Comment by Balgair | Hacker News Reader

Comment by Balgair | Hacker News Reader

One point: Most Bio type people have not had multi-variable calculus, and many have not had calculus at all. So, it's not really that they can't process it fast enough, it's that their techniques for processing it are stereotypical. They figured out how to do that T-Test (or something else) once, and they stick with it, because they really don't know he math behind it.

Also, though there is a TON of 'data' coming in, most of it is not useful. For example, I have a 500Gb file of a stack of .tiff images per fish that I have imaged in a confocal microscope. I have a GFP filter on the scope and therefore only get the green part of the .tiff files exposed, the red and blue are just background noise. Also, most of the image is the dish I have the fishes in. I tickle the fish, they flick their tails, and I see this all in 120fps. Now, I measure how much of an angle the fish made their tails flick, all in 3-D, because that's what the scope records in. I have a half TB per fish to comb through, and I have ~20 fishes, say ~10TB. At the end, I get a single graph comparing the fish with some gene to those without it, and I have 10TB of 'data' left over. Yeah, someone could comb through it all and find something else to look at. But i forgot to record the precise temperatures, the orientation of the fish, the fish that I knew later died, etc. I had that all in my head. And, hey, what do you know?, the p-value is ~.45 and therefore there is no 'real' difference in the fish and we can't include this in a paper. Now all that 'data' is being kept on a drive on some computer somewhere and is counted towards the budget that the lab has on the shared spaces. It's not really 'data' anymore, in that it is useful to advancing knowledge for anyone (it counts as practice I guess), but it still clogs up space.

It seems that you may have been able to extract specific information about things like "how much of an angle the fish made their tails flick" as opposed to storing the raw files. Such a technique would've definitely eliminated extra irrelevant information like "the dish [you] have the fishes in" as well as cut back on the size of the data.

True! That info is what the paper you are trying to write is all about. You take these TBs of data, and effectively compress it into a single graph of maybe 5kB, the thing you really care about.

However, you still have to keep all the data around for possible re-analysis depending on the terms of the paper you submitted to, for up to 10 years or more in some cases, along with all the equipment and maybe some frozen fish samples in LN2 down in the basement. Now, not all labs are so well funded, and sometimes accidents happen and grad students are not always so informed about these rules, but you should be keeping it all ready for re-testing for some number of years. You simply cannot get rid of it all for the sake of science.

If you really want to help out bio-peepz, then helping them program is a great way to do so. Spaghetti does not even come close to describing their 'code'. If anyone really wanted to reproduce the experiment, then they would have to wade into the fetid swamp of 'code' that produced it. Most bio people cannot even begin to tell you how to code or what it even means. They can PCR and Western Blot better than Jesus himself, but code? No way. It is a real hindrance to the sciences, actually. The helping hand that code was meant to be has become a ball-n-chain that drags peoples minds away from them and 'lets the computer do it' for them.

Wow, I failed to considered the need for validation.. but even still, that adds only another layer of testing to ensure that your techniques for converting the raw data to structured data are sound enough to be reproduced.

I'd guess the model still wouldn't be considered "pure" enough by the scientific community unless it can be proven to be free from bias samples of raw information.

> but even still, that adds only another layer of testing to ensure that your techniques for converting the raw data to structured data are sound enough to be reproduced.

That's actually not correct. What if your validation method is broken? What if that unit test you wrote has a bug in the test itself?

In science you have to keep the raw data available.

This is not only for reproduction of your particular structured metric (i.e. tail flick angle), but for other groups to design novel metrics that may add to or trivialize the published result.

>In science you have to keep the raw data available.

There is nothing I'm aware of saying that you can't losslessly convert tiff to png or even tiff to tiff.gz. Nobody needs to collect pcap files for their sensors (the raw data). They just collect the data and stick it in an appropriate file.

Most bio-peeps and medical personnel have an understanding that there is a difference between .png, .jpg, and .tif. But the diff of .tif and .tiff? No idea, let alone the +/- of a .png to a .jpg. It's all just a picture to them.

So, trying to tell them that you can losslessly convert them, though maybe true, is not a good idea. .tiff can handle a stack of images and you only have to call it once in a MatLab script, .jpg cannot and will result in a shitfit as they try to load in an entire folder of images. Then you get into bit-depth and God help you if the conversation ever has you trying to pronounce 'int', 'float', 'str', or 'double'. The class of a variable? Dude, this person could care less and will get out a protractor and measure tail flicks off a print-out before any of that will ever sink in.

I think you underestimate people. If you're an admin doing user support for such a system and someone says they need more storage because of blah blah blah then you offer ways to alter the workflow so they don't need 500G of tiff files. It might even speed up their workflow since it's not streaming 500G off disk.

No need to get into bit depth and int float str and double (though all of the biomedial researchers I've worked with knew these very well). Just: 'you can convert to png and back again without losing data. Just like zipping a file and unzipping it again. Let me show you how...'

Totally on point, regarding multivariate relationships! The problem is not that they do T-test (and other Stat-101-jargon blackbox stuff), but that they stop at it. To many of them, even the existence of multivariate effects is beyond their imagination.

So, inference to many biomedical folks is just 1-dimensional. Big data has a long way to go to penetrate fields where people cannot think in more than 1 dimension!

Eh, look at the journals for the reasoning there. Many editors and reviewers don't have the time to untangle multi-variate effects and make certain the math was right. Maybe that was because they can't do it themselves, but usually if it is that complicated and interconnected and you come out with a 'clear' picture, odds are that it was an error. I'm NOT saying that is in fact true at all. However, most editors get so very many submissions every day that they take the easy to understand papers with clear results and pathways over the harder to understand ones that are more likely to have errors in them as they are more complicated. This feeds backwards into the grad schools and then only simple pathways and mechanisms are encouraged and nurtured, while complex ones are left alone in the publish-or-perish environment. And yes, this is BS, but that is how grants get funded.

The best way to get published is to use a variation on a method that was used many times before. Since the novelty you're focused on is something biological, you stick with the same statistical methods that have gotten published for the last decade.

Unfortunately, there just doesn't seem to be much tenure-juice from innovating in statistical methods for most life-science fields. Not all, of course. Science moves slowly.

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

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

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.

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_...). 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.

>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.

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.

FYI I'm guessing you might be able to compress that data to 5-10% of its size with h265. Feel free to Shoot me an email if you want some pointers.

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