{"id":"https://openalex.org/W7126109831","doi":"https://doi.org/10.1109/bibm66473.2025.11356291","title":"KANCurvNet: A KAN-Based Feature Fusion Network for Multi-Omics Survival Prediction","display_name":"KANCurvNet: A KAN-Based Feature Fusion Network for Multi-Omics Survival Prediction","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126109831","doi":"https://doi.org/10.1109/bibm66473.2025.11356291"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356291","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356291","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124174837","display_name":"Baisheng Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Baisheng Zhou","raw_affiliation_strings":["College of Artificial Intelligence, Taiyuan University Of Technology,Taiyuan,China"],"affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Taiyuan University Of Technology,Taiyuan,China","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079363773","display_name":"D. Li","orcid":null},"institutions":[{"id":"https://openalex.org/I46305995","display_name":"Taiyuan University of Science and Technology","ror":"https://ror.org/01wcbdc92","country_code":"CN","type":"education","lineage":["https://openalex.org/I46305995"]},{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongxi Li","raw_affiliation_strings":["College of Computer Science and Technology, Taiyuan University Of Technology,Taiyuan,China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Taiyuan University Of Technology,Taiyuan,China","institution_ids":["https://openalex.org/I46305995","https://openalex.org/I9086337"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5124174837"],"corresponding_institution_ids":["https://openalex.org/I9086337"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.67288393,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1990","last_page":"1993"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.49559998512268066,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.49559998512268066,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11297","display_name":"Ferroptosis and cancer prognosis","score":0.2370000034570694,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10261","display_name":"Genetic Associations and Epidemiology","score":0.03669999912381172,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.805400013923645},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5677000284194946},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5532000064849854},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4564000070095062},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41690000891685486},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.40880000591278076},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.40689998865127563},{"id":"https://openalex.org/keywords/bilinear-interpolation","display_name":"Bilinear interpolation","score":0.37940001487731934},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.37459999322891235}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.805400013923645},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6726999878883362},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6197999715805054},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5677000284194946},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5532000064849854},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4564000070095062},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4397999942302704},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.40880000591278076},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38679999113082886},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.37459999322891235},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C3019722297","wikidata":"https://www.wikidata.org/wiki/Q4440864","display_name":"High dimensional","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.31700000166893005},{"id":"https://openalex.org/C10515644","wikidata":"https://www.wikidata.org/wiki/Q543310","display_name":"Survival analysis","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.2976999878883362},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C195065555","wikidata":"https://www.wikidata.org/wiki/Q214881","display_name":"Curvature","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356291","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356291","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2001939488","https://openalex.org/W2087667141","https://openalex.org/W2095463689","https://openalex.org/W2110256992","https://openalex.org/W2114843025","https://openalex.org/W2120579447","https://openalex.org/W2149441684","https://openalex.org/W2157076315","https://openalex.org/W2797883881","https://openalex.org/W2964015378","https://openalex.org/W3024899322","https://openalex.org/W3163443268","https://openalex.org/W4380154190"],"related_works":[],"abstract_inverted_index":{"Cancer":[0],"presents":[1],"a":[2,30,61,68,121],"complex":[3],"global":[4],"health":[5],"challenge,":[6],"with":[7,82],"multi-omics":[8,37,117],"data":[9,23],"offering":[10],"unprecedented":[11],"insights":[12],"into":[13],"tumorigenesis.":[14],"However,":[15],"the":[16],"high":[17],"dimensionality":[18,66],"and":[19,47,54,77,119],"heterogeneity":[20,118],"of":[21],"these":[22],"demand":[24],"advanced":[25],"computational":[26],"approaches.":[27],"We":[28],"propose":[29],"novel":[31,122],"KAN-based":[32],"feature":[33],"fusion":[34],"network":[35],"for":[36,65,74,124],"survival":[38,97],"analysis":[39],"framework":[40],"KANCurvNet,":[41],"which":[42],"integrates":[43],"genomic,":[44],"transcriptomic,":[45],"epigenomic,":[46],"proteomic":[48],"profiles":[49],"while":[50,110],"leveraging":[51],"geometric":[52],"curvature":[53],"patient-sample":[55],"neighborhood":[56],"relationships.":[57],"Our":[58],"method":[59],"employs":[60],"variational":[62],"autoencoder":[63],"(VAE)":[64],"reduction,":[67],"multi-scale":[69],"factorized":[70],"bilinear":[71],"model":[72,94],"(MC-FBM)":[73],"cross-omics":[75],"representation,":[76],"graph":[78],"convolutional":[79],"networks":[80],"(GCN)":[81],"Kolmogorov-Arnold":[83],"Networks":[84],"(KAN)":[85],"to":[86],"capture":[87],"high-order":[88],"patterns.":[89],"A":[90],"Cox":[91],"proportional":[92],"hazards":[93],"then":[95],"predicts":[96],"risk.":[98],"Experiments":[99],"across":[100],"multiple":[101],"cancer":[102,128],"cohorts":[103],"demonstrate":[104],"superior":[105],"performance":[106],"over":[107],"traditional":[108],"methods":[109],"maintaining":[111],"interpretability.":[112],"This":[113],"approach":[114],"effectively":[115],"addresses":[116],"introduces":[120],"paradigm":[123],"deep":[125],"learning":[126],"in":[127],"research.":[129]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}
