{"doi":"10.1371/journal.pone.0294772","title":"Traditional Chinese Medicine studies for Alzheimer’s disease via network pharmacology based on entropy and random walk","abstract":"<jats:p>Alzheimer’s disease (AD) is a common neurodegenerative disease having complex pathogenesis, approved drugs can only alleviate symptoms of AD for a period of time. Traditional Chinese medicine (TCM) contains multiple active ingredients that can act on multiple targets simultaneously. In this paper, a novel algorithm based on entropy and random walk with the restart of heterogeneous network (RWRHE) is proposed for predicting active ingredients for AD and screening out the effective TCMs for AD. First, Six TCM compounds containing 20 herbs from the AD drug reviews in the CNKI (China National Knowledge Internet) are collected, their active ingredients and targets are retrieved from different databases. Then, comprehensive similarity networks of active ingredients and targets are constructed based on different aspects and entropy weight, respectively. A comprehensive heterogeneous network is constructed by integrating the known active ingredient-target association information and two comprehensive similarity networks. Subsequently, bi-random walks are applied on the heterogeneous network to predict active ingredient-target associations. AD related targets are selected as the seed nodes, a random walk is carried out on the target similarity network to predict the AD-target associations, and the associations of AD-active ingredients are inferred and scored. The effective herbs and compounds for AD are screened out based on their active ingredients’ scores. The results measured by machine learning and bioinformatics show that the RWRHE algorithm achieves better prediction accuracy, the top 15 active ingredients may act as multi-target agents in the prevention and treatment of AD, Danshen, Gouteng and Chaihu are recommended as effective TCMs for AD, Yiqitongyutang is recommended as effective compound for AD.</jats:p>","journal":"PLOS ONE","year":2023,"id":619719,"datarank":0.3958585994422889,"base_score":2.639057329615259,"endowment":2.639057329615259,"self_citation_contribution":0.3958585994422889,"citation_network_contribution":0.0,"self_endowment_contribution":0.3958585994422889,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1599469,"name":"Shujuan Cao","orcid":"0000-0002-0301-7604","position":1,"is_corresponding":false},{"id":160138,"name":"Yongming Zou","orcid":null,"position":2,"is_corresponding":false},{"id":78561,"name":"Fang‐Xiang Wu","orcid":"0000-0002-4593-9332","position":3,"is_corresponding":false},{"id":1599467,"name":"Xiaolu Wu","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Traditional Chinese Medicine studies for Alzheimer’s disease via network pharmacology based on entropy and random walk","abstract":"<jats:p>Alzheimer’s disease (AD) is a common neurodegenerative disease having complex pathogenesis, approved drugs can only alleviate symptoms of AD for a period of time. Traditional Chinese medicine (TCM) contains multiple active ingredients that can act on multiple targets simultaneously. In this paper, a novel algorithm based on entropy and random walk with the restart of heterogeneous network (RWRHE) is proposed for predicting active ingredients for AD and screening out the effective TCMs for AD. First, Six TCM compounds containing 20 herbs from the AD drug reviews in the CNKI (China National Knowledge Internet) are collected, their active ingredients and targets are retrieved from different databases. Then, comprehensive similarity networks of active ingredients and targets are constructed based on different aspects and entropy weight, respectively. A comprehensive heterogeneous network is constructed by integrating the known active ingredient-target association information and two comprehensive similarity networks. Subsequently, bi-random walks are applied on the heterogeneous network to predict active ingredient-target associations. AD related targets are selected as the seed nodes, a random walk is carried out on the target similarity network to predict the AD-target associations, and the associations of AD-active ingredients are inferred and scored. The effective herbs and compounds for AD are screened out based on their active ingredients’ scores. The results measured by machine learning and bioinformatics show that the RWRHE algorithm achieves better prediction accuracy, the top 15 active ingredients may act as multi-target agents in the prevention and treatment of AD, Danshen, Gouteng and Chaihu are recommended as effective TCMs for AD, Yiqitongyutang is recommended as effective compound for AD.</jats:p>","is_dataset_classified":null,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38019798","pmcid":null,"openalex_id":"https://openalex.org/W4389130353","authors":[],"funders":[{"funder_name":"The Science Fund of Tianjin Education Commission for Higher Education","grant_id":"2019KJ025","title":null}],"total_grants":1,"fwci":1.7125,"citation_percentile":0.86951562,"influential_citations":0,"citation_trend":[{"year":2024,"count":3},{"year":2025,"count":6},{"year":2026,"count":3}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294772&type=printable","host_type":"journal"},{"url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294772&type=printable","host_type":"publisher"},{"url":"https://dx.plos.org/10.1371/journal.pone.0294772","host_type":"publisher"},{"url":"https://doi.org/10.1371/journal.pone.0294772","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38019798","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10686466","host_type":"repository"},{"url":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0294772","host_type":"repository"},{"url":"https://doaj.org/article/f4dd6de9db62465d98ff01f8ef72db18","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10686466/pdf/pone.0294772.pdf","host_type":"repository"}],"fields_of_study":["Computational Drug Discovery Methods","Bioinformatics and Genomic Networks","Alzheimer's disease research and treatments","Humans","Medicine, Chinese Traditional","Alzheimer Disease","Entropy","Network Pharmacology","Neurodegenerative Diseases","Drugs, Chinese Herbal","Molecular Docking Simulation"],"mesh_terms":["Network Pharmacology","Alzheimer Disease","Drugs, Chinese Herbal","Humans","Medicine, Chinese Traditional","Entropy","Neurodegenerative Diseases","Molecular Docking Simulation"],"keywords":["Active ingredient","Random walk","Entropy (arrow of time)","Random forest","Traditional Chinese medicine","Computer science","Similarity (geometry)","Artificial intelligence","Chinese herbs","Medicine","Machine learning","Computational biology","Mathematics","Pharmacology","Biology","Statistics","Alternative medicine"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T08:12:45.734699Z","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":[]}