Fergus Lab at NYU. I believe he's across the hall from Yann LaCunn as well ;)
Still a long way from a Theory of Biogenesis. But a good next step is using a differentiable model to predict novel proteins which have no analogue in Nature. Much like Materials Genome researchers searching for stable phases of matter!
"Training ever bigger convnets and LSTMs on ever bigger datasets gets us closer to Strong AI -- in the same sense that building taller towers gets us closer to the moon." --François Chollet
This is cool, but would be significantly cooler if they did some kind of biological follow up. Perhaps getting their model to output an "ideal" sequence for a desired enzymatic function and then swapping that domain into an existing protein lacking the new function.
This blog post seems to anticipate this happening: https://moalquraishi.wordpress.com/2019/04/01/the-future-of-...
> It does a surprisingly good job of predicting protein function across a diverse set of tasks, including ones structural in nature, like the induction of a single neuron that is able, with some degree of accuracy (ρ = 0.33) to distinguish between α helices and β strands (I suspect the network as a whole is far more performant at this task than the single neuron we’ve identified, but we didn’t push this aspect of the…
And predicting protein function is not that hard either. The ground truth labels are often determined by sequence alignment similarity, not by experiment. So the results are far from profound
Like some of the other ML/AI posts that made it to the top page today, this research too does not give any clear way to reproduce the results. I looked through the pre-print page as well as the full manuscript itself. Without reproducibility and transparency in the code and data, the impact of this research is ultimately limited. No one else can recreate, iterate, and refine the results, nor can anyone rigorously eva…
>any clear way to reproduce the results.
Given that it's evolved I'd imagine this is a given? Or more accurately you could probably duplicate some kind of emergent behaviour but it would be different given different randomized parameters
This is cool, but would be significantly cooler if they did some kind of biological follow up. Perhaps getting their model to output an "ideal" sequence for a desired enzymatic function and then swapping that domain into an existing protein lacking the new function.
Bingo. That would be really interesting. And useful.
There are probably already enzymes in this data set that have measurements of their behavior. Could this modelling approach be coaxed to find the one with the highest processivity? Or do we need more labeled data?
Like some of the other ML/AI posts that made it to the top page today, this research too does not give any clear way to reproduce the results. I looked through the pre-print page as well as the full manuscript itself. Without reproducibility and transparency in the code and data, the impact of this research is ultimately limited. No one else can recreate, iterate, and refine the results, nor can anyone rigorously eva…
>any clear way to reproduce the results. Given that it's evolved I'd imagine this is a given? Or more accurately you could probably duplicate some kind of emergent behaviour but it would be different given different randomized parameters
Usually you use an RNG for which you can publish the seed. So, although it’s random, you can reproduce the results.
Like some of the other ML/AI posts that made it to the top page today, this research too does not give any clear way to reproduce the results. I looked through the pre-print page as well as the full manuscript itself. Without reproducibility and transparency in the code and data, the impact of this research is ultimately limited. No one else can recreate, iterate, and refine the results, nor can anyone rigorously eva…
>any clear way to reproduce the results. Given that it's evolved I'd imagine this is a given? Or more accurately you could probably duplicate some kind of emergent behaviour but it would be different given different randomized parameters
Is it not possible to use the same seed and random number generator to reproduce the results accurately?
Like some of the other ML/AI posts that made it to the top page today, this research too does not give any clear way to reproduce the results. I looked through the pre-print page as well as the full manuscript itself. Without reproducibility and transparency in the code and data, the impact of this research is ultimately limited. No one else can recreate, iterate, and refine the results, nor can anyone rigorously eva…
>any clear way to reproduce the results. Given that it's evolved I'd imagine this is a given? Or more accurately you could probably duplicate some kind of emergent behaviour but it would be different given different randomized parameters
More of what the point is I think is that they don't go into any meta-analysis of big changes that were seen in many of the trials. They don't try to isolate specific mechanisms that formed in a majority of trials that almost made it to this stage for example. They just don't really go into any analysis of the failure trees in trial dataset at all.
IMHO this is probably just a case of them trying to stretch this out across a bunch of different papers, and this is just the announce paper. Which is a shitty practice, but the current academic environment encourages taking good findings and puffing them up into multiple incomplete papers rather than one well-done paper.
This is cool, but would be significantly cooler if they did some kind of biological follow up. Perhaps getting their model to output an "ideal" sequence for a desired enzymatic function and then swapping that domain into an existing protein lacking the new function.
Bingo. That would be really interesting. And useful. There are probably already enzymes in this data set that have measurements of their behavior. Could this modelling approach be coaxed to find the one with the highest processivity? Or do we need more labeled data?
I'm sure they have a bunch of enzymes in their dataset for which kinetic measurements have been published. Another interesting follow up study would attempting to improve kinetic behavior. They could, for instance, analyze some of the catalytically perfect enzymes out there (TIM, SOD, catalase, etc) and see if the model could project improvements onto existing orthogonal protein classes.
Fergus Lab at NYU. I believe he's across the hall from Yann LaCunn as well ;) Still a long way from a Theory of Biogenesis. But a good next step is using a differentiable model to predict novel proteins which have no analogue in Nature. Much like Materials Genome researchers searching for stable phases of matter! "Training ever bigger convnets and LSTMs on ever bigger datasets gets us closer to Strong AI -- in the sa…
> I believe he's across the hall from Yann LaCunn as well ;)
I'm having a hard time processing what the wink might possibly mean in this context.