{"id":"https://openalex.org/W1964880336","doi":"https://doi.org/10.1371/journal.pone.0007522","title":"Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs","display_name":"Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs","publication_year":2009,"publication_date":"2009-10-21","ids":{"openalex":"https://openalex.org/W1964880336","doi":"https://doi.org/10.1371/journal.pone.0007522","mag":"1964880336","pmid":"https://pubmed.ncbi.nlm.nih.gov/19847297","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/2760777"},"language":"en","primary_location":{"id":"doi:10.1371/journal.pone.0007522","doi":"https://doi.org/10.1371/journal.pone.0007522","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0007522","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0007522&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLoS ONE","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0007522&type=printable","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067788557","display_name":"Kyle A. McQuisten","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I69230048","display_name":"Integrated DNA Technologies (United States)","ror":"https://ror.org/009jvpf03","country_code":"US","type":"company","lineage":["https://openalex.org/I69230048"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kyle A. McQuisten","raw_affiliation_strings":["Department of Bioinformatics, Integrated DNA Technologies, Inc, Coralville, Iowa, United States of America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Bioinformatics, Integrated DNA Technologies, Inc, Coralville, Iowa, United States of America","institution_ids":["https://openalex.org/I69230048"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110967329","display_name":"Andrew S. Peek","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I69230048","display_name":"Integrated DNA Technologies (United States)","ror":"https://ror.org/009jvpf03","country_code":"US","type":"company","lineage":["https://openalex.org/I69230048"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andrew S. Peek","raw_affiliation_strings":["Department of Bioinformatics, Integrated DNA Technologies, Inc., Coralville, Iowa, United States of America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Bioinformatics, Integrated DNA Technologies, Inc., Coralville, Iowa, United States of America","institution_ids":["https://openalex.org/I69230048"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5110967329"],"corresponding_institution_ids":["https://openalex.org/I69230048"],"apc_list":{"value":2382,"currency":"USD","value_usd":2382},"apc_paid":{"value":2382,"currency":"USD","value_usd":2382},"fwci":0.6661,"has_fulltext":true,"cited_by_count":20,"citation_normalized_percentile":{"value":0.62573638,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"4","issue":"10","first_page":"e7522","last_page":"e7522"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10725","display_name":"RNA Interference and Gene Delivery","score":0.8295999765396118,"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/T10725","display_name":"RNA Interference and Gene Delivery","score":0.8295999765396118,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.0414000004529953,"subfield":{"id":"https://openalex.org/subfields/3002","display_name":"Drug Discovery"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.013700000010430813,"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/sirna","display_name":"siRNA","score":0.8560000061988831},{"id":"https://openalex.org/keywords/gene-knockdown","display_name":"gene knockdown","score":0.8629999756813049},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.828000009059906},{"id":"https://openalex.org/keywords/general-linear-model","display_name":"general linear model","score":0.9169999957084656},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":1.0},{"id":"https://openalex.org/keywords/feature-mapping","display_name":"feature mapping","score":0.8339999914169312},{"id":"https://openalex.org/keywords/model-comparison","display_name":"model comparison","score":0.7170000076293945},{"id":"https://openalex.org/keywords/cross-validation","display_name":"cross-validation","score":0.7699999809265137},{"id":"https://openalex.org/keywords/feature-relevance","display_name":"feature relevance","score":0.5979999899864197},{"id":"https://openalex.org/keywords/model-prediction-accuracy","display_name":"model prediction accuracy","score":0.3869999945163727}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7443844079971313},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6851803660392761},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6445107460021973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.632655680179596},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6160514950752258},{"id":"https://openalex.org/C22615655","wikidata":"https://www.wikidata.org/wiki/Q203221","display_name":"Small interfering RNA","level":4,"score":0.5261868238449097},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.5057663917541504},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.480844110250473},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.47271889448165894},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33767154812812805},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.144786536693573},{"id":"https://openalex.org/C67705224","wikidata":"https://www.wikidata.org/wiki/Q11053","display_name":"RNA","level":3,"score":0.08319562673568726},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010363","descriptor_name":"Pattern Recognition, Automated","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D016014","descriptor_name":"Linear Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016415","descriptor_name":"Sequence Alignment","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D017423","descriptor_name":"Sequence Analysis, RNA","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D034741","descriptor_name":"RNA, Small Interfering","qualifier_ui":"Q000235","qualifier_name":"genetics","is_major_topic":true}],"locations_count":7,"locations":[{"id":"doi:10.1371/journal.pone.0007522","doi":"https://doi.org/10.1371/journal.pone.0007522","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0007522","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0007522&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLoS ONE","raw_type":"journal-article"},{"id":"pmh:oai:pubmedcentral.nih.gov:2760777","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/2760777","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLoS One","raw_type":"Text"},{"id":"pmid:19847297","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/19847297","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PloS one","raw_type":"Comparative Study"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.619.6615","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.619.6615","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://dollar.biz.uiowa.edu/~street/McQuistenPeek2009.PDF","raw_type":"text"},{"id":"pmh:oai:RePEc:plo:pone00:0007522","doi":null,"is_oa":false,"landing_page_url":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0007522","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:doaj.org/article:54a6ebf42aa84d0aa39f142f1ab115e5","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/54a6ebf42aa84d0aa39f142f1ab115e5","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PLoS ONE, Vol 4, Iss 10, p e7522 (2009)","raw_type":"article"},{"id":"pmh:oai:figshare.com:article/145971","doi":null,"is_oa":true,"landing_page_url":"https://figshare.com/articles/dataset/Comparing_Artificial_Neural_Networks_General_Linear_Models_and_Support_Vector_Machines_in_Building_Predictive_Models_for_Small_Interfering_RNAs/145971","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"Dataset"}],"best_oa_location":{"id":"doi:10.1371/journal.pone.0007522","doi":"https://doi.org/10.1371/journal.pone.0007522","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0007522","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0007522&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLoS ONE","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.504800021648407}],"study_designs":[],"awards":[{"id":"https://openalex.org/G7597769888","display_name":"Support Vector Machine modeling software for improving RNAi efficacy prediction","funder_award_id":"R43GM079132","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1964880336.pdf","grobid_xml":"https://content.openalex.org/works/W1964880336.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1504401263","https://openalex.org/W1509715834","https://openalex.org/W1520518044","https://openalex.org/W1563015844","https://openalex.org/W1647075334","https://openalex.org/W1920564293","https://openalex.org/W1965077482","https://openalex.org/W1970191962","https://openalex.org/W1970406637","https://openalex.org/W1971498002","https://openalex.org/W1978911538","https://openalex.org/W1986687961","https://openalex.org/W1988783048","https://openalex.org/W1991328297","https://openalex.org/W1996164865","https://openalex.org/W2006917788","https://openalex.org/W2021310770","https://openalex.org/W2022314287","https://openalex.org/W2029052373","https://openalex.org/W2033471072","https://openalex.org/W2045452812","https://openalex.org/W2054757478","https://openalex.org/W2068205681","https://openalex.org/W2071596792","https://openalex.org/W2082213106","https://openalex.org/W2097261962","https://openalex.org/W2100600738","https://openalex.org/W2106082032","https://openalex.org/W2109609799","https://openalex.org/W2120199131","https://openalex.org/W2129222324","https://openalex.org/W2136361016","https://openalex.org/W2137489993","https://openalex.org/W2137553623","https://openalex.org/W2141253416","https://openalex.org/W2146634740","https://openalex.org/W2149538815","https://openalex.org/W2151432781","https://openalex.org/W2156336405","https://openalex.org/W2160923685","https://openalex.org/W2164240862","https://openalex.org/W2164641023","https://openalex.org/W2166802510","https://openalex.org/W2167277498"],"related_works":["https://openalex.org/W2022314287","https://openalex.org/W2068400095","https://openalex.org/W2170918433","https://openalex.org/W2247403536","https://openalex.org/W7100997191","https://openalex.org/W2006917788","https://openalex.org/W2162170730","https://openalex.org/W2060022712","https://openalex.org/W2185806702","https://openalex.org/W4413348198"],"abstract_inverted_index":{"BACKGROUND:":[0],"Exogenous":[1],"short":[2],"interfering":[3],"RNAs":[4],"(siRNAs)":[5],"induce":[6,25,49],"a":[7,204,328],"gene":[8],"knockdown":[9],"effect":[10,27,86],"in":[11,176,285,300],"cells":[12],"by":[13,158],"interacting":[14],"with":[15],"naturally":[16],"occurring":[17],"RNA":[18],"processing":[19],"machinery.":[20],"However":[21],"not":[22,290],"all":[23,335],"siRNAs":[24,44],"this":[26],"equally.":[28],"Several":[29],"heterogeneous":[30],"kinds":[31,192],"of":[32,87,144,150,195,244,259,272,276,303,316,331,339],"machine":[33],"learning":[34,89,108,151,165,215,293,317],"techniques":[35,58,109,166,216,250,318],"and":[36,45,63,100,129,153,167,185,193,217,248,279,319,344],"feature":[37,91,135,154,168,218,304,320],"sets":[38,219],"have":[39],"been":[40],"applied":[41],"to":[42,48,56,71,112,141,172,180,208,239,252,256,283],"modeling":[43,228],"their":[46],"abilities":[47],"knockdown.":[50],"There":[51],"is":[52,66,266],"some":[53],"growing":[54],"agreement":[55],"which":[57],"produce":[59],"maximally":[60,286],"predictive":[61,74,103,114,177,287],"models":[62,115,143,211,288,308],"yet":[64],"there":[65,77,311],"little":[67],"consensus":[68],"for":[69,116,183,220,274],"methods":[70,137,200,238,322],"compare":[72,209],"among":[73,102,210,224],"models.":[75,104,246],"Also,":[76],"are":[78,289,312],"few":[79],"comparative":[80],"studies":[81],"that":[82],"address":[83],"what":[84],"the":[85,173,242,257,263,301,307,324,336,342],"choosing":[88],"technique,":[90],"set":[92],"or":[93,226,233],"cross":[94],"validation":[95],"approach":[96],"has":[97],"on":[98],"finding":[99],"discriminating":[101],"PRINCIPAL":[105],"FINDINGS:":[106],"Three":[107],"were":[110,138,156],"used":[111,140],"develop":[113],"effective":[117],"siRNA":[118,145],"sequences":[119],"including":[120],"Artificial":[121],"Neural":[122],"Networks":[123],"(ANNs),":[124],"General":[125],"Linear":[126],"Models":[127],"(GLMs)":[128],"Support":[130],"Vector":[131],"Machines":[132],"(SVMs).":[133],"Five":[134],"mapping":[136,155,169,321],"also":[139],"generate":[142],"activities.":[146],"The":[147,199],"2":[148],"factors":[149],"technique":[152,265,326],"evaluated":[157],"complete":[159],"3x5":[160],"factorial":[161],"ANOVA.":[162],"Overall,":[163],"both":[164],"contributed":[170],"significantly":[171,333],"observed":[174],"variance":[175],"models,":[178],"but":[179,262],"differing":[181],"degrees":[182],"precision":[184,278],"accuracy":[186],"as":[187,189],"well":[188],"across":[190,292],"different":[191],"levels":[194],"model":[196,277],"cross-validation.":[197],"CONCLUSIONS:":[198],"presented":[201],"here":[202],"provide":[203],"robust":[205,268],"statistical":[206],"framework":[207],"developed":[212,309],"under":[213,269,327],"distinct":[214],"siRNAs.":[221],"Further":[222],"comparisons":[223],"current":[225],"future":[227],"approaches":[229],"should":[230,297],"apply":[231],"these":[232],"other":[234],"suitable":[235],"statistically":[236,313],"equivalent":[237],"critically":[240],"evaluate":[241],"performance":[243],"proposed":[245],"ANN":[247,343],"GLM":[249,345],"tend":[251],"be":[253,298],"more":[254,267],"sensitive":[255],"inclusion":[258],"noisy":[260],"features,":[261],"SVM":[264,325],"large":[270],"numbers":[271],"features":[273,332,340],"measures":[275],"accuracy.":[280],"Features":[281],"found":[282],"result":[284],"consistent":[291],"techniques,":[294],"suggesting":[295],"care":[296],"taken":[299],"interpretation":[302],"relevance.":[305],"In":[306],"here,":[310],"differentiable":[314],"combinations":[315,338],"where":[323],"specific":[329],"combination":[330],"outperforms":[334],"best":[337],"within":[341],"techniques.":[346]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":2}],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2016-06-24T00:00:00"}
