{"id":"https://openalex.org/W7140298107","doi":"https://doi.org/10.48550/arxiv.2603.22369","title":"SynLeaF: A Dual-Stage Multimodal Fusion Framework for Synthetic Lethality Prediction Across Pan- and Single-Cancer Contexts","display_name":"SynLeaF: A Dual-Stage Multimodal Fusion Framework for Synthetic Lethality Prediction Across Pan- and Single-Cancer Contexts","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140298107","doi":"https://doi.org/10.48550/arxiv.2603.22369"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.22369","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22369","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.22369","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045732164","display_name":"Zheming Xing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing, Zheming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088856601","display_name":"Siyuan Zhou","orcid":"https://orcid.org/0000-0002-1276-4177"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Siyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130556745","display_name":"Ruinan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ruinan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130594487","display_name":"Rui Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100647260","display_name":"Weina Zhang","orcid":"https://orcid.org/0000-0003-0904-6008"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shiming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015170732","display_name":"Shiqu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Shiqu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100582949","display_name":"Yurui Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yurui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028421654","display_name":"Jiahao Ma","orcid":"https://orcid.org/0000-0002-5067-1756"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Jiahao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130594488","display_name":"Yifan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130603403","display_name":"Xuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130568915","display_name":"Yadong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yadong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100363218","display_name":"Junyi Li","orcid":"https://orcid.org/0000-0003-4878-2884"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Junyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.5084999799728394,"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":0.5084999799728394,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.0820000022649765,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11287","display_name":"Cancer Genomics and Diagnostics","score":0.04450000077486038,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"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/fusion-mechanism","display_name":"Fusion mechanism","score":0.5558000206947327},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.536300003528595},{"id":"https://openalex.org/keywords/synthetic-lethality","display_name":"Synthetic lethality","score":0.4043999910354614},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4016000032424927},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.3662000000476837},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.33059999346733093},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.32350000739097595},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.3125}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7189000248908997},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6169999837875366},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5972999930381775},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.5558000206947327},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.536300003528595},{"id":"https://openalex.org/C2778502085","wikidata":"https://www.wikidata.org/wiki/Q7662767","display_name":"Synthetic lethality","level":4,"score":0.4043999910354614},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4016000032424927},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.3662000000476837},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35600000619888306},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3125},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C63000827","wikidata":"https://www.wikidata.org/wiki/Q3080428","display_name":"Software portability","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2711000144481659}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.22369","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22369","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.22369","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22369","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,18,59,85],"of":[2,12,26,47,100,172,185],"synthetic":[3],"lethality":[4],"(SL)":[5],"is":[6,51,204],"important":[7],"for":[8,83],"guiding":[9],"the":[10,23,45,182,186],"development":[11],"cancer":[13,159],"drugs":[14],"and":[15,67,88,113,150,161,177,189,195],"therapies.":[16],"SL":[17,58,69,84],"faces":[19],"significant":[20],"challenges":[21],"in":[22,169],"effective":[24],"fusion":[25,81,188],"heterogeneous":[27],"multi-source":[28],"data.":[29],"Existing":[30],"multimodal":[31,80],"methods":[32],"often":[33],"suffer":[34],"from":[35,128],"\"modality":[36],"laziness\"":[37],"due":[38],"to":[39,103,123,192],"disparate":[40],"convergence":[41],"speeds,":[42],"which":[43],"hinders":[44],"exploitation":[46],"complementary":[48],"information.":[49],"This":[50],"also":[52],"one":[53],"reason":[54],"why":[55],"most":[56],"existing":[57],"models":[60],"cannot":[61],"perform":[62],"well":[63],"on":[64],"both":[65],"pan-cancer":[66],"single-cancer":[68,89],"pair":[70],"prediction.":[71],"In":[72,153],"this":[73],"study,":[74],"we":[75],"propose":[76],"SynLeaF,":[77],"a":[78,94,98,118,138,162,201],"dual-stage":[79,139],"framework":[82,92],"across":[86,156],"pan-":[87],"contexts.":[90],"The":[91],"employs":[93],"VAE-based":[95],"cross-encoder":[96],"with":[97,146],"product":[99],"experts":[101],"mechanism":[102,141],"fuse":[104],"four":[105],"omics":[106],"data":[107],"types":[108,160],"(gene":[109],"expression,":[110],"mutation,":[111],"methylation,":[112],"CNV),":[114],"while":[115],"simultaneously":[116],"utilizing":[117],"relational":[119],"graph":[120],"convolutional":[121],"network":[122],"capture":[124],"structured":[125],"gene":[126],"representations":[127],"biomedical":[129],"knowledge":[130,144],"graphs.":[131],"To":[132,197],"mitigate":[133],"modality":[134],"laziness,":[135],"SynLeaF":[136,165],"introduces":[137],"training":[140],"employing":[142],"featurelevel":[143],"distillation":[145,190],"adaptive":[147],"uni-modal":[148],"teacher":[149],"ensemble":[151],"strategies.":[152],"extensive":[154],"experiments":[155],"eight":[157],"specific":[158],"pancancer":[163],"dataset,":[164],"achieves":[166],"superior":[167],"performance":[168],"17":[170],"out":[171],"19":[173],"scenarios.":[174],"Ablation":[175],"studies":[176],"gradient":[178],"analyses":[179],"further":[180],"validate":[181],"critical":[183],"contributions":[184],"proposed":[187],"mechanisms":[191],"model":[193],"robustness":[194],"generalization.":[196],"facilitate":[198],"community":[199],"use,":[200],"web":[202],"server":[203],"available":[205],"at":[206],"https://synleaf.bioinformatics-lilab.cn.":[207]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-26T00:00:00"}
