 Hello, good morning, good evening, good afternoon, and thank you for joining this Neo4j Live. Hi, Ben. Welcome to you as well. Thank you for joining. Hi. So, yeah, this Neo4j Live is about CROSR, Knowledge Graph Based Drug Discovery. It's what it's called. So I'm very happy to have ben ben vossa here with me today who is the co-founder of of crosser which is a and i guess you will give us a few uh a few in a few moments you give us some overview of what it is so i'll summarize briefly in my words and then you use the better words i guess so it's uh it's uh yeah i think you you found it across a few years ago. So it's a smallish startup out of the UK on drug discovery by using Knowledge Graph to make the process better, to make it quicker for companies to develop new drugs and new medicine, I guess, for all kinds of diseases to help the long-winded process, which usually takes lots and lots of time to make it just a little bit quicker to be fast on the market, which obviously helps companies sell more in the short term, but also obviously helps everybody, you and me, in the short term, but also obviously helps everybody, you and me, if we get, you know, well working or the right medicine for the right treatments, the right treatments for the right applications. And the illnesses faster is obviously a plus for everybody. So I'm looking forward to this, especially because it's a little of a pet peeve project, or not project for me, but interest, I would say. Since COVID, I'm involved in a few things in the life sciences and healthcare industry around with graphs. So particularly, this is a nice project as well. So enough rambling from me. Ben, again, thank you for joining. Yeah, how's it going? Yeah, good. So yeah, thank you for having me on the E4J Live. I've seen a few of these before and I've really enjoyed them. Dr. AI, there's one recently on a chemistry library that was also really good. Yeah so I'll start by maybe talking a little bit through what CROSSA is and kind of the wider landscape of biomedical knowledge graphs. Well, Ben gets the screen sharing ready. This is obviously, again, as usual, is an open invitation. So we try to make this interactive and as lively as possible. So if you are watching this live, then please use the chat, chime in, tell us what you think, tell us your, maybe your experiences, maybe you're working in healthcare life sciences and are in the process of research. Maybe you are in the pharma industry and are in the process of actually, you know, working on a new medicine for some treatment and you're interested in this or you just are an interested in potty, so like myself I guess let us know, let us hear what your thoughts are and keep it interactive. Yeah, please do. I'll start with a quote that so across at the moment two co-founders, me and my co-founder, Jamie. My background is mostly computational. So I have a background in software engineering, data science. I worked at some consumer tech companies like Deliveroo. So if you're in a country that's served by deliver and you know you've ordered takeaway there then you know i've helped you out there um and uh and um yes and my co-founder jamie um the the ceo of crosser um has a background in uh biology and uh kind of the pharmaceutical industry and he recently sent me this quote that I think well sums up how knowledge graphs are incredibly relevant in life sciences and computational biology in general the quote is from Florian Markovets and it's computational biology is providing a reference map for biology an atlas of life that holds together individual insights and for me and Jamie at Crosser this was yeah this this quote and also this paper so I think he did a paper the paper is all biology is computational biology. And that's kind of a thesis that across that we believe to be true. And the idea is that all these little pieces of biology kind of all come together to build up this atlas of life or an atlas of the cell that really describes all of life together. And having that wider context of the entire cell and all these individual insights together can kind of help us move forward with medicines and getting therapeutics faster to the patient. So for us, that was a very powerful quote. And you can imagine that if you're looking at computational approach for building an Atlas of Life, the best way to model that is through a knowledge graph. And yeah, the leading solution for knowledge graphs and kind of graph-based infrastructure is Neo4j. So I'll move on to the next slide. the leading solution for knowledge graphs and kind of graph-based infrastructure is Neo4j. So let me go to the next slide. And when I'm talking about knowledge graphs in computational biology, I'm talking about a biomedical knowledge graph. And the primary kind of node types of these knowledge graphs are gene, disease, and so you know you have a drug that affects a gene um so that will be the drug will up regulate the gene or it will down regulate the gene um and that gene will have an association to the disease uh so you know there'll be some kind of some kind of genetic variation in that gene will have like a relationship to the disease. So by taking a drug and up or down regulating a gene, you're then having hopefully a positive outcome for patients with that disease. And a kind of wider data model for that is on the right. So this is a biomedical knowledge graph by this open source. It's called HeteoNet. It was released in 2017, and it was, in my opinion, like way ahead of their time. So they released a knowledge graph called Helionet that you can, yeah, I recommend anyone that's interested in biomarker knowledge graphs, this is kind of the one to definitely explore because it kind of starts with these core nodes of gene disease and compound, includes some important functional annotations. so on the gene you have which pathways of this gene in molecular function other go processes uh some other node types around compound uh side effect pharmacological class how the compound uh you know treats or palliates the disease and symptoms anatomy and this this by itself represents a pretty good base layer of schema for a knowledge graph in the biomedical domain so yeah i'd absolutely recommend anyone that's interested in biomedical knowledge graphs this is a great place to start because it's at the right level of information versus simplicity to to do some to do some analytics on knowledge graphs and play around with that. And kind of what this database is built out of is you can see here that HeteoNet combines information from 29 public databases. So that's genes that are from Entrez gene, there may be some information here from Ensembl as well. Compounds from drug banks, disease from disease nodes coming from disease ontology, and I'm sure the pathways is another important kind of biological process from wiki pathways and reactome and the kind of pathway interaction database that kind of feeds into this knowledge graph. And this was released in 2017 and is available for, they actually have a public Neo4j instance that if anyone wants to try out Neo4j in a public domain, If anyone wants to try out Neo4j in a public domain, then they can, here I'm just showing an example, but, so if anyone wants to try out a Neo4j graph database in the biomedical domain, then HetioNet has a publicly available database. You can query cipher the domain language for Neo4j. And here I'm gonna just run a really quick query for let's say, show me all the diseases that have a relationship to the gene APOE. And you can see here that there are various diseases that are related to APOE that may be interesting to you. So yeah, if you're interested in just playing around with biomedical knowledge graph in Neo4j, then HetioNet has a public instance that's definitely a good place to start. Yeah, that's a good point. That's interesting to know for people. I think there's very, I don't know, because you said 2017, do you think this is, are people still working on this or is this more like a project at the time? Yeah, I think this was a project at the time. There are a lot of initiatives that are carrying on at the moment that are more recently updated. which is a gene disease, so if I search for APOE here, for example, gene disease, a kind of relationship database that allows you to kind of look at the profile of genes. Are they related to diseases? Are they, what pathways they exist in all that kind of stuff um but not in a knowledge graph so i think one thing we have found at crosser is that uh there are that there are a lot of academic initiatives such as helionet um that uh build out knowledge graphs um but without the right, but, but, you know, the right funding is required to continually update those graphs, which is why you see a certain, you see like pharmaceutical companies or biotechs that, you know, have financial incentives. So they're able to kind of continually update those knowledge graphs and keep them up to date. But yeah, as of 2017, I'd say that HeteoNet was a very good example. I think another good example of one that's a bit more up to date. But as of 2017, I'd say that HeteoNet was a very good example. I think another good example of one that's a bit more up to date is the clinical knowledge graph. This is open source as well. This was, I think, about a year ago. This is up to date as of about a year ago. A bit of a larger knowledge graph. So compared to Heteo net um kind of more entities so where hetio net had a schema of kind of gene compound disease in the surrounding edges uh the clinical knowledge graph had um that their scheme is a bit more bit wider so they had uh stuff like uh they've split up gene and protein and they included tissue and possibly cell type and some other entities that made it a bit more of like a wider knowledge graph yeah okay so yeah so all these entities build up a biomedical knowledge graph that can help in early stage drug discovery. But at Crosser, we found that there was an opportunity for an enterprise solution for an operating system that assisted with knowledge graph based drug discovery. that assisted with knowledge graph-based drug discovery. By that I mean a platform that has workflows for target identification, target validation, drug repurposing, with specific tasks such as functional gene annotation, identifying which module genes exist in, and analyzing the results of experiments such as RNA-seq and CRISPR screen hits, which we can go through an example in the platform of how that would work and how Neo4j helps us complete that task. That's good. Yes, that's good. So this is the Crosser platform. Yeah. I don't know. Do you need maybe zoom in a little bit? I'm on the big screen. Yeah, yeah, yeah. Yeah, that's better. That's cool. Thank you. Yeah, so this is the Crosser platform. And what we will look at now is a workflow for, just an example workflow for target identification following a recent paper or a high-impact paper that came out in 2018 that provided the system several framework for drug discovery with a focus on epilepsy so this paper came out in 2018 it was it was by the division of brain Sciences at Imperial and some other colleagues and helped to identify a novel target for epilepsy. And as an example, we'll go through how you could use Crosser to speed up this process and perform the same workflow that was done inside the paper. perform the same workflow that was done inside the paper, using our cross-platform. So leveraging software, leveraging Neo4j to kind of do the same workflow, but faster. So, what I mentioned is, we'll start by importing the dataset that was used in CRAFT. CRAFT being the analytical framework for target discovery from this paper. And one of the first things that they did note is that they had an interest in focusing on the cell type microglia. There's a couple of reasons why you might want to initially focus on a cell type. One would be commercial reasons. So if you have a pharmaceutical partner, then they may be interested in that cell type. Also, it may be easier to target genes that exist in that cell type with a drug. So we're going to focus on a module of genes from the craft paper that exist in the cell type microglia. So I will start by importing that module into the CROSA platform. So this is the CROSA canvas. And once I import the modules, this is a module from the CRAF dataset, which is a set of differentially expressed genes in the disease state of epilepsy that also enriched for the cell type microglia. And we can add those to the canvas. And in the paper, this was a this was a module of genes that they decided to focus on for the for identifying drug targets. You can see here, so we've imported differential expressed genes into the canvas. You can see here, here we have a network of genes that has built up an interaction network here. Probably one of the first things I'll do is genes that, as we're trying to look at kind of knowledge graphs and a network of genes, I'll get rid of the genes that don't have a network associated to them and then start here. So the different colors of genes you're looking at now represent how differentially expressed those genes were in the disease state. So, for example, CD22 here is a very deep red, which means that it was highly upregulated in the disease state, was highly upregulated in the disease state, which means it had a much higher expression in samples with epilepsy versus a healthy sample. And ZAP70, for example, here was downregulated in the disease state, so had a much, so had a lower expression level in the in the disease sample versus healthy state in mice which I think this experiment data set was taken out of mice okay yeah yeah usually it's mice I think yeah and and yeah there's a whole uh thing there um yeah one one quick uh sidebar and that is um there's been a very so so on the animal model versus uh human uh model and a discussion around uh the benefits of knowledge graphs and computational biology is that there was a recent um there was a recent bill passed in the u.s that required that meant the fda no longer require animal testing uh for uh drug for drugs to be approved by the fda ah okay yes so this is very, very interesting for knowledge graphs because they've added a new section that means that in place of animal models, you're allowed to have non-clinical tests, which includes cell-based assays, microphysical systems such as organ-on-chips, which is where you take some part of an organ and then you kind of put it on silicon and you're able to create an environment there. Or a computer model, where that computer model could be a knowledge graph that represents the cell in a disease state. So even on the regulatory front, there is form of progress towards computational models of disease. Yeah, I think that's very interesting because then you can, with the help of these models of a knowledge graph, you can find maybe an application that hasn't been used before for already approved, let's say, medicine. I could imagine. And then you can say, hey, you use this for, let's say, I don't know, fever, but you can also use this for, I don't know, headache and you don't have to do the testing anymore. Is that what it is? Yeah, I think it's a couple of things. I think first of all, ethically, it's not having to do animal testing ethically is a great benefit because it doesn't require mice or any other animal to be used in that process. Also an efficiency thing, it's a lot easier to build, it's a lot faster to build a computational model using a product like Neo4j of the cell and in different disease states, if you have the data rather than looking at looking at animal animal models so between those two things of kind of speed and ethical considerations yeah this I think this is a great a great step forward in kind of accelerating a better version of discovery absolutely yeah yeah the less animals are needed, the better it is. That's really good. Yeah. Yeah. A couple of comments here on this. Mark writes, can drive real efforts in drug repurposing. Yeah, I think that's true. Especially there when everything is already approved. I mean, maybe completely new discoveries is probably maybe a little, I don't know. I'm not really a fan of animal testing, but I see their point in some regards. But if something is already approved and you just need to step out of the originally approved path sideways a little bit, then I think it's totally fine. That saves a lot of time a lot of um a lot of um you know any animal pain as well so i think that's that's only wins here john says extends the possibilities of a digital twin work uh i think yes that's that's probably true what do you think about digital twin is there something yeah so across we we think a lot about the concept of a digital twin um you know we have a forward vision of our platform becoming uh you know a digital twin of the cell um and and this this act is is making digital twins more valuable um in meaning that digital twins where you can say where you can interactively for example you know model model a cell and the disease state and say you know if i up or down regulate this gene one of the downstream effects is it moving towards reversing the disease state? And that evidence is now kind of more widely acceptable because of the Modernization Act in helping pharma or biotech companies move forward with, move their drugs forward towards human clinical trials. Yep. forward towards human clinical trials. Yep. Yeah, so moving back across the platform. So yeah, so here you're seeing a network of differential expressed genes in the epilepsy disease state. And differential expressed genes are interesting because if they've changed the disease state there's a hope that if you can revert their expression to normal levels then you're you know reversing the the disease state. There's a lot of there's a lot of conversation around this about, you know, are the most differential expressed genes important? Or, you know, do you just want to find the causal driver of the disease state that's causing this differential expression and then affect that? But investigating the differential expressed genes is a good start. genes is a good start. And what we're going to do is focus on trying to identify small clusters inside this gene set that have been different expressed in epilepsy to potentially identify a target. So the first thing we will do is using the Neo4j graph data science library, which provides some is using the Neo4j Graph Data Science Library, which provides some great analytical features, one being Louvain clustering with community detection and some others around embeddings that potentially we can talk about later if we have time. Yep. We can identify the, we can talk about later if we have time. We can identify the, we can very quickly identify the modules of genes inside this network. So I'm gonna use the cross platform to run clustering. And here using Neo4j's GDS library, Using Neo4j's GDS library, we've identified five clusters of genes, five functional modules of genes in our network. the connections between these genes are interactions, which means that the proteins are functionally interacting and having an effect on each other in a functional environment. So the next thing to do is trying to identify which module you'd find interesting to interrogate. So the paper that we're following, CRAFT, takes the approach of identifying which ones are important based on the enrichment of certain gene ontology processes and pathways in those modules. And one example function that's important is immune response. So having this before, we can now try and identify which biological processes are prevalent in the modules. So what I've just done there is I've just kind of ran enrichment analysis for all these different types of function annotation for the modules and I can look at which ones I think are interesting to investigate and that will vary depending on your hypothesis and yourself as a scientist. So I've just expanded it here just so I can see it a little bit bigger. And this was done. We benefit a lot from Neo4j here. Having these functional entities such as disease, molecular pathway, biological process, cellular component in our knowledge graph and being able to use Neo4j to easily query those relationships. So that'll just do this in a fast way that might require a few steps in a Python or R script without this kind of tool. Yeah. And the module that I want to focus on is this pink one, just cluster two. So there I've just ran enrichment analysis for A, all the nodes separately, but also the individual clusters to try and identify which cluster has some interesting findings. Okay, yep. And it is inside cluster two, which is this pink highlighted cluster here, that we find a couple interesting processes that, as a scientist, I may be interested in investigating further so this would be so for me looking this I look at inflammatory response and inside yeah so inside the original paper one of the things they focus on is immune response processes per module. So if I identify the immune response, you see that this cluster has genes that are associated with inflammatory response and the regulation of immune response in red and yellow, respectively. Yeah. So as a scientist, that might lead me to believe that that module is interesting for one to investigate as a potential therapeutic intervention for epilepsy based on these biological processes and the fact that these genes are already found to be highly enriched for microglia as a cell type. Okay, yeah. So if I wanna focus down, so I've kind of selected this cluster so I can get rid of the initial results and I can focus here on this. So yeah, so it's going to focus down on this set of genes here. And I can quickly see if there's any links between any of the targets I've selected and epilepsy, you can see there's a few. There's a few already known links between epilepsy and the genes that I've selected. And because we're looking at epilepsy, another potentially interesting phenotype is seizures. So we can add that to the graph as well. I can see there's one gene inside this module that's also interesting for seizures, which partly validates the selection of this module to look at further. The relationships, I mentioned the relationships between these disease and phenotypes and the targets inside this knowledge graph is based off evidence that comes out of open targets. And that varies in what the evidence is. So just quickly, here's an example of the open targets evidence that's used to associate a gene to a disease. This is for a widely known association between APP and Alzheimer's disease. And you can see here that there's known genetic variations in APP is associated with patients that have Alzheimer's disease, and that can kind of increase the confidence in knowing that there's a relation between a specific disease and a gene. But also, there's a lot of other factors they consider, for example, text mining. So if there's recent papers that associate epilepsy with CCLF3 or seizures with ITGACs, then that'd be considered in the graph to create these edges. So yeah, so that's, we've just done that quickly to validate that there's some relationship between this cluster and epilepsy or seizures. So we can remove those genes now. And now we have a module of genes that are interesting to epilepsy. We can continue following the CRAF framework to identify a potential drug target for epilepsy. And the way that they move forward in doing that is after they've identified a module of interest, it is to try and identify upstream regulators that have an impact on the module as a whole as potential drug targets. So you're looking for genes that regulate the genes that are in this module and try and reverse them from the disease state back to the healthy state. And in CROSA we would do that using the master regulator analysis type. So here we're looking at this module. So if I run master regular analysis, I've now identified a little bit, now identified potential targets that are upstream of the genes that are inside that module. One of which that's been identified is CSF1R, which I can visualize here, which is upstream of these targets, SLC11A1 and ITUGB2, as a potential therapeutic target to as a potential therapeutic target to intervene with that module. This target was the one that was identified by the original paper as a potentially interesting target as an anti-epidemic drug target. Okay. For a couple of reasons. For a couple of reasons. One, because it regulates multiple targets inside the module. And also because it's a receptor, that R at the end means here, which means it sits on the outside of the cell and it makes an easy drug target to manipulate with therapeutic. Yeah, interesting. So, just so I understand it correctly, so we had this module, we had identified in this module that this is the genes in this, yeah, exactly, in this group of genes here is most likely, at least partly responsible for epilepsy and seizures. And now we have looked for potential genes or ways of how can we influence this group of genes in a way to make people healthy again if they are affected. And this one we identified is particularly good because it doesn't target only one, but it targets a couple in this group. So, yeah, so this module of genes are genes that were, So this module of genes are genes that were, you see they're all kind of red or shading towards red, which means they were all partly upregulated in the disease state, which means they were more expressed in the disease state than the healthy state. And they have a close interaction network between them, which means they represent a module of genes that are potentially functionally related because they interact with each other. So having that information, then we're looking to try and understand how we can reverse the differential expression of these genes to revert back to a healthy state uh so yeah we look for uh what genes um are upstream of of of that module um to identify which ones are potential drug targets um to to provide like a therapeutic intervention. And CSF1R is a good candidate because in our database, it's been identified to have an impact on two of the genes. Potentially that's more in other databases. It really depends on what data you have. And this is an open source dataset we're using for the regular network. But if you use kind of commercial data sets, they may have more information on which targets they affect. Yeah. And it flows with the paper, which does identify CSF1R out of the selection of upstream regulators, mostly because it is a receptor, which means it sits on the kind of outside the cell. And it's an easier drug target than other genes. Yeah, OK. That makes sense. You want to find something that is a good vehicle for the drug and is easy to get to, but has an impact on as much as possible. The question from Heather, the JAK2 has a blue relationship on itself. Can you explain what that means? Yes. means? Yes, so that would mean that JAK2 is known to downregulate itself. It's a phenomenon in biology that I'm not, as a scientist can explain well, but certain genes have a certain effect on their own expression. So some genes when expressed have an effect on the expression of when that transcribes to a protein and it can reduce or increase the expression of that protein. So it's a self loop of a negative self loop. I see. Okay. Thank you. There were other questions, but we can... Let's continue and two questions at the end. Yeah. So yeah, that is the demo that looks to replicate or at least like explore the findings of that paper. And as you saw, there were other targets in that list that might actually be interesting, separate to CSF1R. But if you were interested in CSF1R, then you could benefit from a knowledge graph in further exploration. So, it's going to be the wrong gene there. So this was the target that the paper identified and through upstream regulatory analysis we also identified as a potential target for that module and we're looking at epilepsy. So you can also benefit from knowledge graphs in exploring the kind of functional annotation of that target and the neighborhood. So here I can easily say if I was interested in this target, CSF1R, this target CSF1R, what pathways does it exist in? Transcriptual regulation by Bentex and other interleukin signaling, which means that might help build your hypothesis of which pathways you're interested in looking at for the kind of disease you're looking at. And you can go further. You can look at which drugs are currently known to impact that gene. And you can see there's already a few, there's already quite a few drugs that are known to have an effect on that gene. And you can see there's already quite a few drugs that are known to have an effect on that gene. I think the one that they tested in a preclinical model here was, I think it was, so this one is PLX7486. I think they had, I think they had, so this paper did move forward with a preclinical model of epilepsy to validate CSF, one eye inhibition and they used PLX3397 as a command to do that. I think the way they select that compound was it was a compound that they had available to them that they knew had an effect on that target. But you could also use a knowledge graph to explore other compounds that have an effect on that target and look at um if you you know had a compound of interest you could you could also look at um what other uh what other genes that compound uh has an effect on um so you know if if uh what potential side effects there are of of uh using that compound for that target, because drugs are known to be messy and often have an effect on various targets and not just the one that you're interested in. You have to consider that in your development cycle. Yeah, so that completes the example, like target identification of our platform. So that CSF1R was moved, was validated in a preclinical model, but would obviously need human trials before it was identified as an actual target of interest. Yeah, of course. Yeah. identified as an actual target of interest. Yeah, of course. Yeah. There was an addition to the Jack2 question. Maybe we can quickly slide it in. Sebastian gave some insights and he said, it's indirect effect mainly through stat and SOX. So these are downstream from Jack, he says. So I think that's just like the way it comes to be. So thank you for that, Sebastian. Yeah, yeah. I also, yeah, I mean, that's interesting. Yeah, I also saw that stat one and stat three are potential master regulators for these modules as well. So yeah, interesting that they've found that insight as well. So that's kind of the demonstration of a platform. And the other things that, I i mean if there are more questions i'm happy to answer that yeah yeah yeah yeah well there is uh for sebastian actually had a question or at least a comment uh the he says i think the fda likes to see at least dynamical models they'll both back back but i didn't want to interrupt the back from for the fda um models. They'll both back, but I didn't want to interrupt the back from, from the FDA part we talked about. Ideally, so sorry, I start again. So the FDA likes to see at least dynamical models, ideally parts of ones that have previously been approved. So I guess that makes sense for a new, your new drug. You don't want to go all in with only your let's let's say your ai advisor told you hey this is great let's use it and then it's it's approved i guess this does not work does the knowledge graph connect to relevant models uh for example bio models or something else so to to you know add in some outside information as well. Yeah, I think it really depends on what data you have available to you because I think knowledge graphs are incredibly powerful and I recommend anyone that is interesting to, you really realize the power of a knowledge graph when you're using them regularly because you realize how much data is very quickly available to you. Is Alex still there? Yep. Sorry. Yeah, no worries. Yeah, so you very quickly realize how much data you have available to you, depending on what you want to do. So what I've experienced is i mean you can see there in in just looking at this platform is that um you know there may well be a knowledge graph that's just a uh kind of interaction between uh you may say csf1r and you would just just want to look at the the neighborhood of those targets and that's the knowledge graph and you can see here that you know these gray lines are an interaction with a score between them and the red and blue lines are represent the targets that CF1R up or down regulate but if you have you know wider information adding that to knowledge graph is definitely beneficial. I think there's a lot of benefit in having different knowledge graphs for different disease models. So having a knowledge graph that's focused on Alzheimer's disease, having a knowledge graph that's focused on cardiovascular diseases, cardiometabolic diseases, and having the graphs traded there so that you can kind of have these wider considerations that are very quickly accessible due to the fact that they're sitting in this graph infrastructure such as Neo4j and they're kind of one query away from getting that data. I guess that like ties in with some of the challenges you have, right? I mean, you have lots of data available, but on the other hand, you need to incorporate that data and to build the builder knowledge graph, you need to see, okay, there's lots of data available, but you can incorporate lots of data, but then you need to be on okay, there's lots of data available, but you can incorporate lots of data, but then you need to be on the same level semantically, at least to sort of make sense to connect, you know, reasonably. Yeah, and I think that's a bigger problem in biology than other domains because, you know, there's a lot of arguments on if, for example, I mean, these two targets, you know, there's a lot of arguments on if, for example, I mean, these two targets, you know, they interact with that, they have an interaction in a knowledge graph. So that means the two proteins functionally interact, and they have a confidence score of, you know, 0.944. And this is coming from StringDB, which is another kind of great resource for, which I'm sure anyone in the biomedical know your names, but a protein-protein interaction open source database. But there's a lot of decisions in deciding like which databases you want to consider when building up your knowledge graph, you know, like our knowledge graph here Our knowledge graph here is built from eight, nine source databases that build up a wider knowledge graph across all these different biological entities. And even at the protein interaction level or the gene regulatory network, which is a directed relationship between genes. There's lots of different databases you can choose from. As a scientist, you decide which ones you think are valuable, which confidence thresholds you think you're happy to work with. So if you're happy with, you know, a 90% confidence threshold when looking at analytics, then use that. But if you're very strict, you want like a 98% threshold, then you might increase that before you start analyzing that as a knowledge graph. All right, and then there is one more question from Creek Compendium wants to know if you know tools like druglike made by Martin Shkreli, Shkreli, Shkreli, sorry, which does apparently computational chemistry. I don't know that maybe you know it and leak, legal talking. Druglike? Druglike, that's what he writes, yeah. If that is something you are aware of or is there something if not then uh not not not not hugely aware of it um uh but uh yeah yeah not aware of it but um there are definitely a lot of open source initiatives. It looks like one of those that kind of moves forwards with open software initiatives that are trying to enrich the amount of data that we have available to us as scientists trying to identify novel targets, trying to optimize the best leads for those targets and move forward. So yeah, great to see any open source approaches to that. Yeah, cool. And then maybe one question for me is on the future, on the outlook on other use cases, on the future on the outlook on other use cases other than target identification you see here for knowledge graphs in this particular field of drug discovery? Yeah I think that you know we are really at like I mean that quote that I come back to on you know on computational biology providing reference map for biology as an atlas of life the whole scale of individual insights um by florian markovets he's the cancer researcher at cambridge um i think it does i think we're definitely at a turning point in using kind of these computational approaches to really drive forward drug discovery. Partly from the way we're thinking, I've seen, you know, almost all the big ones uh you know they all you know adopt neo4j um uh as as you know as a knowledge graph i think i saw a a recent presentation by neo4j um at a conference in basel i was at that that you described that well yeah um and uh so so technically we're kind of, you know, the technical infrastructure is moving that direction. Last 20 years, we've seen lots of data generated through kind of initiatives, such as the Human Genome Project, that's really kind of generating that data that's then being able to form into a knowledge graph. We're seeing lots of companies uh being kind of created around around this idea of of computational biology and knowledge graph based drug discovery um some to mention would be benevolent ai um i think uh isomorphic labs is an interesting one to check out it's it's kind of google's or uh the deep mind if you know those know those ai guys um they've kind of started their android discovery company focused on that kind of computational approach and i'm sure that the data infrastructure layer is going to be powered by by knowledge graphs um so, and with, you know, on the regulatory side, seeing stuff like the FDA Moderation Act is definitely helping all of this coming together to, I think really speed up the time from kind of therapeutics and getting that to the patient. Yeah, yeah, absolutely. Yeah, another comment from Mark. He says, I agreed, maybe use disease-specific knowledge graphs for target ID and larger generic multi-disease knowledge graphs for drug repurposing. So, yeah, I think definitely disease-specific knowledge graphs are really valuable when you're focused down on that. If you can get the... Building up these knowledge graphs requires a task of, you know, if you're looking at disease-specific knowledge graph, you need to have the curation to get it down to that kind of disease model layer. But if you can get down there, then I think there's a lot of benefit of being there. And then yeah, for drug repurposing, or even target repurposing. So you know, which which targets might be relevant across diseases, which diseases have similarity to other diseases. So if I'm a company looking at disease area X, can I potentially use all the work I've done so far to focus on another disease, whether that's through the same drug or the same target, then that's where more general knowledge graphs are useful. Yeah. I mean, this is now probably thinking a little bit into the future, but I could imagine if you think about a knowledge, sorry, a disease specific knowledge graph, you're almost generating a digital twin of the paths, of the attack paths of a virus or whatever else or cancer or something as well as of the of the of the of the medicine the drugs that that help against it and then you have you know the direct way and you can you can directly see okay where is where is the where are some weak points where is where is it strong where where does it help where how does it you know how does it work along the whole the full line and then you have you know the the best scenario possible but it's it's you know it's it's becoming very complex obviously yeah yeah yeah exactly um you want to you know you want to the perfect situation for each disease is to have an individual model for that disease a digital twin of you know someone in that disease state you can uh then try and you know in a silico model see like if i intervene with that target uh what's the total impact of that disease state um and as long as you're considering like the whole context then you know i think it's important there to think about like which what cell type are we looking at, which tissue type are we looking at. If you have all those wider contexts, then you can kind of bring them all together to find and take what is like a lot of individual insights and then build novel insights from the wider picture. Yeah, I think that's what's complicated like what sebastian says the problem of digital twins to get to the mirroring of biological variability to get the mirroring of biological variability right yeah where you again need dynamical models or at least very well tuned statistics so yeah i think that's that's probably super super complicated i mean the body is it. Yeah, it's messy. Yeah, it's messy. And it does things differently sometimes. So it's not as, unfortunately, it's not as ones and zeros in this regard, unfortunately. It's not only ones and zeros like with the digital world. Yeah, exactly. All right. Cool. I think that was very interesting. Ben, exactly. All right. Cool. I think that was very interesting. Ben, thank you very much again. Thank you for your questions and comments. If people want to continue to keep the conversation going, how would best people reach you or Crosser? How would they get in touch? Yes. So I'll just share this slide quickly um so this is how you can get in contact um either my email or the kind of social media that i'm available at is linkedin um there's also a cross linkedin page um if you want to visit our website we have a form you can fill out to kind of speak further if you want to visit our website, we have a form you can fill out to kind of speak further. If you want to get a more in-depth demo of the platform or have a conversation about use cases, that kind of stuff, then feel free to reach out over all three of these options. And yeah, also, yeah, check out our website. We have some we're kind of in the process of posting various blogs. we have some we're kind of in the process of posting various blogs one i did recently on uh recommendation systems of drug discovery that we haven't really spoke about here but i spoke about a nose 2022 which is uh which was the neo4j conference that was uh yeah really good um so so i have i did a separate talk there on on uh recommendation systems and that i think it's also interesting if someone should check that out. And there's a blog on our website that goes through that. And we'll probably soon publish a blog on kind of this example of epilepsy as a target identification in our platform. So yeah, definitely follow LinkedIn and the website and get in contact via any of those free options. Cool. Thank you. There's another question from Dev Tether. Maybe we squeeze that in. Wants to know if you have thought to expand CROSSA after the drug has been discovered to use in trials, to use knowledge graphs in CROSSA during the trials or, you know. Yeah. Yeah. It's interesting. So I think knowledge graphs in clinical trials is definitely a great use case as well. I think for us in clinical trials, I see knowledge graphs most beneficial in stuff like patient stratification. So, you know, trying to identify, you know, which patients are based on their demographic and kind of various other factors would be most relevant for that clinical trial. Maybe there's some stuff with geography there. I think, you know, knowledge graphs go even further down the pipeline to, you know, end patient care. line to you know end patient care you know a great a great talk uh recently on u4j live was dr ai that looked at a knowledge graph that used uh that kind of had like you know symptoms and i mean potential uh i think it was a chat bot that uh was kind of leveraged a knowledge graph to help to help the end patients um so i recommend checking that out. So yeah, definitely clinical trials down even further is as a lot of benefits to kind of knowledge graphs there that yeah, we are thinking about. Yeah, cool. Yeah, I posted a link to the Dr. AI session in the chat. I'll add it to the YouTube description also. We had lots of links, so I added them all to the YouTube video description. So if you are watching this, you should find all the links there. So I think that's great. Thank you, Ben, again for your presentation for the demo in particular. I think that was very interesting, for your presentation, for the demo in particular. I think that was very interesting, very good to follow and very good to see. It's a cool project, so yeah, check it out. Go to crossout.co.uk or, like Ben said, write him an email. So with that, yeah, I think we're at the end of this episode. Again again thank you very much for watching thank you very much Ben I'll be back with the final episode of the GraphQL Book Club tomorrow Wednesday at a little earlier than this the slot an hour earlier 4pm Central European Time tomorrow and on Thursday another Neo4j live stream. This is a very busy week. On a book release, Graph Data Processing with Cypher with my colleague Ravi, who wrote a book on the topic. And we'll do an overview of this. So that's happening on Thursday. Yeah, with that, I wish you all a great rest of your day. Have a good evening or enjoy your working day. And have a good rest of your day. Have a good evening or enjoy your working day and have a good rest of your week and see you soon at another of these sessions. Like and subscribe as always and yeah, see you soon. Thank you, Ben. Thanks, guys.
Neo4j Live: Crossr - Knowledge Graph based Drug Discovery
Knowledge graphs are key to extracting insight from volumes of available biomedical data. In this session, we'll cover a practical example of how Neo4j + Graph Data Science can be used to create graph-based features to help identify potential gene targets for a given disease.

Crossr: https://www.crossr.co.uk/
Blog: https://www.crossr.co.uk/post/recommendation-systems-in-drug-discovery
Ben Vozza: https://www.linkedin.com/in/ben-vozza-a652b695/
Lightning Talk @ NODES 2022: https://youtu.be/q5061JL5LaU 
All biology is computational biology: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2002050
Hetionet: https://het.io/
Open Targets: https://platform.opentargets.org/
Clinical Knowledge Graph: https://ckg.readthedocs.io/en/latest/INTRO.html
A systems-level framework for drug discovery identifies Csf1R as an anti-epileptic drug target https://pubmed.ncbi.nlm.nih.gov/30177815/
StringDB: StringDB: https://string-db.org/
Doctor.AI Live Stream: https://youtube.com/live/lZidMw6vVWY

0:00 Intro
3:40 Crossr Overview
7:05 Open Data in Life Sciences
13:00 Biomedical Knowledge Graph - Crossr Demo
19:05 FDA Modernization Act 2.0
24:10 Crossr Demo (contd)
44:10 Q&A & Contact
1:01:00 WrapUp


#neo4j  #lifescience #healthcare #biomedical #knowledgegraph