3 ms·
>"Well, your arguments seem equally non-sequiter to me." Fair enough. >"I spend a lot of time in the world of correlations, in biology." I left that field. O
by nonbel 10y ago
>"Well, your arguments seem equally non-sequiter to me."
Fair enough.
>"I spend a lot of time in the world of correlations, in biology."
I left that field. One reason was because I realized so much of my time was wasted after I attempted to attach numbers to things like "# of receptors required to fit the narrative".
Sure "A causes B", but it must be for some reason totally different from what is usually proposed (and could probably be considered a boring experimental artifact). I just don't care if "A causes B". I want to know for "f(A) ~ B", what is f?
>"It's much better to get a causation: a knob that when you turn it you get a response, rather than no response."
Reminds me of this paper:
"Biologists summarize their
results with the help of all-too-well recognizable diagrams,
in which a favorite protein is placed in the middle
and connected to everything else with two-way arrows.
Even if a diagram makes overall sense (Fig. 3a), it is usually
useless for a quantitative analysis, which limits its
predictive or investigative value to a very narrow range.
The language used by biologists for verbal communications
is not better and is not unlike that used by stock
market analysts. Both are vague (e.g., a balance between
pro- and anti-apoptotic bcl-2 proteins appears to control
the cell viability, and seems to correlate in long-term with
the ability to form tumors) and avoid clear predictions."
https://bml.bioe.uic.edu/BML/Stuff/Stuff_files/biologist%20fix%20radio.pdf https://bml.bioe.uic.edu/BML/Stuff/Stuff_files/biologist%20f...
- epistasis 10y agoI think that you would really really like modern causality research if you bothered to look into it. It's all about f(A) ~ B, but recognizes that there are different ways of varying B. Do you disconnect B from the rest of its influences when you vary it, or are you letting them vary. If you try to model f(A) ~ B in biological systems without simultaneously modeling causality, then it's going to go wrong. This happened to me in year 2 of grad school. The bayesian network built around data without modeling the causality was wrong, the one that took into account the causality from the knockout experiments was right.