{"id":"https://openalex.org/W7171535770","doi":"https://doi.org/10.1145/3807503.3820871","title":"Stable-Shift: Predicting Transcriptional Responses of Unseen Gene Perturbations Using Graph Neural Networks with Biological Priors","display_name":"Stable-Shift: Predicting Transcriptional Responses of Unseen Gene Perturbations Using Graph Neural Networks with Biological Priors","publication_year":2026,"publication_date":"2026-06-30","ids":{"openalex":"https://openalex.org/W7171535770","doi":"https://doi.org/10.1145/3807503.3820871"},"language":null,"primary_location":{"id":"doi:10.1145/3807503.3820871","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3807503.3820871","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3807503.3820871","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5099380961","display_name":"Sajib Acharjee Dip","orcid":"https://orcid.org/0009-0007-0959-2638"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sajib Acharjee Dip","raw_affiliation_strings":["Department of Computer Science, Virginia Tech, Blacksburg, VA, USA"],"raw_orcid":"https://orcid.org/0009-0007-0959-2638","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Virginia Tech, Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061868278","display_name":"Liqing Zhang","orcid":"https://orcid.org/0000-0003-4660-9199"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liqing Zhang","raw_affiliation_strings":["Department of Computer Science, Virginia Tech, Blacksburg, VA, USA; Fralin Biomedical Research Institute, Virginia Tech, Blacksburg, VA, USA and FBRI Cancer Research Center, Washington DC, USA"],"raw_orcid":"https://orcid.org/0000-0003-4660-9199","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Virginia Tech, Blacksburg, VA, USA; Fralin Biomedical Research Institute, Virginia Tech, Blacksburg, VA, USA and FBRI Cancer Research Center, Washington DC, USA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I859038795"],"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6305999755859375},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.512499988079071},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41530001163482666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3594000041484833},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.32589998841285706},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.30379998683929443}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6305999755859375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.576200008392334},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5429999828338623},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.30379998683929443},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2840999960899353},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.27869999408721924},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.2777999937534332},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.2565000057220459}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3807503.3820871","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3807503.3820871","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3807503.3820871","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3807503.3820871","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2315278455","display_name":"NRT-HDR: Convergence at the Interfaces of Policy, Data Science, Environmental Science and Engineering to Combat the Spread of Antibiotic Resistance","funder_award_id":"2125798","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7959702124","display_name":"Collaborative Research: TRTech-PGR Developing computational tools for single-cell genomic data analysis and annotation across diverse plant species.","funder_award_id":"2344169","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8262617916","display_name":"Collaborative Research: URoL:ASC: Using the Rules of Antibiotic Resistance Development to Inform Wastewater Mitigation Strategies","funder_award_id":"2319522","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1986050428","https://openalex.org/W2024580333","https://openalex.org/W2045022798","https://openalex.org/W2072478189","https://openalex.org/W2103017472","https://openalex.org/W2123775239","https://openalex.org/W2130240874","https://openalex.org/W2143512492","https://openalex.org/W2264017649","https://openalex.org/W2294333053","https://openalex.org/W2561754210","https://openalex.org/W2562947047","https://openalex.org/W2900569176","https://openalex.org/W2952391626","https://openalex.org/W2962756421","https://openalex.org/W2965552103","https://openalex.org/W2996140569","https://openalex.org/W3010525763","https://openalex.org/W3024013192","https://openalex.org/W3170265427","https://openalex.org/W3204394507","https://openalex.org/W4212836561","https://openalex.org/W4281716291","https://openalex.org/W4281777185","https://openalex.org/W4375955255","https://openalex.org/W4385955324","https://openalex.org/W4403931101"],"related_works":[],"abstract_inverted_index":{"Predicting":[0],"transcriptional":[1],"responses":[2],"to":[3,16,85,160],"genetic":[4],"perturbations":[5,51],"could":[6],"reduce":[7],"the":[8,79,90,114,135,154,165,168],"experimental":[9],"burden":[10],"of":[11,148,167],"functional":[12],"genomics,":[13],"but":[14],"extrapolation":[15],"genes":[17],"that":[18,60],"were":[19],"never":[20],"perturbed":[21],"during":[22],"training":[23,50],"remains":[24],"difficult.":[25],"We":[26],"present":[27,169],"Stable-Shift,":[28],"a":[29,45],"structured":[30,150],"method":[31],"for":[32,103],"estimating":[33],"unseen-gene":[34,123],"responses.":[35],"Stable-Shift":[36,95],"aggregates":[37],"single-cell":[38],"measurements":[39],"into":[40],"perturbation-level":[41],"expression":[42,73],"shifts,":[43],"fits":[44],"low-rank":[46],"response":[47],"basis":[48,61],"using":[49],"only,":[52],"and":[53,75,110,140,158],"predicts":[54],"an":[55],"unseen":[56],"gene\u2019s":[57],"coordinates":[58],"in":[59,134],"from":[62],"biological":[63],"context.":[64],"The":[65,129],"context":[66],"combines":[67],"STRING":[68],"interactions,":[69],"network":[70],"structure,":[71],"control-cell":[72],"statistics,":[74],"Gene":[76],"Ontology":[77],"annotations;":[78],"evaluated":[80,115],"implementation":[81],"uses":[82],"graph":[83,162],"convolution":[84],"integrate":[86],"these":[87],"inputs.":[88],"On":[89],"supplied":[91,136],"K562":[92],"Perturb-seq":[93],"benchmark,":[94],"obtained":[96],"0.592":[97],"cosine":[98,119],"similarity,":[99],"compared":[100],"with":[101,106],"0.569":[102],"GEARS,":[104],"together":[105],"higher":[107],"Spearman":[108],"correlation":[109],"top-gene":[111],"precision":[112],"among":[113],"methods.":[116],"Its":[117],"mean":[118],"similarity":[120],"over":[121],"five":[122],"splits":[124],"was":[125,132],"0.589":[126],"\u00b1":[127],"0.008.":[128],"same":[130],"ordering":[131],"observed":[133],"graph-aware,":[137],"residualized,":[138],"gene-space,":[139],"Norman-dataset":[141],"comparisons.":[142],"These":[143],"results":[144],"support":[145],"further":[146],"study":[147],"biologically":[149],"latent-response":[151],"prediction,":[152],"while":[153],"lower":[155],"gene-space":[156],"accuracy":[157],"sensitivity":[159],"sparse":[161],"neighborhoods":[163],"limit":[164],"scope":[166],"conclusions.":[170]},"counts_by_year":[],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2026-07-29T00:00:00"}
