How Artificial Intelligence and Machine Learning Can Help Scientists and Researchers Manage Vast Amounts of Data - Q&A
Lately, it seems that Artificial Intelligence (AI) and Machine Learning (ML) are the latest hot topics and everyone is talking about them. Perhaps you’ve been hearing about these topics, but you don’t understand or know exactly how this technology can help your research be more efficient, save money, improve workflows, and ultimately help you use your data to create better outcomes.
Trevor Vieweg, one of the co-founders of Limmi, an AI powered data and analysis platform, answers some of the most common questions he receives from scientists and researchers about the latest changes within the biotech industry and how AI and ML are impacting the sector.
First, what is artificial intelligence and machine learning?
Over the next few months we are going to delve into Artificial Intelligence (AI) and Machine Learning (ML) even more, so be sure to read our future blogs; but for now I’ll share a very brief explanation.
Artificial intelligence enables computers to perform tasks that typically require humans and human expertise. Due to major advancements in the field over the past 10 years, AI can now be used in nearly any industry and includes various subfields such as machine learning, natural language processing, and computer vision.
Machine learning is a subset of AI that involves training algorithms to recognize patterns in data and to make predictions or decisions based on those patterns. ML algorithms can be supervised, unsupervised, or semi-supervised, depending on the type of data and the desired output.
In short: AI is a broader concept where the machines perform tasks that typically require human intelligence, while ML is a subset of AI that focuses specifically on teaching matches how to learn from data.
Our lab has a great deal of data and I think we want to use AI, but I don’t know where to begin.
R&D labs are the perfect environments to utilize AI because of their large amounts of data. One of the main advantages of AI is its ability to quickly and accurately process and analyze large amounts of data. AI can help with all aspects of data management and insights - it can improve your data ingestion workflows, analyze that data for insights, and improve data visualization, all of which helps researchers move faster by focusing on the science and not the software..
The first step is to understand your goals and what you want to achieve with AI - AI can do many things, but like any tool you have to understand what you want to achieve with that tool. We at Limmi specialize in helping customers understand what they can do with AI, how it can help them achieve their goals, and then assess the best fit for your research needs. Limmi has created a cloud-based, easy-to-use platform that was written with scientists and lab employees in mind to handle all aspects of ML and AI development and use. Most scientists and researchers have unique needs as well, and the configurable nature of the Limmi platform allows us to provide a tailored application for your needs.
How can AI be used within our lab?
There are numerous ways AI can be used within research and development labs and I’m going to discuss three broad areas. The possibilities are truly endless as the pace that AI is improving is truly exponential.
- Drug discovery and development: AI models can aid in virtual screening of large compound libraries, identifying potential drug candidates based on their predicted efficacy and safety profiles. This process accelerates the drug discovery pipeline and reduces the costs associated with experimental screening.
- Gene editing and gene therapy: AI models can help researchers identify potential off-target effects of gene-editing technologies like CRISPR, enabling the development of safer and more precise gene therapies. Additionally, they can be used to predict the therapeutic potential of specific gene targets, streamlining the development of gene-based therapies.
- Personalized medicine and diagnostics: By analyzing genomic and clinical data, predictive models can identify genetic variants associated with disease risk or treatment response. This information can be used to develop personalized treatment plans and improve diagnostic accuracy, ultimately enhancing patient care.
These are just a few areas that Limmi is working on - as I mentioned, the possibilities are nearly endless, and not limited to just research.
I have very specific laboratory workflows, can Limmi be configured to meet my unique needs?
Absolutely! Limmi has created an innovative platform that works perfectly with your existing systems, so we can integrate with your existing processes in a matter of weeks. Because we deploy the application to your cloud instance, you and your lab maintain and remain in complete control of your data. There is no wasted time moving the data from one place to another and everything remains in your hands.
However, if your workflows are outside of what Limmi’s platform currently offers we are able to completely configure an AI solution to your unique needs. Some of our current clients required specific machine learning capabilities for their data and we were able to configure the AI component of Limmi to suit their requirements. We can configure a custom AI solution when needed.
I have specific and unique data, can Limmi handle these data sets?
Again, the answer is absolutely! Limmi has an extremely flexible data model and can handle a variety of data. For example, our platform is capable of handling data that includes image data, biomarker types, genomic data, clinical trial data, behavioral data, and more.. We can typically ingest and handle your data within two weeks, letting you get back to your research quickly.
How do we know that the AI generated material is correct and fits with what our lab requires? Does the FDA approve of using AI models?
Ensuring your AI model is running correctly is a key part of any AI engineering operation, and a focus for Limmi. While being able to audit and measure that results are correct is important in any industry, the human safety aspect of biotech means additional safeguards need to be put in place to make sure the model does not have any incorrect bias, and that over time its results are accurate.
In addition, biotech applications need to meet many regulatory standards, including HIPAA, FDA, GDPR, and many others. The FDA has released preliminary guidelines for machine learning applications and Good Machine Learning Practices (GMLP) encouraging companies to think about these aspects of AI early in their development process. Limmi is designed from the ground up to meet these requirements.
There are numerous ways to ensure that the generated material is correct. It’s important to ensure that the data used to train the AI model is accurate and representative of the problem being solved, which requires strong partnership with the experts doing the research. This includes validating the data through multiple sources and peer reviews to ensure the inputs for training and testing data sets are correct. A model is only as good as the dataset it’s trained on.
It’s crucial to regularly evaluate the AI model’s performance through testing and validation, which can involve using a portion of the data to validate the model’s predictions or comparing the model’s results to a known baseline or gold standard. It’s necessary to monitor the model’s performance over time to ensure that it continues to produce accurate results. This may result in retraining the model with new data or adjusting the model’s parameters based on feedback.
Limmi is designed to seamlessly provide these capabilities to researchers so they don’t have to worry about how to implement these controls. Limmi will work closely with your scientists and researchers to optimize their deployments and create the customized AI solution that will best fit your needs.
To end this Q&A, can you provide a bit of info about you and why you started Limmi?
Limmi was born out of the simple belief that modern biotech should have the best AI tools available to create better outcomes for human health. My cofounder Bryan and I have over 30 years of experience as engineers, leaders, and entrepreneurs in the technology sector, focusing on AI applications in recent years..
We first got excited about Biotech by reading about the advancements in AI and the huge amount of applications in the space. When we first saw the state of biotech software, we knew there was a better way and wanted to help. We wanted to leverage our business technical experience to harness the full potential of laboratory data.