{"doi":"10.2174/1381612824666181106102111","title":"Detecting Personalized Determinants During Drug Treatment from Omics Big Data","abstract":"<jats:sec>\n<jats:title />\n<jats:p>Backgrounds: Targeted therapy is the foundation of personalized medicine in cancer, which is often\nunderstood as the right patient using the right drug. Thinking from the viewpoint of determinants during personalized\ndrug treatment, the genetics, epigenetics and metagenomics would provide individual-specific biological\nelements to characterize the personalized responses for therapy.\n</jats:p></jats:sec>\n<jats:sec>\n<jats:title>Methods: </jats:title>\n<jats:p>Such personalized determinants should be not only understood as specific to one person, while they\nshould have certain replicate observations in a group of individuals but not all, which actually provide more\ncredible and reproducible personalized biological features. The requirement of detecting personalized\ndeterminants is well supported by novel high-throughput sequencing technologies and newly temporal-spatial\nexperimental protocols, which quickly produce the omics big data.\n</jats:p></jats:sec>\n<jats:sec>\n<jats:title>Results:</jats:title>\n<jats:p> In this mini-review, we would like to give a brief introduction firstly on the advanced drug or drug-like\ntherapy with genetics, epigenetics and metagenomics, respectively, from the viewpoint of personalized\ndeterminants; then summarize the computational methods helpful to analyze the corresponding omics data under\nthe consideration of personalized biological context; and particularly focus on metagenomics to discuss current\ndata, method, and opportunity for personalized medicine.\n</jats:p></jats:sec>\n<jats:sec>\n<jats:title>Conclusion:</jats:title>\n<jats:p> Totally, detecting personalized determinants during drug treatment from omics big data will bring\nthe precision medicine or personalized medicine from concept to application. More and more inspiring biotechnologies,\ndata resources, and analytic approaches will benefit All of US in the near future.</jats:p>\n</jats:sec>","journal":"Current Pharmaceutical Design","year":2019,"id":13518,"datarank":0.3992731318647802,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"self_citation_contribution":0.3596842909197557,"citation_network_contribution":0.03958884094502454,"self_endowment_contribution":0.3596842909197557,"citer_contribution":0.03958884094502454,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"citer_count":2,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":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":110669,"name":"Xiangtian Yu","orcid":null,"position":1,"is_corresponding":false},{"id":110670,"name":"Chengming Zhang","orcid":null,"position":2,"is_corresponding":false},{"id":110671,"name":"Tao Zeng","orcid":null,"position":3,"is_corresponding":false},{"id":4896,"name":"Lu Wang","orcid":"0000-0002-0206-1830","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"30398110","pmcid":null,"openalex_id":"https://openalex.org/W2899657781","authors":[],"funders":[],"total_grants":0,"fwci":0.8348,"citation_percentile":0.73079956,"influential_citations":0,"citation_trend":[{"year":2019,"count":5},{"year":2020,"count":2},{"year":2021,"count":2},{"year":2023,"count":1}],"oa_status":"closed","license":null,"oa_locations":[{"url":"https://eurekaselect.com/article/download/167065","host_type":"publisher"},{"url":"https://www.eurekaselect.com/167065/article","host_type":"publisher"},{"url":"https://doi.org/10.2174/1381612824666181106102111","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/30398110","host_type":"repository"}],"fields_of_study":["Gene expression and cancer classification","Epigenetics and DNA Methylation","Cancer Genomics and Diagnostics","Biology","Medicine","Antineoplastic Agents","Big Data","Humans","Neoplasms","Precision Medicine"],"mesh_terms":["Big Data","Antineoplastic Agents","Humans","Neoplasms","Precision Medicine"],"keywords":["Personalized medicine","Precision medicine","Metagenomics","Omics","Big data","Context (archaeology)","Data science","Drug response","Genomics","Computer science","Computational biology","Bioinformatics","Medicine","Drug","Biology","Data mining","Genome","Pharmacology","Genetics","Biomarker","Drug treatment","Precision Medicine.","Personalized Determinants"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-05-30T19:26:01.214310Z","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":[]}