{"doi":"10.1109/icif.2003.177356","title":"Adaptive signal analysis of immunological data","abstract":"This paper aims to investigate whether both supervised and unsupervised signal analysis can contribute to the interpretation of immunological data. For this pur- pose a data base was set up containing cellular data from bmnchoalveolar lavage fluid which was obtained from 37 children with pulnionary diseases. The children were di- chotomized into two groups: 20 children suffered from chronic bronchitis whereas 17 children had an interstitial lung disease. A selforganizing map (SOM) and linear in- dependent component analysis were utilized to test higher- order correlations between cellular subsets and the patient gmups. Furthermore, a supervised approach with a per- ceptron trained to the patients' diagnosis was applied. The SOM confirmed rhe results that were expected from previ- ous statistical analyses. The results of the 1CA were rather weak, which liespresumably in the fact that a linear mixing model of independent sources does not hold; neverrheless, we couldfind parameters of high diagnosis influence that were confirmed by the perceptmn. The supervised percep- tron learning afterprincipal component analysis for dimen- sion reduction turned aut to be highly successful by linearly separating the patients into two groups with different diag- noses. The simplicity of the perceptmn made it easy to ex- tract diagnosis rules, which partly were known already and can now readily be tested on largerdata sets. In conclusion, neural network signal nnalysisprovidespmmising tools for rhe analysis of highly complex immunological data.","journal":"Sixth International Conference of Information Fusion, 2003. Proceedings of the","year":2003,"id":7371,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.2458,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2003-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":65851,"name":"D. Hard","orcid":null,"position":1,"is_corresponding":false},{"id":42,"name":"Fabian Joachim Theis","orcid":"0000-0002-2419-1943","position":5,"is_corresponding":false},{"id":23997,"name":"Susanne Krauss‐Etschmann","orcid":"0000-0001-5945-5702","position":6,"is_corresponding":false},{"id":61537,"name":"C.G. Puntoner","orcid":null,"position":7,"is_corresponding":false},{"id":380,"name":"Elmar W. Lang","orcid":"0000-0001-7440-0224","position":8,"is_corresponding":false}],"reference_count":18,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","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":[]}