4 ms·

Why don't you train a machine to analyze the tail deflection. Then do all the fish gene knock-out experiments?

by niels_olson 10y ago

irq11 10y ago
because the experiment is orders of magnitude harder and more expensive than the data analysis.
- niels_olson 10y ago
  In the ML world, yes. But the biologists know how to do the experiments, most of which is raising fish and looking at them under a confocal microscope. Give them an effectively infinite supply of confocal microscopists and they could raise a lot of fish.
Balgair 10y ago
I touch on this in another comment, but mostly because bio people do not understand math and therefore programming. Machine learning is light-years beyond them as a result. I'll give an example: I was at an anesthesiology conference. Dr. Emery Brown is at Harvard and Mass. Gen. and has been triply elected to the Nat'l Academy in Eng., Medicine, and Biology (only 19 other people hold that record). Suffice to say, the guy is Smart with a capital S. He was talking about his new auto-anesthesia machine that records EEG on the head and them modulates the dosage of the anesthesia drugs to maintain or change the depth of anesthesia. It keeps you 'knocked out' better than any human can and will bring you 'back up' probably a lot better too (more testing is needed). Very basically, when knocked out for surgery, your entire brain rhythmically fires at ~11Hz. As you wake up, that rhythm deceases and goes away, the deeper you are, then your brain increases that rhythm. You measure that with the EEGs and filter out all the rest. So, to keep you knocked out, you increase the dosage when you see the rhythm slowing and decrease when you see the rhythm speeding up. Anyone that has taken differential equations and the barest EE knows that the way to control that is with an Op-Amp circuit (https://upload.wikimedia.org/wikipedia/commons/f/fb/Voltage_stabiliser_OA%2C_IEC_symbols.svg https://upload.wikimedia.org/wikipedia/commons/f/fb/Voltage_...). It is incredibly straightforward if you were even barely awake in those classes. You don't need something as complicated as Machine Learning to do it, you just use feedback. Now, when the Q&A session started with Dr. Brown, it was a mad-house. The anesthesiologists and neuroscientists were just dumbfounded that a simple circuit like that could control the machine with total clarity and reliability. No explaining by a guy with that level of gravitas and a credential list longer than your arm could convince them that it would work. The phrase they kept coming up with was 'I'm not a math person'. Ok, got it? These folks are brilliant in bio and neuro and surgery. Just flabbergastingly good. They can feel how much drug is needed for a baby that just got their arm ripped off in a car accident. But they will never understand the math and they do not trust it at all as a consequence. So trying to say that Machine Learning is the pancea to this flood of data is like saying to Eskimos that to heat their houses they need to simply invent a nuclear reactor. It's never going to happen.
- searine 10y ago
  >I touch on this in another comment, but mostly because bio people do not understand math and therefore programming. This is extremely condescending. I know many brilliant biologists who are also great mathematicians, statisticians, and programmers. Anesthesiology isn't really a bio field at all. It's a medical field. A field that doesn't do a lot of cutting edge research. What you describe is engineering, and is likely headed by competent engineers, and funded by said Anesthesiologist. >But they will never understand the math and they do not trust it at all as a consequence. Again. Extremely condescending. The people who researched, designed, and built the machine you describe are likely extremely competent in math. Yet, you are describing the end-user, and from that inferring the designer of the machine to be of the same expertise as the user. Check your assumptions.
  - niels_olson 10y ago
    Balgair was responding to me. I'm a pathologist with an undergrad in physics. Balgair's assumptions are well founded. It's like trying to convince horsemen to buy this new carriage an otto cycle engine. Maybe one of those horsemen will have an engineering degree. But it will be decades before all the horses are off the road.

Why don't you train a machine to analyze the tail deflection. Then do all the fish gene knock-out experiments?

irq11 10y ago

because the experiment is orders of magnitude harder and more expensive than the data analysis.

niels_olson 10y ago
In the ML world, yes. But the biologists know how to do the experiments, most of which is raising fish and looking at them under a confocal microscope. Give them an effectively infinite supply of confocal microscopists and they could raise a lot of fish.

Balgair 10y ago

I touch on this in another comment, but mostly because bio people do not understand math and therefore programming. Machine learning is light-years beyond them as a result. I'll give an example: I was at an anesthesiology conference. Dr. Emery Brown is at Harvard and Mass. Gen. and has been triply elected to the Nat'l Academy in Eng., Medicine, and Biology (only 19 other people hold that record). Suffice to say, the guy is Smart with a capital S. He was talking about his new auto-anesthesia machine that records EEG on the head and them modulates the dosage of the anesthesia drugs to maintain or change the depth of anesthesia. It keeps you 'knocked out' better than any human can and will bring you 'back up' probably a lot better too (more testing is needed). Very basically, when knocked out for surgery, your entire brain rhythmically fires at ~11Hz. As you wake up, that rhythm deceases and goes away, the deeper you are, then your brain increases that rhythm. You measure that with the EEGs and filter out all the rest. So, to keep you knocked out, you increase the dosage when you see the rhythm slowing and decrease when you see the rhythm speeding up. Anyone that has taken differential equations and the barest EE knows that the way to control that is with an Op-Amp circuit (https://upload.wikimedia.org/wikipedia/commons/f/fb/Voltage_stabiliser_OA%2C_IEC_symbols.svg https://upload.wikimedia.org/wikipedia/commons/f/fb/Voltage_...). It is incredibly straightforward if you were even barely awake in those classes. You don't need something as complicated as Machine Learning to do it, you just use feedback. Now, when the Q&A session started with Dr. Brown, it was a mad-house. The anesthesiologists and neuroscientists were just dumbfounded that a simple circuit like that could control the machine with total clarity and reliability. No explaining by a guy with that level of gravitas and a credential list longer than your arm could convince them that it would work. The phrase they kept coming up with was 'I'm not a math person'. Ok, got it? These folks are brilliant in bio and neuro and surgery. Just flabbergastingly good. They can feel how much drug is needed for a baby that just got their arm ripped off in a car accident. But they will never understand the math and they do not trust it at all as a consequence. So trying to say that Machine Learning is the pancea to this flood of data is like saying to Eskimos that to heat their houses they need to simply invent a nuclear reactor. It's never going to happen.

searine 10y ago
>I touch on this in another comment, but mostly because bio people do not understand math and therefore programming. This is extremely condescending. I know many brilliant biologists who are also great mathematicians, statisticians, and programmers. Anesthesiology isn't really a bio field at all. It's a medical field. A field that doesn't do a lot of cutting edge research. What you describe is engineering, and is likely headed by competent engineers, and funded by said Anesthesiologist. >But they will never understand the math and they do not trust it at all as a consequence. Again. Extremely condescending. The people who researched, designed, and built the machine you describe are likely extremely competent in math. Yet, you are describing the end-user, and from that inferring the designer of the machine to be of the same expertise as the user. Check your assumptions.
- niels_olson 10y ago
  Balgair was responding to me. I'm a pathologist with an undergrad in physics. Balgair's assumptions are well founded. It's like trying to convince horsemen to buy this new carriage an otto cycle engine. Maybe one of those horsemen will have an engineering degree. But it will be decades before all the horses are off the road.