Predicting translational progress in biomedical research

PLoS Biology

Volumn 17, Issue 10, 2019, Pages

  Ian Hutchins, B ^a   Davis, Matthew T ^a   Meseroll, Rebecca A ^a   Santangelo, M ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOMEDICINE; CLINICAL RESEARCH; CORRELATIONAL STUDY; DATA PROCESSING; KNOWLEDGE; MACHINE LEARNING; MEASUREMENT ACCURACY; PRACTICE GUIDELINE; PREDICTION; PUBLICATION; SCORING SYSTEM; TRANSLATIONAL RESEARCH;

EID: 85073113299     PISSN: 15449173     EISSN: 15457885     Source Type: Journal    
DOI: 10.1371/journal.pbio.3000416     Document Type: Article

References (45)

1. Contopoulos-Ioannidis DG, Alexiou GA, Gouvias TC, Ioannidis JP. Medicine. Life cycle of translational research for medical interventions. Science. 2008; 321(5894):1298–9. https://doi.org/10.1126/science. 1160622 PMID: 18772421
2. Spector JM, Harrison RS, Fishman MC. Fundamental science behind today’s important medicines. Science Translational Medicine. 2018; 10(438).
3. Hanney SR, Castle-Clarke S, Grant J, Guthrie S, Henshall C, Mestre-Ferrandiz J, et al. How long does biomedical research take? Studying the time taken between biomedical and health research and its translation into products, policy, and practice. Health Research Policy and Systems. https://doi.org/10. 1186/1478-4505-13-1 PMID: 25552353
4. Weber GM. Identifying translational science within the triangle of biomedicine. Journal of Translational Medicine. 2013; 11:126. https://doi.org/10.1186/1479-5876-11-126 PMID: 23705970
5. Bush V. Science the endless frontier. Washington: United States Government Printing Office, 1945. 184 p. Science Education. 1945; 29(4):218-.
6. Galkina Cleary E, Beierlein JM, Khanuja NS, McNamee LM, Ledley FD. Contribution of NIH funding to new drug approvals 2010–2016. Proceedings of the National Academy of Sciences of the United States of America. 2018; 115(10):2329–34. https://doi.org/10.1073/pnas.1715368115 PMID: 29440428
7. McNamee LM, Walsh MJ, Ledley FD. Timelines of translational science: From technology initiation to FDA approval. PLoS ONE. 2017; 12(5):e0177371. https://doi.org/10.1371/journal.pone.0177371 PMID: 28481922
8. Beierlein JM, McNamee LM, Walsh MJ, Kaitin KI, DiMasi JA, Ledley FD. Landscape of Innovation for Cardiovascular Pharmaceuticals: From Basic Science to New Molecular Entities. Clinical therapeutics. 2017; 39(7):1409-25.e20. https://doi.org/10.1016/j.clinthera.2017.06.001 PMID: 28652015.
9. Beierlein JM, McNamee LM, Walsh MJ, Ledley FD. Patterns of Innovation in Alzheimer’s Disease Drug Development: A Strategic Assessment Based on Technological Maturity. Clinical Therapeutics. 2015; 37(8):1643-51.e3. https://doi.org/10.1016/j.clinthera.2015.07.003 PMID: 26243074
10. McNamee LM, Ledley FD. Modeling timelines for translational science in cancer; the impact of technological maturation. PLoS ONE. 2017; 12(3):e0174538. https://doi.org/10.1371/journal.pone.0174538 PMID: 28346525
11. Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, et al. The support of human genetic evidence for approved drug indications. Nature Genetics. 2015; 47:856. https://doi.org/10.1038/ng.3314 PMID: 26121088
12. Colvis CM, Austin CP. Innovation in therapeutics development at the NCATS. Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology. 2014; 39(1):230–2. https://doi.org/10.1038/npp.2013.247 PMID: 24317308
13. Searles A, Doran C, Attia J, Knight D, Wiggers J, Deeming S, et al. An approach to measuring and encouraging research translation and research impact. Health research policy and systems. 2016; 14 (1):60. https://doi.org/10.1186/s12961-016-0131-2 PMID: 27507300
14. Williams RS, Lotia S, Holloway AK, Pico AR. From scientific discovery to cures: bright stars within a galaxy. Cell. 2015; 163(1):21–3. https://doi.org/10.1016/j.cell.2015.09.007 PMID: 26406364
15. Ke Q. Identifying translational science through embeddings of controlled vocabularies. J Am Med Inform Assoc. 2019; 26(6):516–23. https://doi.org/10.1093/jamia/ocy177 PMID: 30830170
16. Surkis A, Hogle JA, DiazGranados D, Hunt JD, Mazmanian PE, Connors E, et al. Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approach. J Transl Med. 2016; 14(1):235. https://doi.org/10.1186/s12967-016-0992-8 PMID: 27492440
17. Wang D, Song C, Barabasi AL. Quantifying long-term scientific impact. Science. 2013; 342(6154):127–32. https://doi.org/10.1126/science.1237825 PMID: 24092745
18. Hutchins BI, Yuan X, Anderson JM, Santangelo GM. Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level. PLoS Biol. 2016; 14(9):e1002541. https://doi.org/10.1371/journal.pbio.1002541 PMID: 27599104
19. Trochim W, Kane C, Graham MJ, Pincus HA. Evaluating translational research: a process marker model. Clinical and translational science. 2011; 4(3):153–62. https://doi.org/10.1111/j.1752-8062.2011.00291.x PMID: 21707944
20. National Library of Medicine. Medical Subject Headings. https://www.nlm.nih.gov/mesh/.
21. Breiman L. Random Forests. Machine Learning. 2001; 45(1):5–32.
22. Powers DMW. Evaluation: From precision, recall and f-measure to roc., informedness, markedness & correlation. Journal of Machine Learning Technologies. 2011; 2(1):37–63.
23. Liaw A, Wiener M. Classification and Regression by randomForest. R News. 2002; 2(3):18–22.
24. Ioannidis JP. Measuring co-authorship and networking-adjusted scientific impact. PLoS ONE. 2008; 3 (7):e2778. https://doi.org/10.1371/journal.pone.0002778 PMID: 18648663
25. Morris ZS, Wooding S, Grant J. The answer is 17 years, what is the question: understanding time lags in translational research. Journal of the Royal Society of Medicine. 2011; 104(12):510–20. https://doi.org/10.1258/jrsm.2011.110180 PMID: 22179294
26. Ioannidis JP, Khoury MJ. Assessing value in biomedical research: the PQRST of appraisal and reward. JAMA. 2014; 312(5):483–4. https://doi.org/10.1001/jama.2014.6932 PMID: 24911291
27. Santangelo GM. Article-level assessment of influence and translation in biomedical research. Molecular biology of the cell. 2017; 28(11):1401–8. https://doi.org/10.1091/mbc.E16-01-0037 PMID: 28559438
28. Bertuzzi S, Drubin DG. No shortcuts for research assessment. Mol Biol Cell. 2013; 24(10):1505–6. https://doi.org/10.1091/mbc.E13-04-0193 PMID: 23686606
29. Hartshorne JK, Schachner A. Tracking replicability as a method of post-publication open evaluation. Front Comput Neurosci. 2012; 6:8. https://doi.org/10.3389/fncom.2012.00008 PMID: 22403538
30. Kilicoglu H. Biomedical text mining for research rigor and integrity: tasks, challenges, directions. Brief Bioinform. 2017. https://doi.org/10.1093/bib/bbx057 PMID: 28633401
31. Olfson M, Wall MM, Blanco C. Incentivizing Data Sharing and Collaboration in Medical Research-The S-Index. JAMA Psychiatry. 2017; 74(1):5–6. https://doi.org/10.1001/jamapsychiatry.2016.2610 PMID: 27784040
32. Grabitz P, Lazebnik Y, Nicholson J, Rife S. Science with no fiction: measuring the veracity of scientific reports by citation analysis. bioRxiv. 2017. https://doi.org/10.1101/172940
33. Archer SN, Schmidt C, Vandewalle G, Dijk D-J. Phenotyping of PER3 variants reveals widespread effects on circadian preference, sleep regulation, and health. Sleep Medicine Reviews. 2017.
34. National Library of Medicine, MeSH Tree View. https://meshb.nlm.nih.gov/treeView.
35. Loh W-Y. Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2011; 1(1):14–23.
36. National Library of Medicine Introduction to MeSH. https://www.nlm.nih.gov/mesh/introduction.html. [cited 2018 Aug 8].
37. National Library of Medicine. PubMed/Medline Data Download. https://www.nlm.nih.gov/databases/download/pubmed_medline.html. [cited 2018 Aug 8].
38. Office of Portfolio Analysis, National Institutes of Health. iCite. https://icite.od.nih.gov. [cited 2018 Aug 8].
39. Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995; 20(3):273–97.
40. Pearl R, Reed LJ. On the Rate of Growth of the Population of the United States Since 1790 and its Mathematical Representation. Proceedings of the National Academy of Sciences of the United States of America. 1920; 6(6):275–88. https://doi.org/10.1073/pnas.6.6.275 PMID: 16576496
41. K. Nigam JL, and A. McCallum. Using maximum entropy for text classification. IJCAI-99 Workshop on Machine Learning for Information Filtering. 1999:61–7.
42. Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J. LIBLINEAR: A Library for Large Linear Classification. J Mach Learn Res. 2008; 9:1871–4.
43. Deeplearning4j Development Team. Building Neural Networks with DeepLearning4J. https://deeplearning4j.org/docs/latest/deeplearning4j-nn-multilayernetwork. [cited 2018 Aug 8].
44. Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural computation 2006; 18:1527–54. https://doi.org/10.1162/neco.2006.18.7.1527 PMID: 16764513
45. Hutchins BI, Baker K, Davis M, et al. The NIH Open Citation Collection: A public access, broad coverage resource. PLoS Biol. 2019. https://doi.org/10.1371/journal.pbio.3000385