Cancer-inspired genomics mapper model for the generation of synthetic DNA sequences with desired genomics signatures

Teddy Lazebnik, Liron Simon-Keren

Research output: Contribution to journalArticlepeer-review


Genome data are crucial in modern medicine, offering significant potential for diagnosis and treatment. Thanks to technological advancements, many millions of healthy and diseased genomes have already been sequenced; however, obtaining the most suitable data for a specific study, and specifically for validation studies, remains challenging with respect to scale and access. Therefore, in silico genomics sequence generators have been proposed as a possible solution. However, the current generators produce inferior data using mostly shallow (stochastic) connections, detected with limited computational complexity in the training data. This means they do not take the appropriate biological relations and constraints, that originally caused the observed connections, into consideration. To address this issue, we propose cancer-inspired genomics mapper model (CGMM), that combines genetic algorithm (GA) and deep learning (DL) methods to tackle this challenge. CGMM mimics processes that generate genetic variations and mutations to transform readily available control genomes into genomes with the desired phenotypes. We demonstrate that CGMM can generate synthetic genomes of selected phenotypes such as ancestry and cancer that are indistinguishable from real genomes of such phenotypes, based on unsupervised clustering. Our results show that CGMM outperforms four current state-of-the-art genomics generators on two different tasks, suggesting that CGMM will be suitable for a wide range of purposes in genomic medicine, especially for much-needed validation studies.

Original languageEnglish
Article number107221
JournalComputers in Biology and Medicine
Early online date10 Jul 2023
StatePublished - Sep 2023
Externally publishedYes


  • Bioinformatics deep learning model
  • Biomarker detection
  • In silico genome generation
  • Validation

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Computer Science Applications


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