The principle of uncertainty in biology: Will machine learning/artificial intelligence lead to the end of mechanistic studies?
PLoS Biology, ISSN: 1545-7885, Vol: 22, Issue: 2, Page: e3002495
2024
- 32Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures32
- Readers32
- 32
Article Description
Molecular Biology has long tried to discover mechanisms, considering that unless we understand the principles, we cannot develop applications. Now machine learning and artificial intelligence enable direct leaps to application without understanding the principles. Will this herald a decline in mechanistic studies?
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85184586826&origin=inward; http://dx.doi.org/10.1371/journal.pbio.3002495; http://www.ncbi.nlm.nih.gov/pubmed/38329935; https://dx.plos.org/10.1371/journal.pbio.3002495; https://dx.doi.org/10.1371/journal.pbio.3002495; https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002495
Public Library of Science (PLoS)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know