AI Innovates Analysis of Complex Phenotypic Screening Data

Hi, I'm Oren Kraus, co-founder of Phenomic AI. We're a phenotypic drug discovery company based in Toronto, and we're using AI to analyze all the data that we're generating in our own lab experiments. The benefits of AI are huge across the industry. So lots of data is being generated from lots of different types of experiments including imaging experiments, sequencing experiments, and it's really too much data for any method to really work well.
So, AI is going to enable us to integrate all these different data sets, interpret them a lot more easily. Specifically for imaging experiments, we can learn patterns that aren't really noticeable by other techniques, so we can really find novel effects of compounds, novel effects of biologics, and really discover breakthrough medicines that way.
At the upcoming SLAS Conference focused on AI in process automation, we're going to be describing the platform we built for analyzing images from high-content screens, and this goes over the whole workflow from storing all the images, managing them, building a reproducible method for analyzing these screens with AI approaches specifically deep-learning and being able to communicate the results really effectively to scientists.
Our technology is really designed for allowing scientists to interact directly with their complex imaging data. So what they can do is, you know, once they're done the screen, they get the results within a few hours and they can right away interact with all the different clusters that show up in the phenotypic data and they could even see what phenotypes are there by looking directly at the images that pop up.
And it really cuts out a lot of communication that needs to happen between different teams because the scientists can really work directly with our platform and get the results they care about. So, attendees should come to the talk at the upcoming SLAS meeting because I'll go over the platform we built for analyzing high-content screening data, which covers everything from data management and also allowing users to apply deep-learning models to lots of different screens and also what those deep-learning models can actually do for different screening use cases.
And I'll introduce the product we're launching with PerkinElmer to really make that platform more broadly available to the whole industry.