{"doi":"10.1101/2022.07.08.499317","title":"Asymmetric trichotomous data partitioning enables development of predictive machine learning models using limited siRNA efficacy datasets","abstract":"ABSTRACT Chemically modified small interfering RNAs (siRNAs) are promising therapeutics guiding sequence-specific silencing of disease genes. However, identifying chemically modified siRNA sequences that effectively silence target genes is a challenge. Such determinations necessitate computational algorithms. Machine Learning (ML) is a powerful predictive approach for tackling biological problems, but typically requires datasets significantly larger than most available siRNA datasets. Here, we describe a framework for applying ML to a small dataset (356 modified sequences) for siRNA efficacy prediction. To overcome noise and biological limitations in siRNA datasets, we apply a trichotomous (using two thresholds) partitioning approach, producing several combinations of classification threshold pairs. We then test the effects of different thresholds on random forest (RF) ML model performance using a novel evaluation metric accounting for class imbalances. We identify thresholds yielding a model with high predictive power outperforming a simple linear classification model generated from the same data. Using a novel method to extract model features, we observe target site base preferences consistent with current understanding of the siRNA-mediated silencing mechanism, with RF providing higher resolution than the linear model. This framework applies to any classification challenge involving small biological datasets, providing an opportunity to develop high-performing design algorithms for oligonucleotide therapies.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":309740,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9507,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":398202,"name":"Dmitry Korkin","orcid":"0000-0002-3875-9085","position":1,"is_corresponding":false},{"id":302206,"name":"Anastasia Khvorova","orcid":"0000-0001-6928-8071","position":2,"is_corresponding":false},{"id":884614,"name":"Kathryn Monopoli","orcid":"0000-0002-8615-4849","position":0,"is_corresponding":true}],"reference_count":41,"raw_metadata":null,"created_at":"2026-07-19T00:33:15.860993Z","pmid":null,"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":[]}