of a protein, even though you know it's a super different application area! But the method in itself, like these lines of code I take from one project to another, they're the same! I like a lot that this this method can be translated. But how can you turn a probabilistic method into something that has a connection to truth and to the real world. Yeah that's a good question, because we're learning with a distribution of known proteins. This is also a bit where I see the limitations if you do purely in silico modeling. We can apply some filters, like computational filters, to check does this fulfill certain criteria. But ultimately, we would have to go to the lab. So there of course we would need the collaboration with wet lab people to synthesize the actual protein. But I think the strength would be of course the use all of these computational methods. And while from a probabilistic method we can generate thousands of samples, we can narrow down the samples to go into testing at the end by using a lot of filters in that pipeline. I guess that's where we also have the strength of our methods. It's not the ultimate truth. I get your question, but we can get a very good set of candidates. That is the hope of course. That's a lovely answer. I love it. Your last words. I guess what I would take away from my past years is if you find something that you enjoy, keep doing it; if you have the opportunity and go for possibilities that come up, even though it might not seem you know very clearly from the beginning, in the end I think you can get to some very cool places! So passion will drive you to cool places. I would say so! 19 DAILY WACV Monday Read 160 FASCINATING interviews with Women in Computer Vision! Read 160 FASCINATING interviews with Women in Computer Vision! Lea Bogensperger
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