Machine learning generates realistic genomes for imaginary humans

Machines, thanks to novel algorithms and advances in computer technology, can now learn complex models and even generate high-quality synthetic data such as photo-realistic images or even resumes of imaginary humans.

A study recently published in the international journal PLOS Genetics uses machine learning to mine existing biobanks and generate chunks of human genomes which do not belong to real humans but have the characteristics of real genomes.

"Existing genomic databases are an invaluable resource for biomedical research, but they are either not publicly accessible or shielded behind long and exhausting application procedures due to valid ethical concerns. This creates a major scientific barrier for researchers. Machine-generated genomes, or artificial genomes as we call them, can help us overcome the issue within a safe ethical framework," said Burak Yelmen, first author of the study and Junior Research Fellow of Modern Population Genetics at the University of Tartu.

All in all, machine learning approaches had provided faces, biographies and multiple other features to a handful of imaginary humans: now we know more about their biology. These imaginary humans with realistic genomes could serve as proxies for all the real genomes which are not publicly available or require long application procedures or collaborations, hence removing an important accessibility barrier in genomic research, in particular for underrepresented populations.