Getting Data Ready for AI: How to Prepare Your Data for Machine Learning

Artificial intelligence (AI) and machine learning (ML) are revolutionizing research in chemistry and biology. If you’ve heard of Google DeepMind’s AlphaFold, you’ll know it made headlines by predicting protein structures with remarkable accuracy. Now, with the latest AlphaFold3 model, we can even predict how complex proteins interact with all other molecules in the protein data bank.

So, how can you unlock the power of AI for your research? It’s as easy as feeding your data files into an algorithm and having it work its AI magic, right?

Unfortunately, no.

While AI tools may help you identify patterns, recognize characteristics, or make predictions, the underlying algorithm must be trained on high-quality data.

Even with a well-trained AI like AlphaFold, any new data must be consistently annotated for the algorithm to make sense of it.

In this article, you’ll learn:

What is meant by “high-quality data”?

How to overcome common stumbling blocks of data findability, accessibility, interoperability, and reusability

How to approach getting your data ready for AI

Table of Contents

What counts as high-quality data?

You might have heard the phrase “garbage in, garbage out” in discussions about the effectiveness of AI algorithms. If trained on poor-quality data, AI tools will provide little meaningful insight, or worse – they may be biased and misleading when run on real-world data.

High-quality data doesn’t just mean it’s experimentally valid. Data generated by rigorous scientific research must also be clean, complete, and correctly labelled. Here’s a brief list of high-quality data considerations:

Accuracy: Simple enough, your data must be correct, consistent, and error-free.
Completeness: Your dataset shouldn’t have gaps or missing data points. Negative data should also be included to help inform the algorithm.
Relevance: To help the algorithm learn which relationships matter between variables, keep the datasets relevant to the key variables you’re monitoring.
Cleanliness: Data should be processed, i.e. normalized, duplications removed, and gaps and outliers accounted for.
Consistent annotation: Data and metadata must be correctly and consistently labelled so the algorithm can identify each variable (e.g. genes, small molecules, etc)
Volume: Quality trumps quantity, but AI models require significant amounts of training data to ensure accuracy.

Another key point for training data is that the datasets must be representative of real-world data and balanced to avoid skewing any potential biases.

Although timeliness, i.e. up-to-date data, is often cited as important for data quality, it doesn’t matter much for scientific research data. Most observational data is as true today as it was decades ago. Unless a fundamental shift in technology has changed the status quo and upended the thinking of previous methods, historical datasets are high-quality.

Common Stumbling Blocks Facing Researchers

Assuming you’ve generated robust datasets, preparing your data for AI is no walk in the park. Leveraging AI is a collaboration between disparate groups including researchers, programmers, and data scientists, so the data input must be meaningful to everyone involved to ensure success.

Common pain points in data management are often around its findability, accessibility, interoperability, and reusability. To help guide researchers in scientific data management, the Wilkinson lab published the FAIR Guidance Principles in 2016 to address these specific issues. Now, these principles guide large databases such as Genbank and UniProt, paving the way for their use in AI algorithms.

What is FAIR data?

Findability: Your data should be easy to locate, both for humans and machines. Proper naming conventions are key here.

Accessibility: Data should be easily accessible with minimal barriers while respecting security and regulatory requirements.

Interoperability: Standardizing datasets for integration with other datasets and reading by different systems is crucial. For example, the address format in a phonebook is interoperable with Google Maps, but unwritten local names for places typically aren’t.

Reusability: Ensure your data can be used in future research by providing clear documentation on how it was collected, processed, and annotated.

The real challenge comes in applying these standardization principles across all of the experiments and datasets generated between researchers, research groups, and from multiple lab instruments.

It may seem simple to say that agreed-upon naming conventions, supporting documentation for each experiment, and secure but accessible storage systems are all you need. Unfortunately, many labs have yet to embrace full digitalization and still operate with pen and paper lab books, sticky notes, USB drives, and computers. Experiments are named by the whims of the researcher performing it and the resulting data get stored and analyzed across multiple systems, accessible only to those with the password.

The result is that only data deemed worthy of publication see the light of day and future researchers can’t utilize the remaining valuable data generated because it lacks context.

By implementing FAIR principles, you naturally improve the quality of your datasets and enhance the accessibility of your data – crucial factors for successfully extracting insightful information from your research data using AI tools.

So, how do you make your data FAIR from the start?

Preparing for AI Begins with Data Management

Automating data management processes from capture to storage with specialized software is a good place to begin when considering AI tools further down the road. Software can remove many of the manual burdens associated with capturing data while helping you adhere to FAIR principles.

For example, electronic lab notebooks (ELNs), lab information management systems (LIMS), and software associated with specific instruments in your lab are already annotating your experiments and capturing data. Implementing a standardized naming convention within these systems will help make your data more findable across your organization.

Data accessibility is crucial for effective collaboration within teams or across interdisciplinary groups. A well-annotated, searchable database that consolidates relevant information fosters collaboration and streamlines research.

Interoperability is a common hurdle for inter-disciplinary collaboration and using AI tools. Correct annotation of data and metadata helps contextualize information for other users and systems. Consistency is key and international guidelines should be used for naming where possible. Unfortunately, annotation is, by and large, a time-consuming and manual process for historical data.

Reproducibility of data comes down to keeping a record of how data was collected, processed, and stored. Most ELN/LIMS systems will track experimental design, and instrument software should collect information on execution, however, these data must be collated and stored with the relevant documents in the database.

Software tools like ZONTAL can take this a step further, acting as an invisible layer between researcher and software, collecting, annotating, and storing data and metadata appropriately to comply with FAIR guidelines.

By implementing data management solutions for FAIR principles, you’ll be well on your way to leveraging AI for groundbreaking research in biology and chemistry.