If you’re working in pharma or biotech, artificial intelligence (AI) is no stranger. You likely use it to help you identify new targets to explore for a therapeutic area, for drug repurposing or to identify plausible biomarkers for your disease of interest. You may think using AI is enough and will have all the answers if there are enough data. However, there’s a big problem with that assumption.
Limitations of AI-derived biomedical data
Biomedical data have errors and are mainly unstructured. So, removing errors and structuring the data to make them usable to address specific questions is essential, yet far beyond current natural language processing (NLP) approaches and generative AI models with large memories. So for AI models to provide insight, the underlying data must be based on ‘high-quality’ data. High-quality means it’s got to be accurate, yet also complete and comprehensive, up-to-date and standardized.
To complicate matters, scientific knowledge evolves daily, and the genetic basis of hundreds of diseases are identified each year. So the amount of biomedical data is constantly growing and, well… there’s a lot of it. Yet we still don’t know what 99% of our DNA even does. So with all the groundbreaking discoveries yet to be made, you don’t want to miss anything that will help you make your next big discovery.
Like panning for gold
Can you reconcile your need for data that’s accurate yet also complete? How do you find the needles in the haystack yet ensure you won’t miss valuable data that could give you unique insights? What’s the best way to convert biomedical data into biomedical knowledge?
And, even if the data you’ve got ticks all those boxes, there’s always the question of accessibility. How are you going to access it? And how much will you have access to? What if you only want a small slice of the data? Are there access models that will accommodate your specific needs, whether big or small?
Biomedical data analysis without core knowledge = statistically significant nonsense
To turn data into its usable form of information to create knowledge, it must be honed, fine-tuned and polished—by a human. This produces high-quality data and is the very core and backbone of our knowledge and database offerings, such as our premier QIAGEN Biomedical Knowledge Base. They are trusted by over 90,000 scientists worldwide, in over 4000 accounts, to make confident decisions.
As leaders of this augmented scientific data collection approach, we’re excited by the development of AI tools for curation and continue to evaluate and evolve our technology to take advantage of beneficial advancements. We apply state-of-the-art AI to maximize the completeness of evidence in our knowledge base. But for scientific interpretation, scalable content quality is ultimately essential.
AI + manual curation = Accurate and complete biomedical data
Our curation team scales with today’s growth in scientific publishing because we leverage NLP and other technologies to speed curation but still rely on human certification of biological findings to ensure quality. With domain-specific analytics, you can compute over our unparalleled knowledge base of high-quality evidence; something AI cannot infer.
Accurate biomedical knowledge, right off the shelf
Our experience and findings show the quality of AI and machine-generated content is not good enough for scientific purposes. We regularly identify many false positives and false negatives from machine-only curation. That’s why we’ve been perfecting our market-leading ‘augmented molecular intelligence’ approach for over two decades and leverage 200+ PhD scientists to work alongside machines to verify and improve the utility of the content to drive sound research hypotheses.
Our human curation team enables us to:
- Collect accurate findings from context, graphics and supplemental data
- Clarify data, disambiguate terms and select the best reference data sources
- Explain ‘uninterpretable’ data that stands up to statistical tests and improves AI predictions
- Remove the noise: Our large-scale and efficient manual review process eliminates the noise in AI-collected data so you can quickly arrive at insights you can trust
Access the data your way
Yet, having a collection of high-quality and reliable data alone isn’t enough. It’s got to be accessible when you need it, how you need it.
That’s why we’ve developed API access to QIAGEN Biomedical Knowledge Base. Now you can rest easy with data that’s not just reliable; it’s also available the way you want it, from the entire knowledge base to just the right slice for your project.
- Want to generate a specific list of targets for lung carcinoma, or for a neurological disorder?
- Would you like to build a disease-specific knowledge graph to unravel the mechanism of action of molecules surrounding your favorite gene?
- Want to find which targets could go into a phase I trial for a drug you are trying to repurpose for another indication?
That’s all possible with data that’s easy to access any way you’d like it.
Reliable insights, sliced and diced just for you
Learn about how flexible access to QIAGEN Biomedical Knowledge Base will open doors to reliable data that deliver true insights. With >35 million findings, >2.1 million entities and >24 million unique relationships, it’s got data that will fuel your data- and analytics-driven drug discovery, at whatever scale you need. Request a consultation to discover how this powerful tool will transform your drug discovery research.