{"id":"https://openalex.org/W4367338029","doi":"https://doi.org/10.4236/ojapps.2023.134044","title":"Drug-Treatment Generation Combinatorial Algorithm Based on Machine Learning and Statistical Methodologies","display_name":"Drug-Treatment Generation Combinatorial Algorithm Based on Machine Learning and Statistical Methodologies","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4367338029","doi":"https://doi.org/10.4236/ojapps.2023.134044"},"language":"en","primary_location":{"id":"doi:10.4236/ojapps.2023.134044","doi":"https://doi.org/10.4236/ojapps.2023.134044","is_oa":true,"landing_page_url":"http://dx.doi.org/10.4236/ojapps.2023.134044","pdf_url":"https://www.scirp.org/pdf/ojapps_2023042714030652.pdf","source":{"id":"https://openalex.org/S2764596442","display_name":"Open Journal of Applied Sciences","issn_l":"2165-3917","issn":["2165-3917","2165-3925"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core"],"host_organization":"https://openalex.org/P4310320480","host_organization_name":"Scientific Research Publishing","host_organization_lineage":["https://openalex.org/P4310320480"],"host_organization_lineage_names":["Scientific Research Publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Journal of Applied Sciences","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.scirp.org/pdf/ojapps_2023042714030652.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078346083","display_name":"Karen Gishyan","orcid":"https://orcid.org/0000-0002-6239-9294","observed_orcids":["https://orcid.org/0000-0002-6239-9294"]},"institutions":[{"id":"https://openalex.org/I4210133484","display_name":"Institute for Informatics and Automation Problems","ror":"https://ror.org/04a8k4p47","country_code":"AM","type":"facility","lineage":["https://openalex.org/I4210133484","https://openalex.org/I99064381"]},{"id":"https://openalex.org/I99064381","display_name":"National Academy of Sciences of Armenia","ror":"https://ror.org/04mczx267","country_code":"AM","type":"government","lineage":["https://openalex.org/I99064381"]}],"countries":["AM"],"is_corresponding":true,"raw_author_name":"Karen Gishyan","raw_affiliation_strings":["Institute for Informatics and Automation Problems, NAS RA, Yerevan, Armenia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Informatics and Automation Problems, NAS RA, Yerevan, Armenia","institution_ids":["https://openalex.org/I4210133484","https://openalex.org/I99064381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5078346083"],"corresponding_institution_ids":["https://openalex.org/I4210133484","https://openalex.org/I99064381"],"apc_list":null,"apc_paid":null,"fwci":0.087,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.25731694,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":"13","issue":"04","first_page":"548","last_page":"561"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.7026000022888184,"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"}},"topics":[{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.7026000022888184,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.1289999932050705,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.08489999920129776,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/combination-therapy","display_name":"combination therapy","score":0.7639999985694885},{"id":"https://openalex.org/keywords/personalized-treatment","display_name":"personalized treatment","score":0.5630000233650208},{"id":"https://openalex.org/keywords/clustering","display_name":"clustering","score":0.6620000004768372},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"supervised learning","score":0.7760000228881836},{"id":"https://openalex.org/keywords/treatment-response-prediction","display_name":"treatment response prediction","score":0.3409999907016754}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6904759407043457},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.648432195186615},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6226569414138794},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5606050491333008},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5344082117080688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5339226722717285},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.5229504704475403},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5071725845336914},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33206120133399963},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.17310872673988342},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"score":0.09108003973960876},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0874902606010437},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4236/ojapps.2023.134044","doi":"https://doi.org/10.4236/ojapps.2023.134044","is_oa":true,"landing_page_url":"http://dx.doi.org/10.4236/ojapps.2023.134044","pdf_url":"https://www.scirp.org/pdf/ojapps_2023042714030652.pdf","source":{"id":"https://openalex.org/S2764596442","display_name":"Open Journal of Applied Sciences","issn_l":"2165-3917","issn":["2165-3917","2165-3925"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core"],"host_organization":"https://openalex.org/P4310320480","host_organization_name":"Scientific Research Publishing","host_organization_lineage":["https://openalex.org/P4310320480"],"host_organization_lineage_names":["Scientific Research Publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Journal of Applied Sciences","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.4236/ojapps.2023.134044","doi":"https://doi.org/10.4236/ojapps.2023.134044","is_oa":true,"landing_page_url":"http://dx.doi.org/10.4236/ojapps.2023.134044","pdf_url":"https://www.scirp.org/pdf/ojapps_2023042714030652.pdf","source":{"id":"https://openalex.org/S2764596442","display_name":"Open Journal of Applied Sciences","issn_l":"2165-3917","issn":["2165-3917","2165-3925"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core"],"host_organization":"https://openalex.org/P4310320480","host_organization_name":"Scientific Research Publishing","host_organization_lineage":["https://openalex.org/P4310320480"],"host_organization_lineage_names":["Scientific Research Publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Journal of Applied Sciences","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.9537000060081482}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4367338029.pdf","grobid_xml":"https://content.openalex.org/works/W4367338029.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1175355699","https://openalex.org/W1973047627","https://openalex.org/W2166662937","https://openalex.org/W2540149843","https://openalex.org/W2605253636","https://openalex.org/W2770506929","https://openalex.org/W2774112395","https://openalex.org/W2885408863","https://openalex.org/W2892308164","https://openalex.org/W2895437177","https://openalex.org/W2898574163","https://openalex.org/W2901997892","https://openalex.org/W2908459598","https://openalex.org/W2950722229","https://openalex.org/W2982378999","https://openalex.org/W3008003211","https://openalex.org/W3082618359","https://openalex.org/W3097763558","https://openalex.org/W3130580848","https://openalex.org/W4283445992","https://openalex.org/W4387146123"],"related_works":["https://openalex.org/W6960507403","https://openalex.org/W3081201079","https://openalex.org/W4244046359","https://openalex.org/W4410379963","https://openalex.org/W4382371649","https://openalex.org/W6903998844","https://openalex.org/W7077062603","https://openalex.org/W4414648829","https://openalex.org/W2765388300","https://openalex.org/W2165863045"],"abstract_inverted_index":{"Finding":[0],"out":[1],"the":[2,12,20,52,65,78,84,97],"desired":[3],"drug":[4,31],"combinations":[5,16,32,98],"is":[6],"a":[7,45],"challenging":[8],"task":[9],"because":[10],"of":[11,14,72,86,88,93],"number":[13],"different":[15],"that":[17,23],"exist":[18],"and":[19,44,103],"adversarial":[21],"effects":[22],"may":[24],"arise.":[25],"In":[26],"this":[27],"work,":[28],"we":[29],"generate":[30],"over":[33],"multiple":[34],"stages":[35],"using":[36,99],"distance":[37],"calculation":[38,48],"metrics":[39],"from":[40],"supervised":[41],"learning,":[42],"clustering,":[43],"statistical":[46],"similarity":[47],"metric":[49],"for":[50,60],"deriving":[51],"optimal":[53],"treatment":[54],"sequences.":[55],"The":[56],"combination":[57],"generation":[58],"happens":[59],"each":[61,70],"patient":[62],"based":[63],"on":[64],"characteristics":[66],"(features)":[67],"observed":[68],"during":[69],"stage":[71],"treatment.":[73],"Our":[74],"approach":[75],"considers":[76],"not":[77],"drug-to-drug":[79],"(one-to-one)":[80],"effect,":[81],"but":[82],"rather":[83],"effect":[85],"group":[87,92],"drugs":[89],"with":[90],"another":[91],"drugs.":[94],"We":[95],"evaluate":[96],"an":[100],"FNN":[101],"model":[102],"identify":[104],"future":[105],"improvement":[106],"directions.":[107]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2023-04-30T00:00:00"}
