{"doi":"10.34133/2022/9784273","title":"High-Content Screening and Analysis of Stem Cell-Derived Neural Interfaces Using a Combinatorial Nanotechnology and Machine Learning Approach","abstract":"A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-based therapies. Nevertheless, high-throughput investigation of the cell-type-specific biophysical cues associated with stem cell-derived neural interfaces continues to be a significant obstacle to overcome. To this end, we developed a combinatorial nanoarray-based method for high-throughput investigation of neural interface micro-/nanostructures (physical cues comprising geometrical, topographical, and mechanical aspects) and the effects of these complex physical cues on stem cell fate decisions. Furthermore, by applying a machine learning (ML)-based analytical approach to a large number of stem cell-derived neural interfaces, we comprehensively mapped stem cell adhesion, differentiation, and proliferation, which allowed for the cell-type-specific design of biomaterials for neural interfacing, including both adult and human-induced pluripotent stem cells (hiPSCs) with varying genetic backgrounds. In short, we successfully demonstrated how an innovative combinatorial nanoarray and ML-based platform technology can aid with the rational design of stem cell-derived neural interfaces, potentially facilitating precision, and personalized tissue engineering applications.","journal":"Research","year":2022,"id":262391,"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":14,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9512,"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":310091,"name":"Brian Conley","orcid":null,"position":1,"is_corresponding":false},{"id":817895,"name":"Jinho Yoon","orcid":"0000-0003-4614-9217","position":2,"is_corresponding":false},{"id":853034,"name":"Christopher Rathnam","orcid":"0000-0003-3111-4687","position":3,"is_corresponding":false},{"id":309013,"name":"Thanapat Pongkulapa","orcid":"0000-0003-0678-1736","position":4,"is_corresponding":false},{"id":869907,"name":"Brandon Conklin","orcid":"0000-0002-6834-3275","position":5,"is_corresponding":false},{"id":853036,"name":"Yannan Hou","orcid":"0000-0002-5499-6865","position":6,"is_corresponding":false},{"id":309016,"name":"Ki‐Bum Lee","orcid":"0000-0002-8164-0047","position":7,"is_corresponding":false},{"id":309011,"name":"Letao Yang","orcid":"0000-0002-0572-9787","position":0,"is_corresponding":true}],"reference_count":62,"raw_metadata":null,"created_at":"2026-07-19T00:26:20.717229Z","pmid":"36204248","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":[]}