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00:00:00
yeah
00:00:05
yes
00:00:11
i i i i i i
00:00:22
oh
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oh
00:00:32
my answer is i hope yes i think yes we are
00:00:39
in my case i'm i'm dealing with another kind of signal
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uh uh we are working in a project dealing with a genome eek sequences
00:00:49
finally that's these are signals uh uh reconvene for four levels is like uh
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but uh
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two beats to with levels uh signals and
00:01:05
what we were doing was more the same which are the filter off
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that are more important for a diagnosing somebody
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with a that kind of disease and these filters
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if you well close to the sequence some of these filters are connected directly to the
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to the sequence and they are reacting to part of the sequence so if you can
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that would be the next step the first step was was for for that the cases that were active making the filter
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we try to do a some kind of alignment and either defined which are the letters
00:01:48
of uh of the sick with that are closer
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to that and uh with the bid biological databases
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you defined that belongs to that kind of propane and these are active because uh this is uh
00:02:02
'kay propane in a in a year and a buyers and because
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of that that's the way we are extracting some explanation which are
00:02:13
i think and that's something that we plan to do as soon as we have uh we'll have a student or a system
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the able to that why not use the same use below
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the net the eh the adversarial network in that case to generate
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this sequence that will maximise that filter that filters that
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we really know it's very important for classifying for example disease
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then
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that filter we activate will maximise that and that the filter would be the compost
00:02:47
or don't stick with the maximise that we become balls of some kind of a letters
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and then we can look for that matter which are the real war proteins other the more similar
00:02:59
for us that's a signal that would be a similar to twist the part of speech for example that
00:03:05
could back see my some of the filter that will detect but is not my domain so i'm not that
00:03:13
i presented here images that are are
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common images because they are easier for all of us we didn't divide buttons and things
00:03:23
uh if you are speaking about um by the logical images
00:03:28
uh and not everyone is able to read them so because of that
00:03:32
we need really actually an expert to annotate and we cannot be so abstract
00:03:39
because we have a no says uh uh don't know says uh everywhere in any match and we are happy with that
00:03:46
uh it's very hard to say okay we have filters with the cotton ones but perhaps
00:03:53
they are if they're too much they correspond to another phenomena then that's is not as
00:04:00
straight forward to say okay the same thing we we we did with these images we are going to do it with the
00:04:05
read only the the legally matches on the will go i is going to have to work very certainly we would need to
00:04:12
so far there and be much much more careful
00:04:17
i hope that you consider to
00:04:21
uh_huh
00:04:26
if not the intent to be here for the steel for uh the whole the whole morning

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