{"id":"https://openalex.org/W7151920287","doi":"https://doi.org/10.48550/arxiv.2604.04981","title":"An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing","display_name":"An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing","publication_year":2026,"publication_date":"2026-04-04","ids":{"openalex":"https://openalex.org/W7151920287","doi":"https://doi.org/10.48550/arxiv.2604.04981"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.04981","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04981","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":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.2604.04981","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047315657","display_name":"Philipp R\u00f6chner","orcid":"https://orcid.org/0000-0003-3376-6670"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"R\u00f6chner, Philipp","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026282255","display_name":"Clarissa Gon\u00e7alves Kramer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kr\u00e4mer, Clarissa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101982949","display_name":"Johannes Mayer","orcid":"https://orcid.org/0000-0002-1280-217X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mayer, Johannes U","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035443886","display_name":"Franz Rothlauf","orcid":"https://orcid.org/0000-0003-3376-427X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rothlauf, Franz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133180991","display_name":"Steffen Albrecht","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Albrecht, Steffen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5041678821","display_name":"Maximilian Sprang","orcid":"https://orcid.org/0000-0002-8438-4747"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sprang, Maximilian","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/T11642","display_name":"Genomics and Rare Diseases","score":0.33880001306533813,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"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/T11642","display_name":"Genomics and Rare Diseases","score":0.33880001306533813,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"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/T10885","display_name":"Gene expression and cancer classification","score":0.19349999725818634,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.125,"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/feature","display_name":"Feature (linguistics)","score":0.6632999777793884},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5382999777793884},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5037999749183655},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4715000092983246},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4602999985218048},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4496999979019165},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3806999921798706},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.31150001287460327}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7031999826431274},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6632999777793884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6399999856948853},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5382999777793884},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5037999749183655},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4715000092983246},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4602999985218048},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4422999918460846},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3806999921798706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3334999978542328},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.31150001287460327},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C2779346075","wikidata":"https://www.wikidata.org/wiki/Q7268763","display_name":"Quality Score","level":3,"score":0.2906000018119812},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2799000144004822},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.26489999890327454},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2590999901294708}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.04981","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04981","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":"doi:10.48550/arxiv.2604.04981","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04981","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Next-generation":[0],"sequencing":[1],"(NGS)":[2],"is":[3],"a":[4,51,63,95,146],"key":[5],"technique":[6],"for":[7],"studying":[8],"the":[9,40,118,126,175,178,181,209],"DNA":[10],"and":[11,129,155,201],"RNA":[12],"of":[13,42,54,73,81,98,139,163,180,205,211],"organisms.":[14],"However,":[15],"identifying":[16],"quality":[17,43,86,148,153,172,212],"problems":[18],"in":[19,112],"NGS":[20,46,68],"data":[21],"across":[22],"different":[23,194],"experimental":[24],"settings":[25],"remains":[26],"challenging.":[27],"To":[28,57],"develop":[29],"automated":[30,152],"quality-control":[31],"tools,":[32],"researchers":[33,190],"require":[34],"datasets":[35],"with":[36,70],"features":[37,83,99,106,124],"that":[38],"capture":[39],"characteristics":[41],"problems.":[44,213],"Existing":[45],"repositories,":[47],"however,":[48],"offer":[49],"only":[50],"limited":[52],"number":[53,97,204],"quality-related":[55,74],"features.":[56],"address":[58],"this":[59],"gap,":[60],"we":[61],"propose":[62],"dataset":[64,144],"derived":[65,84,108,150],"from":[66,85,101,109,132,151,174],"37,491":[67],"samples":[69,131],"two":[71],"types":[72,196],"feature":[75,140,183,187,195],"representations.":[76,141,184],"The":[77,91,142,185],"first":[78],"type":[79,93],"consists":[80],"34":[82],"control":[87,154],"tools":[88],"(QC-34":[89,197],"features).":[90,122],"second":[92],"has":[94],"variable":[96],"ranging":[100],"eight":[102],"to":[103,191],"1,183.":[104],"These":[105],"were":[107],"read":[110],"counts":[111],"problematic":[113],"genomic":[114,134],"regions":[115],"identified":[116],"by":[117],"ENCODE":[119],"blocklist":[120],"(BL":[121],"All":[123],"describe":[125],"same":[127],"human":[128],"mouse":[130],"five":[133],"assays,":[135],"allowing":[136],"direct":[137],"comparison":[138],"proposed":[143,186],"includes":[145],"binary":[147],"label,":[149],"domain":[156],"experts.":[157],"Among":[158],"all":[159],"samples,":[160],"$3.2\\%$":[161],"are":[162],"low":[164],"quality.":[165],"Supervised":[166],"machine":[167],"learning":[168],"algorithms":[169],"accurately":[170],"predicted":[171],"labels":[173],"features,":[176],"confirming":[177],"relevance":[179],"provided":[182],"representations":[188],"enable":[189],"study":[192],"how":[193],"vs.":[198],"BL":[199,206],"features)":[200,207],"granularities":[202],"(varying":[203],"affect":[208],"detection":[210]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-09T00:00:00"}