{"doi":"10.1093/bib/bbad030","title":"AD-Syn-Net: systematic identification of Alzheimer’s disease-associated mutation and co-mutation vulnerabilities via deep learning","abstract":"Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. To address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework ('AD-Syn-Net'), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors.","journal":"Briefings in Bioinformatics","year":2023,"id":374228,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9551,"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":319164,"name":"Zeynep H. Coban Akdemir","orcid":null,"position":1,"is_corresponding":false},{"id":261770,"name":"Ruixuan Gao","orcid":"0000-0001-7137-2512","position":2,"is_corresponding":false},{"id":66208,"name":"Xiaoqian Jiang","orcid":"0000-0001-9933-2205","position":3,"is_corresponding":false},{"id":106260,"name":"Gloria Sheynkman","orcid":"0000-0002-4223-9947","position":4,"is_corresponding":false},{"id":341320,"name":"Erxi Wu","orcid":"0000-0002-1680-3639","position":5,"is_corresponding":false},{"id":321218,"name":"Jason H. Huang","orcid":"0000-0002-4426-0168","position":6,"is_corresponding":false},{"id":56377,"name":"Nidhi Sahni","orcid":"0000-0002-9155-5882","position":7,"is_corresponding":false},{"id":56383,"name":"S. Stephen Yi","orcid":"0000-0003-0047-8103","position":8,"is_corresponding":false},{"id":875109,"name":"Xingxin Pan","orcid":"0000-0002-4429-4878","position":0,"is_corresponding":true}],"reference_count":119,"raw_metadata":null,"created_at":"2026-07-19T01:16:11.341687Z","pmid":"36752347","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":[]}