{"id":"https://openalex.org/W4401751419","doi":"https://doi.org/10.1109/isbi56570.2024.10635171","title":"Path-Gptomic: A Balanced Multi-Modal Learning Framework For Survival Outcome Prediction","display_name":"Path-Gptomic: A Balanced Multi-Modal Learning Framework For Survival Outcome Prediction","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4401751419","doi":"https://doi.org/10.1109/isbi56570.2024.10635171"},"language":"en","primary_location":{"id":"doi:10.1109/isbi56570.2024.10635171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi56570.2024.10635171","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","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/A5019362565","display_name":"Hongxiao Wang","orcid":"https://orcid.org/0000-0001-6948-7329"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongxiao Wang","raw_affiliation_strings":["Capital Normal University,Beijing,China,100048"],"affiliations":[{"raw_affiliation_string":"Capital Normal University,Beijing,China,100048","institution_ids":["https://openalex.org/I96852419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076291659","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0002-0576-9455"},"institutions":[{"id":"https://openalex.org/I127128434","display_name":"Genomics Institute of the Novartis Research Foundation","ror":"https://ror.org/017136v53","country_code":"US","type":"facility","lineage":["https://openalex.org/I127128434","https://openalex.org/I1283582996","https://openalex.org/I4210117619"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Yang","raw_affiliation_strings":["Novartis,San Diego,CA,USA,92121"],"affiliations":[{"raw_affiliation_string":"Novartis,San Diego,CA,USA,92121","institution_ids":["https://openalex.org/I127128434"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059912382","display_name":"Zhuo Zhao","orcid":"https://orcid.org/0000-0002-4449-2663"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuo Zhao","raw_affiliation_strings":["University of Notre Dame,Notre Dame,IN,USA,46556"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame,Notre Dame,IN,USA,46556","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102865632","display_name":"Pengfei Gu","orcid":"https://orcid.org/0000-0003-1160-9623"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pengfei Gu","raw_affiliation_strings":["University of Notre Dame,Notre Dame,IN,USA,46556"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame,Notre Dame,IN,USA,46556","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083092112","display_name":"Nishchal Sapkota","orcid":null},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nishchal Sapkota","raw_affiliation_strings":["University of Notre Dame,Notre Dame,IN,USA,46556"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame,Notre Dame,IN,USA,46556","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060901632","display_name":"Danny Z. Chen","orcid":"https://orcid.org/0000-0001-6565-2884"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danny Z. Chen","raw_affiliation_strings":["University of Notre Dame,Notre Dame,IN,USA,46556"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame,Notre Dame,IN,USA,46556","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5019362565"],"corresponding_institution_ids":["https://openalex.org/I96852419"],"apc_list":null,"apc_paid":null,"fwci":2.1822,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.89243456,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.4846000075340271,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.4846000075340271,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.7567633390426636},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.7545504570007324},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6537946462631226},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5564388632774353},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4033474326133728},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.15978789329528809},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15962302684783936}],"concepts":[{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.7567633390426636},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.7545504570007324},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6537946462631226},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5564388632774353},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4033474326133728},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.15978789329528809},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15962302684783936},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi56570.2024.10635171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi56570.2024.10635171","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2612519453","https://openalex.org/W2761668583","https://openalex.org/W2797883881","https://openalex.org/W2954499361","https://openalex.org/W2978426779","https://openalex.org/W2996092187","https://openalex.org/W3083699157","https://openalex.org/W3121523901","https://openalex.org/W3128336011","https://openalex.org/W3174004012","https://openalex.org/W3203898052","https://openalex.org/W3214280461","https://openalex.org/W3217335519","https://openalex.org/W4288419182","https://openalex.org/W4291021272","https://openalex.org/W4302012695","https://openalex.org/W4312639100","https://openalex.org/W4372260352","https://openalex.org/W4387211638","https://openalex.org/W4392168151","https://openalex.org/W6762913911","https://openalex.org/W6767213177","https://openalex.org/W6846232569"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2978999882","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278"],"abstract_inverted_index":{"For":[0],"predicting":[1],"cancer":[2,79],"survival":[3,80,132,168],"outcomes,":[4],"standard":[5],"approaches":[6],"in":[7],"clinical":[8],"research":[9],"are":[10,50,139,152],"often":[11,56],"based":[12],"on":[13,101,156],"two":[14,157],"main":[15],"modalities:":[16],"pathology":[17],"images":[18],"for":[19,29,64,78,108,131],"observing":[20],"cell":[21],"morphology":[22],"features,":[23],"and":[24,47,142],"genomic":[25],"(e.g.,":[26],"bulk":[27,109],"RNA-seq)":[28],"quantifying":[30],"gene":[31],"expressions.":[32],"However,":[33],"existing":[34],"pathology-genomic":[35],"multi-modal":[36,75],"algorithms":[37],"face":[38],"significant":[39],"challenges:":[40],"(1)":[41],"Valuable":[42],"biological":[43,87],"insights":[44],"regarding":[45],"genes":[46],"gene-gene":[48],"interactions":[49],"frequently":[51],"overlooked;":[52],"(2)":[53],"one":[54],"modality":[55],"dominates":[57],"the":[58,65,91,115,126,137,145],"optimization":[59],"process,":[60,147],"causing":[61],"inadequate":[62],"training":[63,146],"other":[66],"modality.":[67],"In":[68],"this":[69],"paper,":[70],"we":[71,89,118],"introduce":[72],"a":[73,95,120],"new":[74],"\"Path-GPTOmic\"":[76],"framework":[77],"outcome":[81],"prediction.":[82,133],"First,":[83],"to":[84,113,125],"extract":[85],"valuable":[86],"insights,":[88],"regulate":[90],"embedding":[92],"space":[93],"of":[94,136],"foundation":[96],"model,":[97],"scGPT,":[98],"initially":[99],"trained":[100],"single-cell":[102],"RNA-seq":[103,110],"data,":[104],"making":[105],"it":[106],"adaptable":[107],"data.":[111],"Second,":[112],"address":[114],"imbalance-between-modalities":[116],"problem,":[117],"propose":[119],"gradient":[121],"modulation":[122],"mechanism":[123],"tailored":[124],"Cox":[127],"partial":[128],"likelihood":[129],"loss":[130],"The":[134],"contributions":[135],"modalities":[138,151],"dynamically":[140],"monitored":[141],"adjusted":[143],"during":[144],"encouraging":[148],"that":[149],"both":[150],"sufficiently":[153],"trained.":[154],"Evaluated":[155],"TCGA(The":[158],"Cancer":[159],"Genome":[160],"Atlas)":[161],"datasets,":[162],"our":[163],"model":[164],"achieves":[165],"substantially":[166],"improved":[167],"prediction":[169],"accuracy.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
