AI has begun to transform and accelerate every step of the drug development lifecycle from target identification to drug discovery and clinical trial design. However, the rapid development and adoption of AI platforms come with its own set of challenges regarding data quality, data privacy, and an evolving regulatory landscape. Panelists in this webinar will share their insights into the major opportunities and challenges facing biopharma leaders today as they continue to evaluate and incorporate new AI tools into their R&D workflows.
In the rapidly evolving landscape of drug discovery, the ability to integrate high-quality research findings into knowledge graphs is paramount. For over twenty years, the Ingenuity team with QIAGEN has curated these nodes and relationships. Our QIAGEN Biomedical KB-HD make these data available for consumption outside of Ingenuity Pathway Analysis.
In this talk, we will:
• Explore how to query and leverage this curated data resource to accelerate the drug discovery process.
• Provide live demonstration of the underlying database Biomedical-HD
• Show how the Biomedical KB-HD can be rapidly deployed
• Show how the underlying schema and ontologies could serve as a scaffold for integrating your own research
Overall, this demonstration will show the critical role of knowledge graphs in predicting adverse outcomes and toxicity, highlighting their transformative potential in steering pharmaceutical research and development.
Learn more about Biomedical KB: https://qiagen.showpad.com/share/GmpIJaN5kRfsHRD5WPwgc
In this 60-minute session, the trainer will go over how to effectively utilize a relationship database for below applications. In this case, QIAGEN Biomedical Knowledgebase (Biomedical KB – a database containing high-quality relationship literature findings, the same database backing Ingenuity Pathway Analysis/IPA) will be used, but learning can possibly be applied to similar databases as well.
The trainer/QIAGEN team will go over
• Conducting Geneset Enrichment Analysis (GSEA)
• How multiple Geneset enrichment methods can be used instead of only those integrated in software GUI
• Target Validation at cell, tissue, and organ level
• Potentially other use cases, queries, and applications requested by registrants
Learn more about Biomedical KB: https://qiagen.showpad.com/share/GmpIJaN5kRfsHRD5WPwgc
Ingenuity pathway analysis (IPA) which is currently cited in tens of thousands of publications and used by large number of biopharmaceuticals is backed by QIAGEN Biomedical Knowledgebase. Accordingly, Biomedical relationships knowledge has more or less become a requirement for innovative data- and analytics-driven drug discovery. It powers biomedical knowledge graph analysis, artificial intelligence (AI)-driven target identification and many more applications.
In this 1hr webinar, the speaker will introduce Biomedical Knowledgebase and how it allows its users to tackle applications that are not doable by Ingenuity Pathway Analysis graphical user interface or can be done faster and with more flexibility programmatically. The speaker will demonstrate queries such as
• Quickly find the shortest connections between genes/proteins/metabolites of interest in the context of specific disease through queries
• Systematically build a network given a short list genes/proteins/metabolites/chemicals
• Recreate a drug mechanism of action
Note: Per feedback of registrants, we may edit above topics as we would like to cover what would be most relevant to you.
You’re a data scientist working on drug discovery, watching the geography of your discipline shift dramatically with the introduction of accessible generative AI. It seems like everyone is talking about the incredible AI-powered future that awaits by using their tool. But is AI really that useful? What objective, concrete gains can you expect to see in your research?
You may be familiar with the QIAGEN Biomedical KB-HD, formerly known as the BKB. It’s the leading manually curated biomedical knowledge base, boasting over 24 million high-quality relationships created over 4000 human-years of manual work. But it takes time to deliver validated, full-context relationships.
Our new offering, the QIAGEN Biomedical KB-AI, uses generative AI data curation to create the largest biomedical knowledge graph on the market. Quality is great, but sometimes, you need the kind of quantity that only AI can enable. Let's get into it.
AI data curation helps create large, streamlined and timely knowledge graphs that cover more ground than any human-curated effort could. If you're looking for the whole picture, you might just find it in an AI-curated knowledge graph.
In an era of near-limitless public experimental data but little standardization, meaningful insights are lost to noise. Large collections of quality experimental data are essential for big-picture discoveries that stand up to scrutiny.
In this webinar, you will learn how to feed your drug discovery programs by integrating connections mined from QIAGEN Biomedical Knowledge Base with deeply curated disease datasets from QIAGEN OmicSoft Lands.
Combining unified 'omics datasets with contextual relationship evidence from our knowledge graph, we will address complex questions such as:
In an era of near-limitless public experimental data but little standardization, meaningful insights are lost to noise. Large collections of quality experimental data are essential for big-picture discoveries that stand up to scrutiny.
In this webinar, you will learn how to feed your drug discovery programs by integrating connections mined from QIAGEN Biomedical Knowledge Base with deeply-curated disease datasets from QIAGEN OmicSoft Lands.
Combining unified 'omics datasets with contextual relationship evidence from our knowledge graph, we will address complex questions such as:
• Which genes aren't expressed in normal tissue, yet are expressed in diseases of interest, based on experimental evidence?
• Which of these proteins are cell surface proteins, with evidence for extracellular localization?
• How are these proteins related directly or indirectly to disease pathways, and can these be connected to known drug targets?
• Can we identify correlated biomarkers, mutation targets, clinical factors or other means of cohort selection?
In an era of near-limitless public experimental data but little standardization, meaningful insights are lost to noise. Large collections of quality experimental data are essential for big-picture discoveries that stand up to scrutiny.
In this webinar, you will learn how to feed your drug discovery programs by integrating connections mined from QIAGEN Biomedical Knowledge Base with deeply-curated disease datasets from QIAGEN OmicSoft Lands.
Combining unified 'omics datasets with contextual relationship evidence from our knowledge graph, we will address complex questions such as:
• Which genes aren't expressed in normal tissue, yet are expressed in diseases of interest, based on experimental evidence?
• Which of these proteins are cell surface proteins, with evidence for extracellular localization?
• How are these proteins related directly or indirectly to disease pathways, and can these be connected to known drug targets?
• Can we identify correlated biomarkers, mutation targets, clinical factors or other means of cohort selection?
Here's the scenario - You're a passionate scientist in a leading pharmaceutical company hoping to uncover a transformative drug candidate. Naturally, you use artificial intelligence (AI) to help you target the most promising leads. After weeks of dedicated work, you start to realize that something seems a little off with your results. Maybe you recognize that the algorithm-proposed drug candidate has a history of poor tolerance in human clinical trials. Or perhaps the drug candidate fails to reproduce even the most basic PK/PD modeling results in vitro. Just like you, many drug discovery researchers have found themselves misled by the results proposed by AI.
Even with state-of-the-art algorithms, outcomes of AI for drug discovery heavily depend on the data and context backing them up. Many researchers, just like you, are seeking ways to navigate the intricacies and challenges of this rapidly evolving field. The path to successful AI-driven drug discovery may appear complex, but with the right guidance, AI can significantly enhance both the efficiency and effectiveness of your drug discovery journey.
Here are 3 of our best secrets to help ensure your success when using AI for drug discovery:
1. Start with quality data
The foundation of any successful AI model lies in the quality of its training data. Inconsistent or noisy biomedical data can introduce biases, potentially making the AI model veer off course. Imagine trying to master a language using an inaccurate dictionary; the outcome would be a garbled mess.
Similarly, training an AI model on low-quality biomedical data can lead to misguided conclusions. Data quality, integrity and relevance are paramount. Using expert-curated databases ensures the model begins with accurate and comprehensive knowledge.
That's where our QIAGEN Biomedical Knowledge Base (BKB) database comes in. Curated by experts and continuously updated, QIAGEN BKB ensures you equip your AI models with the best possible start. It offers a strong foundation for building knowledge graphs and data models. Just as a building's strength depends on its foundation, your AI model's efficacy depends on starting with quality data.
2. Root AI inferences in real biological contexts
The power of AI lies in its ability to process vast amounts of information quickly. But it's worth remembering that an AI model, regardless of its sophistication, doesn't inherently understand the complexities of human biology. It sees numbers, patterns and correlations but not causations.
An AI model might draw associations that, at a glance, seem significant. However, without the biological context, these associations can be misleading. To avoid chasing after false positives, it's crucial to ensure the AI's conclusions are rooted within the biological realities.
Here's the good news: QIAGEN BKB and QIAGEN Ingenuity Pathway Analysis (IPA) have built-in causality. With IPA you can quickly check the conclusions your AI generates. IPA's intuitive GUI interface provides visual pathways, disease networks, upstream regulators/downstream effects and isoform-level differential expression analysis, all with the ability to bring in primary datasets for custom-tailored analyses.
3. Validate findings with peer-reviewed research
Science, at its core, thrives on collaboration, verification and iteration. A discovery today can be the stepping stone for a revolutionary breakthrough tomorrow. AI can be a potent tool in accelerating these discoveries, but its suggestions need validation.
While using AI for drug discovery can uncover potential candidates, it's essential to validate these findings using published, peer-reviewed studies. Not only does this process lend credibility to your findings, but it also provides invaluable insights. For instance, understanding which cell lines have been used in previous studies can guide your preclinical testing, ensuring you're on the right track.
For this crucial step, QIAGEN OmicSoft's curated omics data collection is your ally, especially for enterprises in need of high-quality multi-omics datasets. You can tap into a comprehensive landscape of sources, offering validation from published studies beyond just a single public repository. Such validation lends credibility to your discoveries and provides invaluable insights. QIAGEN OmicSoft's curated omics data collection facilitates this crucial step, bridging the gap between AI predictions and experimental data to construct disease models and digital twins of cells/organs/organisms.
Validating your cell line selection is also a critical factor for successful preclinical research. Using ATCC Cell Line Land, you can access authenticated cell line ‘omics data to make informed decisions before purchasing cell lines, helping to streamline your workflows, save time and resources, and enhance the predictability and reproducibility of your studies.
You can be confident in steering your research in the right direction with AI, provided you eliminate guesswork and maximize efficiency by using quality biomedical data, ensure biological soundness of AI results and validate your findings. By applying these three powerful tweaks to your AI, you'll surely revolutionize your drug discovery by spotting promising leads much quicker.
We design our QIAGEN Digital Insights knowledge and software with your success in mind.
After all, the future of new therapies is waiting, and we want to ensure you're well-equipped to lead the way. Want to uncover more secrets to drive drug discovery success from our experts?
Continue reading to see how QIAGEN can power your research.
Looking to collaborate further? Fast track your analysis with QIAGEN Discovery Bioinformatics Services.
If you're working in pharma or biotech, you probably rely on artificial intelligence (AI) to help you identify new drug targets or disease biomarkers within large datasets. As pharma scientists, we know AI is becoming standard practice. But what happens if your AI models are accidentally fed data from unreliable sources? Imagine wasting months of time and resources chasing a drug target that turns out to be a flop. Ouch.
The pitfalls of unreliable, unstructured biomedical data are not just hypotheticals - they're a stark reality we and other researchers face around the globe. For AI models to provide reliable insights, the underlying data must be 'high quality', meaning it's accurate, comprehensive, up-to-date and standardized.
Leave AI errors in the dust with expert-curated biomedical data
Enter QIAGEN Biomedical Knowledge Base (BKB), a repository of meticulously curated biomedical relationships data. We've tailored the BKB database with a keen, expert eye to ensure the molecular connections and relationships are only extracted from the highest quality sources like peer-reviewed scientific literature.
With continuous updates and enriched contextual information, such as tissue specificity and relationship directionality, we make sure you always have access to the most current and reliable data.
As a data scientist, you can mine our BKB through database files or directly from API integration. Our BKB data is particularly useful for creating graph-based models that handle interconnected heterogeneous biological data far better than simple relational databases do. With API integration, we can even help you customize BKB to seamlessly fit with your internal workflows.
What discoveries are waiting for you with the power of QIAGEN BKB?
Here are 3 ideas to get you started:
1. Identify high-quality drug targets using inferred causal interactions
Literature is full of publications showcasing associations between biomarkers, receptors and disease. But how do you know if that relationship is strong or weak within the context of finding a good target for drug development?
Using knowledge graphs powered by BKB data, you can thoroughly explore causal relationships between your target and disease, before you ever even set foot in the lab. Start with a high-level view of the pharmacological landscape of your target, then zoom in on the pathways directly linking target and diseases. From there, you can overlap pathways and functions to discover the exact mechanisms that connect a target to a disease, such as activation/inhibition signaling pathways or RNA binding.
2. Explore the current clinical landscape around your target
QIAGEN Biomedical Knowledge Base contains a wealth of clinical trial information connecting drug targets and their indications. Use knowledge graphs to intuitively navigate from your target to ongoing and completed drug trials, including whether the drug is an antagonist or agonist, the drug's indication of use and trial phase.
Using BKB, you can efficiently prioritize diseases for drug development within the current clinical landscape surrounding your target.
3. Build disease interactomes using protein-protein interactions
What if there are promising disease indications for your target that have not yet been discovered in the literature? You can easily use BKB data to build a knowledge graph based on the protein-protein interactome (PPI) surrounding your target. You can use the PPI graph like a roadmap, measuring and ranking the protein-protein relationships by distance, and determine which diseases are related to those proteins.
This may even lead you to discover less obvious disease indications associated with your target - creating perfect positioning for your new drug to accelerate your entry into the market.
"If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team" (1).
Andrew Ng, Founder & CEO of Landing AI
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Don't miss your chance to supercharge your AI toolbox and transform your drug discovery
During a recent webinar we hosted, a unique and talented group of experts came together to share their insights. They used gene-disease knowledge graphs created by combining AI with BKB to reveal promising novel drug targets for neuroinflammatory disease and other devastating autoimmune disorders.
This webinar offers a unique opportunity to learn directly from the experts how you can best use BKB to enhance and advance your drug discovery strategy. See BKB in action with an example of real-world drug discovery. Watch the webinar now.
Continue your journey with us and explore how you can combine AI and QIAGEN Biomedical Knowledge Base to maximize your drug discovery efforts while minimizing your timelines:
QIAGEN BKB powers the leading pathway analysis software in life science research
Looking for a transformative software suite that already integrates QIAGEN Biomedical Knowledge Base? Was that a ‘yes, please’?
QIAGEN Ingenuity Pathways Analysis (IPA) merges the advanced analytics of BKB with intuitive user design - all without ever having to write a line of code. Learn more.
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