{"doi":"10.1101/2023.02.24.529941","title":"Strategies for effectively modelling promoter-driven gene expression using transfer learning","abstract":"The ability to deliver genetic cargo to human cells is enabling rapid progress in molecular medicine, but designing this cargo for precise expression in specific cell types is a major challenge. Expression is driven by regulatory DNA sequences within short synthetic promoters, but relatively few of these promoters are cell-type-specific. The ability to design cell-type-specific promoters using model-based optimization would be impactful for research and therapeutic applications. However, models of expression from short synthetic promoters (promoter-driven expression) are lacking for most cell types due to insufficient training data in those cell types. Although there are many large datasets of both endogenous expression and promoter-driven expression in other cell types, which provide information that could be used for transfer learning, transfer strategies remain largely unexplored for predicting promoter-driven expression. Here, we propose a variety of pretraining tasks, transfer strategies, and model architectures for modelling promoter-driven expression. To thoroughly evaluate various methods, we propose two benchmarks that reflect data-constrained and large dataset settings. In the data-constrained setting, we find that pretraining followed by transfer learning is highly effective, improving performance by 24-27%. In the large dataset setting, transfer learning leads to more modest gains, improving performance by up to 2%. We also propose the best architecture to model promoter-driven expression when training from scratch. The methods we identify are broadly applicable for modelling promoter-driven expression in understudied cell types, and our findings will guide the choice of models that are best suited to designing promoters for gene delivery applications using model-based optimization. Our code and data are available at https://github.com/anikethjr/promoter_models.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":390219,"datarank":0.38474240361923057,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"self_citation_contribution":0.38474240361923057,"citation_network_contribution":0.0,"self_endowment_contribution":0.38474240361923057,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9501,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":818238,"name":"Michael H. Herschl","orcid":"0000-0002-5666-8291","position":1,"is_corresponding":false},{"id":1161538,"name":"Xinyang Geng","orcid":"0009-0000-5500-6387","position":2,"is_corresponding":false},{"id":1162013,"name":"Sathvik Kolli","orcid":null,"position":3,"is_corresponding":false},{"id":1162014,"name":"Amy X. Lu","orcid":null,"position":4,"is_corresponding":false},{"id":1162015,"name":"Aviral Kumar","orcid":null,"position":5,"is_corresponding":false},{"id":235423,"name":"Patrick D. Hsu","orcid":"0000-0002-9380-2648","position":6,"is_corresponding":false},{"id":72208,"name":"Sergey Levine","orcid":"0000-0001-6764-2743","position":7,"is_corresponding":false},{"id":453684,"name":"Nilah M. Ioannidis","orcid":"0000-0001-9628-8229","position":8,"is_corresponding":false},{"id":1161537,"name":"Aniketh Janardhan Reddy","orcid":"0000-0002-9782-5361","position":0,"is_corresponding":true}],"reference_count":55,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:18:32.854511Z","pmid":"36909524","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}