Machine learning generates realistic genomes for imaginary humans | University of Tartu

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Contacts of UT units

Faculty of Arts and Humanities
Faculty phone: 
+372 737 5341
Faculty address: 
Jakobi 2, rooms 116–121, 51005 Tartu
  • Dean's Office
    Faculty phone: 
    + 372 737 5341
    Faculty address: 
    Jakobi 2, rooms 116–121, 51005 Tartu
  • Institute of History and Archaeology
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    +372 737 5651
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    Jakobi 2, 51005 Tartu
  • Institute of Estonian and General Linguistics
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    +372 737 5221
    Faculty address: 
    Jakobi 2, 51005 Tartu
  • Institute of Philosophy and Semiotics
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    +372 737 5314
    Faculty address: 
    Jakobi 2, rooms 309–352, 51005 Tartu
  • Institute of Cultural Research
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    Ülikooli 16, 51003 Tartu
  • School of Theology and Religious Studies
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    +372 737 5301
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    Ülikooli 18–310, 50090 Tartu
  • College of Foreign Languages and Cultures
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    J. Liivi 4, 50409, Tartu
  • Viljandi Culture Academy
    Faculty phone: 
    +372 435 5232
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    Posti 1, 71004 Viljandi
Faculty of Social Sciences
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+372 737 5957
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Lossi 36, 51003 Tartu
  • Dean's Office
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    Lossi 36, 51003 Tartu
  • Institute of Education
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  • Johan Skytte Institute of Political Studies
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  • School of Economics and Business Administration
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  • Institute of Biomedicine and Translational Medicine
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Faculty of Science and Technology
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+372 737 5820
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Vanemuise 46–208, 51014 Tartu
  • Dean's Office
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    Faculty address: 
    Vanemuise 46–208, 51003 Tartu
  • Institute of Computer Science
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  • Estonian Marine Institute
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  • Institute of Physics
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  • Institute of Chemistry
    Faculty phone: 
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  • Institute of Genomics
    Faculty phone: 
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    Riia 23b, 51010 Tartu
  • Institute of Mathematics and Statistics
    Faculty phone: 
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    Narva mnt 18, 51009 Tartu
  • Institute of Molecular and Cell Biology
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  • Tartu Observatory
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    Observatooriumi 1, Tõravere, 61602 Tartumaa
  • Institute of Technology
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    Nooruse 1, 50411 Tartu
  • Institute of Ecology and Earth Sciences
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    Vanemuise 46, 51003 Tartu
Institutions
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    W. Struve 1, 50091 Tartu
  • Youth Academy
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    Uppsala 10, 51003 Tartu
  • Museum
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    Lossi 25, 51003 Tartu
  • University of Tartu Natural History Museum and Botanical Garden
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    Vanemuise 46, 51003 Tartu
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  • University Office in Tallinn
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  • Tartu University Hospital
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Machine learning generates realistic genomes for imaginary humans

Figure: A generator machine shapes random noise while a discriminator machine tests the generated data against a database of available real data. Once the process is complete, the algorithm will generate artificial data that looks like the real one, but is actually completely new. Credit: Yelmen et al. 2021

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.

The pluridisciplinary team performed multiple analyses to assess the quality of the generated genomes compared to real ones. “Surprisingly, these genomes emerging from random noise mimic the complexities that we can observe within real human populations and, for most properties, they are not distinguishable from other genomes from the biobank we used to train our algorithm, except for one detail: they do not belong to any gene donor,” said Dr Luca Pagani, one of the senior authors of the study and a Mobilitas Pluss fellow.

The study additionally involves the assessment of the proximity of artificial genomes to real genomes to test whether the privacy of the original samples is preserved. “Although detecting privacy leaks among thousands of genomes could appear as looking for a needle in a haystack, combining multiple statistical measures allowed us to check all models carefully. Excitingly, the detailed exploration of complex leakage patterns can lead to improvements in generative model evaluation and design, and will fuel back the machine learning field,” said Dr Flora Jay, the coordinator of the study and CNRS researcher in the Interdisciplinary computer science laboratory (LRI/LISN, Université Paris-Saclay, French National Centre for Scientific Research). 

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.

Further information:
Burak Yelmen, first author of the study and Junior Research Fellow of Modern Population Genetics at the University of Tartu, burak.yelmen [ät] ut.ee
Luca Pagani, one of the senior authors of the study, Associate Professor of Population Genetics at the University of Tartu and a Mobilitas Pluss fellow, luca.pagani [ät] ut.ee
Flora Jay, the coordinator of the study and CNRS researcher at the Interdisciplinary computer science laboratory (LRI/LISN, Université Paris-Saclay, French National Centre for Scientific Research), flora.jay [ät] lri.fr

Sandra Sommer
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sandra.sommer [ät] ut.ee
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