{"doi":"10.1093/bioadv/vbaf225","title":"tBN-CSDI: a time-varying blue noise-based diffusion model for time-series imputation","abstract":"Motivation: Missing data imputation remains a critical challenge in high-dimensional time-series data analysis, where traditional methods often struggle to capture complex nonlinear dependencies inherent in sequential data. Diffusion-based generative models have shown state-of-the-art performance by modeling the conditional distribution of missing values given observed data. However, these models typically rely on isotropic white noise during training, which can obscure important frequency-dependent correlations that are crucial for accurate imputation. Results: To address the limitations of conventional imputation methods, we propose a novel approach called time-varying blue noise-based conditional score-based diffusion model (tBN-CSDI). By modulating the noise schedule according to the frequency characteristics of the data, tBN-CSDI improves the recovery of subtle, high-frequency temporal patterns that are often overlooked by existing techniques. Experimental results on both healthcare and single-cell RNA-seq datasets show that tBN-CSDI consistently outperforms existing imputation methods, achieving over a 30% reduction in imputation error under high data sparsity. These findings underscore tBN-CSDI's potential as a robust and effective solution for imputing sparse and noisy time-series data. We further discuss its practical applications in improving change-point detection and gene regulatory network inference, demonstrating its broader utility in biomedical and biological research. Availability and implementation: The computer code and data for the proposed method are available on GitHub: https://github.com/gbishop345/tBN-CSDI.","journal":"Bioinformatics Advances","year":2024,"id":470314,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9497,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1248165,"name":"Tong Si","orcid":"0000-0002-0430-7283","position":1,"is_corresponding":false},{"id":1304784,"name":"Isabelle Luebbert","orcid":null,"position":2,"is_corresponding":false},{"id":259053,"name":"Noor Al‐Hammadi","orcid":"0000-0003-1742-6609","position":3,"is_corresponding":false},{"id":1135071,"name":"Haijun Gong","orcid":"0000-0003-1666-1674","position":4,"is_corresponding":false},{"id":1304783,"name":"Graham Bishop","orcid":null,"position":0,"is_corresponding":true}],"reference_count":13,"raw_metadata":null,"created_at":"2026-07-19T02:05:36.656771Z","pmid":"41127882","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":[]}