Publications

Pierre-Aurélien Gilliot, Thomas E Gorochowski. Come with the Sequence, Stay for the Structure: RNA Conformational Learning to Predict Protein Expression, 2023 ICML Workshop on Computational Biology. [pdf]
Pierre-Aurélien Gilliot, Thomas E Gorochowski. Transfer learning for cross-context prediction of protein expression from 5’UTR sequence, In Review. [pdf]
Pierre-Aurélien Gilliot, Thomas E Gorochowski. Effective design and inference for cell sorting and sequencing based massively parallel reporter assays, Bioinformatics, Volume 39, Issue 5, May 2023. [paper][pdf]
Pierre-Aurélien Gilliot, Thomas E Gorochowski. Design and Analysis of Massively Parallel Reporter Assays Using FORECAST. In: Selvarajoo, K. (eds) Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology. Methods in MolecularBiology, vol 2553. (2023). [paper][pdf]
Pierre-Aurélien Gilliot, Thomas E Gorochowski. Sequencing enabling design and learning in synthetic biology. Current Opinion in Chemical Biology, 58, 54–62 (2020). [paper][pdf]

Conference and Talks

Generative Modelling Reading Group, November 2023: Introduction to diffusion models. [slides]
PhD Viva, September 2023: Data centric approaches to genetic part design. [slides]
2023 ICML Workshop on Computational Biology, Honolulu: Come with the Sequence, Stay for the Structure: RNA Conformational Learning to Predict Protein Expression. [pdf][poster]
2023 Turing workshop on AI, Engineering Biology & Beyond, Edinburgh. Transfer learning for the design of genetic parts. [slides]
2022 5th Annual Machine Learning and AI In (Bio)Chemical Engineering Conference, Cambridge. Systemic comparison of neural network architectures for protein expression prediction in bacteria. [slides]
2021 SynbioUK, Nottingham. Optimal design and effective inference for Flow-Seq experiments. [poster]
2021 RECOMB, Padova (online). Optimal design and effective inference for Flow-Seq experiments. [poster]