{"id":"https://openalex.org/W4405765725","doi":"https://doi.org/10.48550/arxiv.2412.16276","title":"SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classification","display_name":"SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classification","publication_year":2024,"publication_date":"2024-12-20","ids":{"openalex":"https://openalex.org/W4405765725","doi":"https://doi.org/10.48550/arxiv.2412.16276"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2412.16276","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.16276","pdf_url":"https://arxiv.org/pdf/2412.16276","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2412.16276","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084672494","display_name":"Yingxu Wang","orcid":"https://orcid.org/0000-0003-0635-2747"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yingxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113770951","display_name":"Victor Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Victor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027852193","display_name":"Nan Yin","orcid":"https://orcid.org/0000-0002-0177-8514"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Nan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100781720","display_name":"Siwei Liu","orcid":"https://orcid.org/0000-0002-7326-2883"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Siwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Segal, Eran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Segal, Eran","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":true,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9398000240325928,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9398000240325928,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6318727135658264},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5232856869697571},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48874592781066895},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4818497896194458},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41488659381866455},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.24372610449790955}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6318727135658264},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5232856869697571},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48874592781066895},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4818497896194458},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41488659381866455},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24372610449790955}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2412.16276","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.16276","pdf_url":"https://arxiv.org/pdf/2412.16276","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2412.16276","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2412.16276","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2412.16276","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.16276","pdf_url":"https://arxiv.org/pdf/2412.16276","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4405765725.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Classifying":[0],"Antimicrobial":[1],"Peptides":[2],"(AMPs)":[3],"from":[4,11],"the":[5,37,71,75,82,99,126],"vast":[6],"collection":[7],"of":[8,78,102],"peptides":[9,103,163],"derived":[10],"metagenomic":[12],"sequencing":[13],"offers":[14],"a":[15,132],"promising":[16],"avenue":[17],"for":[18,42,181],"combating":[19],"antibiotic":[20],"resistance.":[21],"However,":[22],"most":[23],"existing":[24],"AMP":[25,129,194],"classification":[26],"methods":[27],"rely":[28],"primarily":[29],"on":[30,121,191],"sequence-based":[31],"representations":[32],"and":[33,66,81,104,117,145,164,171,186,195],"fail":[34],"to":[35,50,97,112,141,159,176,206],"capture":[36,113],"spatial":[38,118],"structural":[39,52,68,143],"information":[40],"critical":[41],"accurate":[43],"identification.":[44],"Although":[45],"recent":[46],"graph-based":[47],"approaches":[48],"attempt":[49],"incorporate":[51],"information,":[53],"they":[54],"typically":[55],"construct":[56,105],"residue-":[57],"or":[58],"atom-level":[59],"graphs":[60,107],"that":[61,135,199],"introduce":[62],"redundant":[63],"atomic":[64],"details":[65],"increase":[67],"complexity.":[69],"Furthermore,":[70],"class":[72,152],"imbalance":[73],"between":[74],"small":[76],"number":[77],"known":[79],"AMPs":[80],"abundant":[83],"non-AMPs":[84],"significantly":[85,201],"hinders":[86],"predictive":[87],"performance.":[88],"To":[89,150],"address":[90],"these":[91],"challenges,":[92],"we":[93,124],"employ":[94],"lightweight":[95],"OmegaFold":[96],"predict":[98],"three-dimensional":[100],"structures":[101],"peptide":[106],"using":[108],"C":[109],"\u03b1":[110],"atoms":[111],"their":[114],"backbone":[115],"geometry":[116],"topology.":[119],"Building":[120],"this":[122],"representation,":[123],"propose":[125],"Spatial":[127],"GNN-based":[128],"Classifier":[130],"(SGAC),":[131],"novel":[133],"framework":[134],"leverages":[136],"Graph":[137],"Neural":[138],"Networks":[139],"(GNNs)":[140],"extract":[142],"features":[144],"generate":[146,177],"discriminative":[147],"graph":[148],"representations.":[149],"handle":[151],"imbalance,":[153],"SGAC":[154,200],"incorporates":[155],"Weight-enhanced":[156,173],"Contrastive":[157],"Learning":[158],"cluster":[160],"structurally":[161],"similar":[162],"separate":[165],"dissimilar":[166],"ones":[167],"through":[168],"adaptive":[169],"weighting,":[170],"applies":[172],"Pseudo-label":[174],"Distillation":[175],"high-confidence":[178],"pseudo":[179],"labels":[180],"unlabeled":[182],"samples,":[183],"achieving":[184],"balanced":[185],"consistent":[187],"representation":[188],"learning.":[189],"Experiments":[190],"publicly":[192],"available":[193],"non-AMP":[196],"datasets":[197],"demonstrate":[198],"achieves":[202],"state-of-the-art":[203],"performance":[204],"compared":[205],"baselines.":[207]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2024-12-25T00:00:00"}