{"doi":"10.1101/2023.09.12.557189","title":"omicsMIC: a Comprehensive Benchmarking Platform for Robust Comparison of Imputation Methods in Mass Spectrometry-based Omics Data","abstract":"Abstract Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics, and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive and systematic comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometrybased omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to simulate and evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. OmicsMIC is freely available at https://github.com/WQLin8/omicsMIC .","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":411460,"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":0.8903,"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":12368,"name":"Jiadong Ji","orcid":"0000-0003-3562-8861","position":1,"is_corresponding":false},{"id":293495,"name":"Kuan‐Jui Su","orcid":null,"position":2,"is_corresponding":false},{"id":320620,"name":"Chuan Qiu","orcid":"0000-0001-6202-9229","position":3,"is_corresponding":false},{"id":320626,"name":"Qing Tian","orcid":"0000-0003-3830-6606","position":4,"is_corresponding":false},{"id":291583,"name":"Lan‐Juan Zhao","orcid":"0000-0001-6342-2495","position":5,"is_corresponding":false},{"id":449233,"name":"Zhe Luo","orcid":"0000-0003-1384-1565","position":6,"is_corresponding":false},{"id":268248,"name":"Hui Shen","orcid":"0000-0003-0335-6064","position":7,"is_corresponding":false},{"id":240067,"name":"Chong Wu","orcid":"0000-0002-8400-1785","position":8,"is_corresponding":false},{"id":85487,"name":"Hong‐Wen Deng","orcid":"0000-0002-0387-8818","position":9,"is_corresponding":false},{"id":1191892,"name":"Weiqiang Lin","orcid":"0000-0002-5484-0860","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:21:42.971455Z","pmid":"37745599","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":[]}