{"id":"https://openalex.org/W7214034784","doi":"https://doi.org/10.5281/zenodo.22893849","title":"Pre-trained Deep Learning Models for Detecting Topological Threadings in Ring Polymers","display_name":"Pre-trained Deep Learning Models for Detecting Topological Threadings in Ring Polymers","publication_year":2026,"publication_date":"2026-09-22","ids":{"openalex":"https://openalex.org/W7214034784","doi":"https://doi.org/10.5281/zenodo.22893849"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.22893849","doi":"https://doi.org/10.5281/zenodo.22893849","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22893849","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Software"},"volume":null,"type":"software","is_traditional":false,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.22893849","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091998696","display_name":"Filippo Conforto","orcid":"https://orcid.org/0009-0006-2859-1131","observed_orcids":["https://orcid.org/0009-0006-2859-1131"]},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Conforto, Filippo","raw_affiliation_strings":["University of Edinburgh"],"raw_orcid":"https://orcid.org/0009-0006-2859-1131","affiliations":[{"raw_affiliation_string":"University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011851622","display_name":"Roman Sta\u0148o","orcid":"https://orcid.org/0000-0001-6779-3680","observed_orcids":["https://orcid.org/0000-0001-6779-3680"]},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sta\u0148o, Roman","raw_affiliation_strings":["University of Cambridge"],"raw_orcid":"https://orcid.org/0000-0001-6779-3680","affiliations":[{"raw_affiliation_string":"University of Cambridge","institution_ids":["https://openalex.org/I241749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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/T12536","display_name":"Topological and Geometric Data Analysis","score":0.2524000108242035,"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"}},"topics":[{"id":"https://openalex.org/T12536","display_name":"Topological and Geometric Data Analysis","score":0.2524000108242035,"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/T10304","display_name":"Geometric and Algebraic Topology","score":0.1581999957561493,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.13670000433921814,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ring-polymers","display_name":"ring polymers","score":0.8840000033378601},{"id":"https://openalex.org/keywords/polymer-melts","display_name":"polymer melts","score":0.6470000147819519},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"recurrent neural networks","score":0.7250000238418579},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8930000066757202},{"id":"https://openalex.org/keywords/pre-trained-models","display_name":"pre-trained models","score":0.49399998784065247}],"concepts":[{"id":"https://openalex.org/C78555687","wikidata":"https://www.wikidata.org/wiki/Q778947","display_name":"Writhe","level":3,"score":0.8809000253677368},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5708000063896179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5428000092506409},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5184000134468079},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.4602000117301941},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3935999870300293},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3747999966144562},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3682999908924103},{"id":"https://openalex.org/C190694206","wikidata":"https://www.wikidata.org/wiki/Q3276654","display_name":"Polygon (computer graphics)","level":3,"score":0.3628999888896942},{"id":"https://openalex.org/C2779863119","wikidata":"https://www.wikidata.org/wiki/Q6423048","display_name":"Knot (papermaking)","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3465000092983246},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.3416999876499176},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.33550000190734863},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3294999897480011},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.3192000091075897}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.22893849","doi":"https://doi.org/10.5281/zenodo.22893849","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22893849","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Software"},{"id":"doi:10.5281/zenodo.22893850","doi":"https://doi.org/10.5281/zenodo.22893850","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22893850","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Software"}],"best_oa_location":{"id":"doi:10.5281/zenodo.22893849","doi":"https://doi.org/10.5281/zenodo.22893849","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22893849","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Software"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7216356252","https://openalex.org/W3005786300","https://openalex.org/W4394854922","https://openalex.org/W7020334751","https://openalex.org/W7170797462","https://openalex.org/W6967529726","https://openalex.org/W3082584545","https://openalex.org/W6894296501","https://openalex.org/W7147080723","https://openalex.org/W2910296587"],"abstract_inverted_index":{"This":[0],"repository":[1],"contains":[2,34],"pre-trained":[3,36],"deep":[4],"neural":[5,37],"network":[6,38,133],"models":[7,179],"and":[8,28,70,180,186],"an":[9],"inference":[10],"pipeline":[11],"supporting":[12],"the":[13,136,151],"manuscript:\"Learning":[14],"to":[15,74,83,177],"Detect":[16],"Threadings":[17],"in":[18,40],"Melts":[19],"of":[20,150],"Rings\"":[21],"(Filippo":[22],"Conforto,":[23],"Roman":[24],"Stano,":[25],"Jan":[26],"Smrek,":[27],"Davide":[29],"Michieletto).":[30],"INCLUDED":[31],"MODELS:The":[32],"archive":[33],"four":[35],"architectures":[39],"TensorFlow":[41],"/":[42],"Keras":[43],"SavedModel":[44,166],"format:":[45],"1.":[46],"CNN1D_StA":[47],"(models/NN_model_best_SIGWRITHE10_Norm_False_Net_CNN1D_Threading)-":[48],"Input:":[49,94,116,145],"1D":[50,95,110],"non-normalised":[51,96,118],"Segment-to-All":[52,97],"(StA)":[53,98],"mutual":[54,121,139],"writhe":[55,99,111,122,140],"profile":[56,100],"(shape:":[57,101,125,155],"[batch,":[58,102,126,156],"200,":[59,103,157],"1]).-":[60,104,129],"Performance:":[61],"Primary":[62],"model.":[63,163],"Achieves":[64],">98%":[65],"accuracy":[66],"on":[67,109,135],"test":[68],"pairs":[69],"generalises":[71],"without":[72],"retraining":[73],"dense":[75],"melts":[76],"across":[77],"chain":[78],"lengths":[79],"N":[80],"=":[81],"100":[82],"1600":[84],"(TPR":[85],">":[86],"91%,":[87],"FPR":[88],"approx":[89],"0%).":[90],"2.":[91],"RNN_StA":[92],"(models/NN_model_best_SIGWRITHE10_Norm_False_Net_RNN_Threading)-":[93],"Role:":[105,130,159],"Recurrent":[106],"sequential":[107],"classifier":[108],"representations.":[112],"3.":[113],"CNN2D_StS":[114],"(models/NN_model_best_SIGWRITHEMATRIX10_Norm_False_Net_CNN2D_Threading)-":[115],"2D":[117,131],"Segment-to-Segment":[119],"(StS)":[120],"density":[123],"matrix":[124],"100,":[127,128],"convolutional":[132],"operating":[134],"full":[137],"pairwise":[138],"field.":[141],"4.":[142],"RNN_XYZ":[143],"(models/NN_model_best_XYZ_Norm_True_Net_RNN_Threading)-":[144],"Normalised":[146],"3D":[147],"Cartesian":[148],"coordinates":[149],"two":[152],"ring":[153],"polymers":[154],"3]).-":[158],"Baseline":[160],"coordinate-space":[161],"recurrent":[162],"CONTENTS:-":[164],"models/:":[165],"directories":[167],"for":[168],"all":[169],"4":[170],"trained":[171],"architectures.-":[172],"load_and_predict.py:":[173],"Self-contained":[174],"Python":[175],"script":[176],"load":[178],"run":[181],"verification/inference.-":[182],"README.md:":[183],"Quickstart":[184],"instructions":[185],"input":[187],"specifications.":[188]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-09-23T00:00:00"}