Machine Learning & AI for Science

Using your data to build models is not the same as asking an AI chatbot for an answer

AI is increasingly used as a broad term, but not all AI-based analysis is the same.

The machine learning offered by NextGenSeek is not simply uploading your dataset to ChatGPT, Claude or another generative AI tool and asking it to analyse the results. Instead, we use established machine-learning methods to build and test models directly from your scientific data.

These models can be used to identify patterns, predict experimental or biological outcomes, classify samples, discover groups within a dataset, and investigate which variables contribute to those predictions.

Just as importantly, the model needs to be evaluated properly. Training and test data, cross-validation, class imbalance, overfitting, data leakage and the structure of the experiment all need to be considered before a result can be trusted.

When can machine learning help?

Biological datasets often contain relationships that are difficult to identify by looking at one variable at a time. Machine learning can be useful when many measurements may contribute to an outcome, when relationships are not straightforward, or when we want to explore structure in the data without specifying what we expect to find beforehand.

Depending on the research question, this can mean asking:

Can we predict an experimental or biological outcome?

Which measurements are most informative?

Can samples be separated into biologically meaningful groups?

How early in an experiment can an outcome be predicted?

Can a combination of measurements predict something that none of them can predict alone?