Advancing computational biology and bioinformatics research through open innovation competitions
Autoři:
Andrea Blasco aff001; Michael G. Endres aff001; Rinat A. Sergeev aff001; Anup Jonchhe aff004; N. J. Maximilian Macaluso aff004; Rajiv Narayan aff004; Ted Natoli aff004; Jin H. Paik aff001; Bryan Briney aff005; Chunlei Wu aff006; Andrew I. Su aff006; Aravind Subramanian aff004; Karim R. Lakhani aff001
Působiště autorů:
Laboratory for Innovation Science at Harvard, Harvard University, Cambridge, MA, United States of America
aff001; Harvard Business School, Harvard University, Boston, MA, United States of America
aff002; Institute for Quantitative Social Science, Harvard University, Cambridge, MA, United States of America
aff003; The Broad Institute, Cambridge, MA, United States of America
aff004; Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, CA, United States of America
aff005; Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States of America
aff006; National Bureau of Economic Research, Cambridge, MA, United States of America
aff007
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0222165
Souhrn
Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.
Klíčová slova:
Gene expression – Computational biology – Bioinformatics – Gene mapping – Algorithms – Antibodies – Memory – Computer architecture
Zdroje
1. Hughes AJ, Mornin JD, Biswas SK, Beck LE, Bauer DP, Raj A, et al. Quanti.us: a tool for rapid, flexible, crowd-based annotation of images. Nature methods. 2018;15(8):587–590. doi: 10.1038/s41592-018-0069-0 30065368
2. Cooper S, Khatib F, Treuille A, Barbero J, Lee J, Beenen M, et al. Predicting protein structures with a multiplayer online game. Nature. 2010;466(7307):756. doi: 10.1038/nature09304 20686574
3. Saez-Rodriguez J, Costello JC, Friend SH, Kellen MR, Mangravite L, Meyer P, et al. Crowdsourcing biomedical research: leveraging communities as innovation engines. Nature reviews Genetics. 2016;17(8):470–486. doi: 10.1038/nrg.2016.69 27418159
4. Hill SM, et al. Inferring causal molecular networks: empirical assessment through a community-based effort. Nature methods. 2016;13(4):310–318. doi: 10.1038/nmeth.3773 26901648
5. Costello JC, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology. 2014;32(12):1202–1212. doi: 10.1038/nbt.2877 24880487
6. Lakhani KR, Boudreau KJ, Loh PR, Backstrom L, Baldwin C, Lonstein E, et al. Prize-based contests can provide solutions to computational biology problems. Nature biotechnology. 2013;31(2):108. doi: 10.1038/nbt.2495 23392504
7. Abdollahi N, Albani A, Anthony E, Baud A, Cardon M, Clerc R, et al. Meet-U: educating through research immersion. PLoS computational biology. 2018;14(3):e1005992. doi: 10.1371/journal.pcbi.1005992 29543809
8. Jeppesen LB, Lakhani KR. Marginality and problem-solving effectiveness in broadcast search. Organization science. 2010;21(5):1016–1033. doi: 10.1287/orsc.1090.0491
9. Mak RH, Endres MG, Paik JH, Sergeev RA, Aerts H, Williams CL, et al. Use of Crowd Innovation to Develop an Artificial Intelligence–Based Solution for Radiation Therapy Targeting. JAMA oncology. 2019;5(5):654–661. doi: 10.1001/jamaoncol.2019.0159 30998808
10. Lees WD, Shepherd AJ. In: Keith JM, editor. Studying Antibody Repertoires with Next-Generation Sequencing. New York, NY: Springer New York; 2017. p. 257–270. Available from: https://doi.org/10.1007/978-1-4939-6613-4_15.
11. Mathonet P, Ullman C. The Application of Next Generation Sequencing to the Understanding of Antibody Repertoires. Frontiers in Immunology. 2013;4:265. doi: 10.3389/fimmu.2013.00265 24062737
12. Burton DR, Mascola JR. Antibody responses to envelope glycoproteins in HIV-1 infection. Nat Immunol. 2015;16(6):571–576. doi: 10.1038/ni.3158 25988889
13. Lamb J, et al. The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science. 2006;313(5795):1929–1935. doi: 10.1126/science.1132939 17008526
14. Subramanian A, et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell. 2018;171(6):1437–1452.e17. doi: 10.1016/j.cell.2017.10.049
15. Qu XA, Rajpal DK. Applications of Connectivity Map in drug discovery and development. Drug Discovery Today. 2012;17(23-24):1289–1298. doi: 10.1016/j.drudis.2012.07.017 22889966
16. Hastie T, Tibshirani R, Sherlock G, Eisen M, Brown P, Botstein D. Imputing Missing Data for Gene Expression Arrays; 1999.
17. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics (Oxford, England). 2012;28(6):882–883. doi: 10.1093/bioinformatics/bts034
18. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. 2005;102(43):15545–15550. doi: 10.1073/pnas.0506580102
19. Raman SK, Pentkovski V, Keshava J. Implementing streaming SIMD extensions on the Pentium III processor. IEEE Micro. 2000;20(4):47–57. doi: 10.1109/40.865866
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