@inproceedings{
ghari2024gflownet,
title={{GF}lowNet Assisted Biological Sequence Editing},
author={Pouya M. Ghari and Alex M Tseng and G{\"o}kcen Eraslan and Romain Lopez and Tommaso Biancalani and Gabriele Scalia and Ehsan Hajiramezanali},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g0G8DQSBcj}
}
Editing biological sequences has extensive applications in synthetic biology and medicine, such as designing regulatory elements for nucleic-acid therapeutics and treating genetic disorders. The primary objective in biological-sequence editing is to determine the optimal modifications to a sequence which augment certain biological properties while adhering to a minimal number of alterations to ensure predictability and potentially support safety. In this paper, we propose GFNSeqEditor, a novel biological-sequence editing algorithm which builds on the recently proposed area of generative flow networks (GFlowNets). Our proposed GFNSeqEditor identifies elements within a starting seed sequence that may compromise a desired biological property. Then, using a learned stochastic policy, the algorithm makes edits at these identified locations, offering diverse modifications for each sequence to enhance the desired property. The number of edits can be regulated through specific hyperparameters. We conducted extensive experiments on a range of real-world datasets and biological applications, and our results underscore the superior performance of our proposed algorithm compared to existing state-of-the-art sequence editing methods.
GFlowNet Assisted Biological Sequence Editing
Pouya M. Ghari Alex M Tseng Gökcen Eraslan Romain Lopez Tommaso Biancalani Gabriele Scalia Ehsan Hajiramezanali