.116.195198. PubMed DOI PMC
Gasking EB. Why was Mendel’s Work Ignored? J Hist Ideas. 1959;20:60–84. doi: 10.2307/2707967. DOI
Mukherjee S. The Gene: An Intimate History. New York: Large Print Press [in English]; 2017.
Hudson TJ, Anderson W, Artez A, Barker AD, Bell C, Bernabé RR, et al. International network of cancer genome projects. Nature. 2010;464:993–8. doi: 10.1038/nature08987. PubMed DOI PMC
Campbell PJ, Getz G, Korbel JO, Stuart JM, Jennings JL, Stein LD, et al. Pan-cancer analysis of whole genomes. Nature. 2020;578:82–93. doi: 10.1038/s41586-020-1969-6. PubMed DOI PMC
Marabelle A, Le DT, Ascierto PA, Di Giacomo AM, De Jesus-Acosta A, Delord J-P, et al. Efficacy of Pembrolizumab in Patients With Noncolorectal High Microsatellite Instability/Mismatch Repair–Deficient Cancer: Results From the Phase II KEYNOTE-158 Study. J Clin Oncol. 2019;38:1–10. doi: 10.1200/JCO.19.02105. PubMed DOI PMC
Gerstung M, Papaemmanuil E, Martincorena I, Bullinger L, Gaidzik VI, Paschka P, et al. Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet. 2017;49:332–40. doi: 10.1038/ng.3756. PubMed DOI PMC
Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8:53. doi: 10.1186/s40537-021-00444-8. PubMed DOI PMC
Sakellaropoulos T, Vougas K, Narang S, Koinis F, Kotsinas A, Polyzos A, et al. A Deep Learning Framework for Predicting Response to Therapy in Cancer. Cell Rep. 2019;29:3367–3373.e3364. doi: 10.1016/j.celrep.2019.11.017. PubMed DOI
Yuan Y, Bar-Joseph Z. Deep learning for inferring gene relationships from single-cell expression data. Proc Natl Acad Sci. 2019;116:27151–8. doi: 10.1073/pnas.1911536116. PubMed DOI PMC
Savage N. Breaking into the black box of artificial intelligence. Nature. 2022. 10.1038/d41586-022-00858-1. PubMed
Zaritsky A, Jamieson AR, Welf ES, Nevarez A, Cillay J, Eskiocak U, et al. Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma. Cell Syst. 2021;12:733–747.e736. doi: 10.1016/j.cels.2021.05.003. PubMed DOI PMC
Ray M, Sable MN, Sarkar S, Hallur V. Essential interpretations of bioinformatics in COVID-19 pandemic. Meta Gene. 2021;27:100844–100844. doi: 10.1016/j.mgene.2020.100844. PubMed DOI PMC
Blassel L, Zhukova A, Villabona-Arenas CJ, Atkins KE, Hué S, Gascuel O. Drug resistance mutations in HIV: new bioinformatics approaches and challenges. Curr Opin Virol. 2021;51:56–64. doi: 10.1016/j.coviro.2021.09.009. PubMed DOI
Xu L, Ru X, Song R. Application of Machine Learning for Drug–Target Interaction Prediction. Front Genet. 2021;12:680117. doi: 10.3389/fgene.2021.680117. PubMed DOI PMC
Abelson S, Collord G, Ng SWK, Weissbrod O, Mendelson Cohen N, Niemeyer E, et al. Prediction of acute myeloid leukaemia risk in healthy individuals. Nature. 2018;559:400–4. doi: 10.1038/s41586-018-0317-6. PubMed DOI PMC
Gerstung M, Jolly C, Leshchiner I, Dentro SC, Gonzalez S, Rosebrock D, et al. The evolutionary history of 2,658 cancers. Nature. 2020;578:122–8. doi: 10.1038/s41586-019-1907-7. PubMed DOI PMC
Wu L, Han L, Li Q, Wang G, Zhang H, Li L. Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding. Mol Plant. 2021;14:77–94. doi: 10.1016/j.molp.2020.12.012. PubMed DOI
Park S, Min S, Choi H-S, Yoon S. deepMiRGene: Deep Neural Network based Precursor microRNA Prediction. ArXiv. 2016; abs/1605.00017.
Lee B, Baek J, Park S, Yoon S. deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks. 2016:434–42. 10.1145/2975167.2975212.
Chen Y, Li Y, Narayan R, Subramanian A, Xie X. Gene expression inference with deep learning. Bioinformatics. 2016;32:1832–9. doi: 10.1093/bioinformatics/btw074. PubMed DOI PMC
Singh R, Lanchantin J, Robins G, Qi Y. DeepChrome: deep-learning for predicting gene expression from histone modifications. Bioinformatics. 2016;32:i639–i648. doi: 10.1093/bioinformatics/btw427. PubMed DOI
Poplin R, Chang PC, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36:983–7. doi: 10.1038/nbt.4235. PubMed DOI
Hoffman GE, Bendl J, Girdhar K, Schadt EE, Roussos P. Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification. Nucleic Acids Res. 2019;47:10597–611. doi: 10.1093/nar/gkz808. PubMed DOI PMC
Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18:24. doi: 10.1186/s12874-018-0482-1. PubMed DOI PMC
Lee C, Zame W, Yoon J, van der Schaar M. DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks. Proceedings of the AAAI Conference on Artificial Intelligence. 2018;32. 10.1609/aaai.v32i1.11842.
Zhao L, Dong Q, Luo C, Wu Y, Bu D, Qi X, et al. DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis. Comput Struct Biotechnol J. 2021;19:2719–25. doi: 10.1016/j.csbj.2021.04.067. PubMed DOI PMC
Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C, Efron MJ, et al. Big Data: Astronomical or Genomical? PLoS Biol. 2015;13:e1002195. doi: 10.1371/journal.pbio.1002195. PubMed DOI PMC
Eraslan G, Avsec Ž, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet. 2019;20:389–403. doi: 10.1038/s41576-019-0122-6. PubMed DOI
Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet. 2019;51:12–18. doi: 10.1038/s41588-018-0295-5. PubMed DOI PMC
Grapov D, Fahrmann J, Wanichthanarak K, Khoomrung S. Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine. OMICS. 2018;22:630–6. doi: 10.1089/omi.2018.0097. PubMed DOI PMC
Webb S. Deep learning for biology. Nature. 2018;554:555–7. doi: 10.1038/d41586-018-02174-z. PubMed DOI
Koumakis L. Deep learning models in genomics; are we there yet? Comput Struct Biotechnol J. 2020;18:1466–73. doi: 10.1016/j.csbj.2020.06.017. PubMed DOI PMC
Benzon W. GPT-3: Waterloo or Rubicon? Here be Dragons, Version 4.1. 2022.
Radford A, Kim J, Hallacy C, Ramesh A, Goh G, Agarwal S. et al. Learning Transferable Visual Models From Natural Language Supervision. 2021. 10.48550/arXiv.2103.00020.