A task performed by Bioinformatics Scientists
Analyze large molecular datasets, such as raw microarray data, genomic sequence data, or proteomics data, for clinical or basic research purposes.
Technically, much of it, experts rate AI capability 3.5/5, but workers don't want it automated (desire 2.83/5). It's an Automation Red Light: proceed with care.
Do you want this task automated?
How much the people who do this task want AI to take it over (avg of 6 workers).
How capable experts rate current AI at this task (avg of 2 experts).
How much human involvement should stay?
The Human Agency Scale (H1–H5) captures how much human direction workers think this task should keep.
Why workers feel the way they do
- Frees up time for higher-value work50%
- Helps handle larger scale50%
- It's repetitive17%
- It's stressful17%
- Reduces human error17%
- It's difficult17%
How to automate this
Doable with technology, but keep a person clearly in charge.
Analyzing & processing data
Let the spreadsheet or a BI tool do the math and the charts.
- 1Set up formulas and pivot tables once, so the numbers you need to analyze large molecular datasets, such as raw microarray data, genomic sequence data, or proteomics data, recalculate themselves.
- 2Connect a free dashboard so reports refresh themselves (Looker Studio, Power BI).
- 3Use AI to summarise the findings, but keep the numbers in the spreadsheet.
Tools that can help
Know a better way, or spot an error?
Share how you automate this in real life, or correct the data. Reviewed before publishing.
This is 1 of 7 tasks studied for Bioinformatics Scientists, where workers want 71% of tasks automated.
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