just saw this; I'm part of the computational modeling team that worked on this -- can try to field any questions or find more qualified people to do so.
1. Would you advertise this tool as a visualization to help with future research and understanding cells OR a possible diagnostic aid?
2. Is there any project that aims to apply these tools to find changes in cells of an aging organism? Do you think that would be useful?
3. Is it possible to figure out for any given class of cell how much of its volume is understood? e.g. "there's this little part and we have no idea what's going on there" or "this protein is everywhere and we can't figure out what it does".
4. How can you evaluate the correctness of your probabilistic model? Neural nets and auto-encoders are known to produce bad results. as an exaggeration, you wouldn't want to have this as your model of human face: https://zo7.github.io/img/2016-09-25-generating-faces/random...
And thanks for publishing the source code for training!
Characterizing cellular variation is exactly what we're interested in, e.g when and why are the mitts clustered around the nuclear vs not. Lots of images in our data have them packed around the nucleus -- you can look at the localizations from our microscopes here (select the Tom20 tag): http://www.allencell.org/3d-cell-viewer.html here. You can also grab the raw data (including bright field images e.g. what you would "see" in the microscope) here http://www.allencell.org/data-downloading.html#DownloadImage...
[+] [-] donovanr|7 years ago|reply
[+] [-] m1el|7 years ago|reply
1. Would you advertise this tool as a visualization to help with future research and understanding cells OR a possible diagnostic aid?
2. Is there any project that aims to apply these tools to find changes in cells of an aging organism? Do you think that would be useful?
3. Is it possible to figure out for any given class of cell how much of its volume is understood? e.g. "there's this little part and we have no idea what's going on there" or "this protein is everywhere and we can't figure out what it does".
4. How can you evaluate the correctness of your probabilistic model? Neural nets and auto-encoders are known to produce bad results. as an exaggeration, you wouldn't want to have this as your model of human face: https://zo7.github.io/img/2016-09-25-generating-faces/random...
And thanks for publishing the source code for training!
[+] [-] scentoni|7 years ago|reply
[+] [-] blurbleblurble|7 years ago|reply
[+] [-] frozenport|7 years ago|reply
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[+] [-] bawana|7 years ago|reply
[+] [-] donovanr|7 years ago|reply