{"id":"https://openalex.org/W4405270517","doi":"https://doi.org/10.1109/cvmi61877.2024.10781631","title":"Comparative study of Federated Learning and Machine Learning for Drug Discovery","display_name":"Comparative study of Federated Learning and Machine Learning for Drug Discovery","publication_year":2024,"publication_date":"2024-10-19","ids":{"openalex":"https://openalex.org/W4405270517","doi":"https://doi.org/10.1109/cvmi61877.2024.10781631"},"language":"en","primary_location":{"id":"doi:10.1109/cvmi61877.2024.10781631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvmi61877.2024.10781631","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023612188","display_name":"Kausar Ali","orcid":"https://orcid.org/0000-0002-3085-4854"},"institutions":[{"id":"https://openalex.org/I171210897","display_name":"Aligarh Muslim University","ror":"https://ror.org/03kw9gc02","country_code":"IN","type":"education","lineage":["https://openalex.org/I171210897"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Kausar Ali","raw_affiliation_strings":["(Aligarh Muslim University),Department of Computer Science,Aligarh,India"],"affiliations":[{"raw_affiliation_string":"(Aligarh Muslim University),Department of Computer Science,Aligarh,India","institution_ids":["https://openalex.org/I171210897"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047605719","display_name":"Aasim Zafar","orcid":"https://orcid.org/0000-0003-1331-014X"},"institutions":[{"id":"https://openalex.org/I171210897","display_name":"Aligarh Muslim University","ror":"https://ror.org/03kw9gc02","country_code":"IN","type":"education","lineage":["https://openalex.org/I171210897"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Aasim Zafar","raw_affiliation_strings":["(Aligarh Muslim University),Department of Computer Science,Aligarh,India"],"affiliations":[{"raw_affiliation_string":"(Aligarh Muslim University),Department of Computer Science,Aligarh,India","institution_ids":["https://openalex.org/I171210897"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5023612188"],"corresponding_institution_ids":["https://openalex.org/I171210897"],"apc_list":null,"apc_paid":null,"fwci":1.518,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.85367329,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9753000140190125,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.9753000140190125,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7208484411239624},{"id":"https://openalex.org/keywords/drug-discovery","display_name":"Drug discovery","score":0.5623365640640259},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4617948532104492},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4400622248649597},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3659769892692566},{"id":"https://openalex.org/keywords/bioinformatics","display_name":"Bioinformatics","score":0.09345406293869019}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7208484411239624},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.5623365640640259},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4617948532104492},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4400622248649597},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3659769892692566},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.09345406293869019},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvmi61877.2024.10781631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvmi61877.2024.10781631","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1997808394","https://openalex.org/W3099252273","https://openalex.org/W3186401068","https://openalex.org/W3200628798","https://openalex.org/W4205556479","https://openalex.org/W4285613887","https://openalex.org/W4293155325","https://openalex.org/W4297541603","https://openalex.org/W4304892324","https://openalex.org/W4313627870","https://openalex.org/W4319863561","https://openalex.org/W4322754240","https://openalex.org/W4365448810","https://openalex.org/W4382318866","https://openalex.org/W4389070650","https://openalex.org/W4389525040"],"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":{"It":[0],"is":[1,29],"widely":[2],"recognized":[3],"that":[4],"machine":[5,141],"learning":[6,142,169],"has":[7,145],"proven":[8],"to":[9,46,56,116,127,139,157],"be":[10,63],"one":[11],"of":[12,20,40,129,189,244],"the":[13,18,38,50,83,92,119,159,162,171],"most":[14],"effective":[15],"technologies":[16,164],"in":[17,23,53,102,230],"field":[19],"bioinformatics,":[21],"particularly":[22],"drug":[24,103,204],"discovery.":[25,205],"However,":[26],"this":[27,96,153,207],"technology":[28,68],"associated":[30],"with":[31,86,149,222],"certain":[32],"challenges,":[33],"including":[34],"data":[35,55,197],"security":[36],"concerns,":[37],"risk":[39],"exposing":[41],"patients\u2019":[42],"personal":[43],"medical":[44],"information":[45],"malicious":[47],"actors,":[48],"and":[49,105,113,165,195,225,234],"costs":[51,199],"involved":[52],"transferring":[54],"a":[57,66,87,107,242],"central":[58],"repository.":[59],"These":[60],"challenges":[61],"can":[62],"addressed":[64],"using":[65,200],"novel":[67],"called":[69],"Federated":[70,100,114,123,201,232],"Learning,":[71],"which":[72],"trains":[73],"models":[74],"on":[75],"local":[76],"devices":[77],"or":[78,174],"locations":[79],"by":[80,122],"sharing":[81],"only":[82],"learned":[84],"parameters":[85],"global":[88],"model,":[89],"rather":[90],"than":[91],"actual":[93],"data.":[94],"In":[95,177,206],"study,":[97,208],"we":[98,155,180,209,238],"apply":[99],"Learning":[101,112,115,124,202,233],"discovery":[104],"conduct":[106],"comparative":[108,228],"analysis":[109,229],"between":[110,161],"Machine":[111,131,235],"determine":[117,166],"if":[118],"results":[120],"achieved":[121],"are":[125],"comparable":[126],"those":[128],"traditional":[130],"Learning.":[132,236],"While":[133],"numerous":[134],"studies":[135],"have":[136,239],"been":[137,146,240],"conducted":[138],"compare":[140],"algorithms,":[143],"there":[144],"limited":[147],"comparison":[148,243],"federated":[150,168],"learning.":[151],"Through":[152],"analysis,":[154,179],"aim":[156],"identify":[158],"differences":[160],"two":[163],"whether":[167],"achieves":[170],"same,":[172],"better,":[173],"worse":[175],"results.":[176],"our":[178,187],"obtained":[181],"slightly":[182],"better":[183],"results,":[184],"thereby":[185],"achieving":[186],"objective":[188],"securing":[190],"data,":[191],"protecting":[192],"patient":[193],"privacy,":[194],"eliminating":[196],"transfer":[198],"for":[203],"implemented":[210],"six":[211],"algorithms\u2014Artificial":[212],"Neural":[213],"Networks,":[214],"Logistic":[215],"Regression,":[216],"Perceptron,":[217],"Ridge":[218,220],"Classifier,":[219],"Classifier":[221],"Cross":[223],"Validation,":[224],"SGD":[226],"Classifier\u2014for":[227],"both":[231,245],"Consequently,":[237],"accomplished":[241],"technologies.":[246]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
