{"doi":"10.1093/bib/bbac535","title":"VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants","abstract":"Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a 'one-stop' framework for the accurate identification of pathogenicity and functional impact of variants, we developed a two-stage deep-learning-based computational solution, termed VPatho, which was trained using a total of 9619 pathogenic GOF/LOF and 138 026 neutral variants curated from various databases. A total number of 138 variant-level, 262 protein-level and 103 genome-level features were extracted for constructing the models of VPatho. The development of VPatho consists of two stages: (i) a random under-sampling multi-scale residual neural network (ResNet) with a newly defined weighted-loss function (RUS-Wg-MSResNet) was proposed to predict variants' pathogenicity on the gnomAD_NV + GOF/LOF dataset; and (ii) an XGBOD model was constructed to predict the functional impact of the given variants. Benchmarking experiments demonstrated that RUS-Wg-MSResNet achieved the highest prediction performance with the weights calculated based on the ratios of neutral versus pathogenic variants. Independent tests showed that both RUS-Wg-MSResNet and XGBOD achieved outstanding performance. Moreover, assessed using variants from the CAGI6 competition, RUS-Wg-MSResNet achieved superior performance compared to state-of-the-art predictors. The fine-trained XGBOD models were further used to blind test the whole LOF data downloaded from gnomAD and accordingly, we identified 31 nonLOF variants that were previously labeled as LOF/uncertain variants. As an implementation of the developed approach, a webserver of VPatho is made publicly available at http://csbio.njust.edu.cn/bioinf/vpatho/ to facilitate community-wide efforts for profiling and prioritizing the query variants with respect to their pathogenicity and functional impact.","journal":"Briefings in Bioinformatics","year":2022,"id":250242,"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":27,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9548,"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":60427,"name":"Chen Li","orcid":"0000-0002-1847-754X","position":1,"is_corresponding":false},{"id":891840,"name":"Shahid Iqbal","orcid":"0000-0003-4321-8598","position":2,"is_corresponding":false},{"id":693675,"name":"Muhammad Arif","orcid":"0000-0003-3950-6618","position":3,"is_corresponding":false},{"id":258532,"name":"Fuyi Li","orcid":"0000-0001-5216-3213","position":4,"is_corresponding":false},{"id":891841,"name":"Maha A. Thafar","orcid":"0000-0003-0539-7361","position":5,"is_corresponding":false},{"id":891842,"name":"Zihao Yan","orcid":"0000-0002-8131-4663","position":6,"is_corresponding":false},{"id":117053,"name":"Apilak Worachartcheewan","orcid":"0000-0003-3021-3632","position":7,"is_corresponding":false},{"id":891843,"name":"Xiaofeng Xu","orcid":"0000-0002-3674-199X","position":8,"is_corresponding":false},{"id":258542,"name":"Jiangning Song","orcid":"0000-0001-8031-9086","position":9,"is_corresponding":false},{"id":626617,"name":"Dong‐Jun Yu","orcid":"0000-0002-6786-8053","position":10,"is_corresponding":false},{"id":693674,"name":"Fang Ge","orcid":"0000-0001-5792-5379","position":0,"is_corresponding":true}],"reference_count":66,"raw_metadata":null,"created_at":"2026-07-19T00:24:28.242323Z","pmid":"36528806","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":[]}