Peptide drug development with symbolic regression
An example of peptide drug featurization and modelling using anticancer peptides.
Evaluating the calibration of your QLattice models
Let’s study how well calibrated the QLattice models are, and to what extent calibrators can improve them.
An introduction to calibration (part I): Understanding the basics
What do machine learning model outputs represent? What if the predictions that we’re making come with a future risk?
An introduction to calibration (part II): Platt scaling, isotonic regression, and beta calibration
Calibrators are tools used to transform the scores generated by your models into (almost) real mathematical probabilities.
Multi-omics analysis made easy: A breast cancer example
The QLattice, a new explainable AI algorithm, can cut through the noise of omics data sets and point to the most relevant inputs and models.
siRNA molecule activity: explainable AI models uncover underlying mechanisms
Our results-driven world pressures us to give up good science.
How I Learned to Stop Encoding and Love the QLattice
Curing your data preprocessing blues: Automatic handling of categorical data and scaling
Black-box medicine is taking over. Here’s an alternative.
The increasing application of black-box models gives rise to a range of both ethical and scientific questions.
AI and nucleic acid-based medicine: can symbolic regression unite them?
How the QLattice explains the cellular toxicity of RNA-targeting drugs.
The QLattice: A new machine learning model you didn’t know you needed
Data sciencey-sphere, I have big news. A radical new machine learning model has surfaced.
