{"id":"https://openalex.org/W3118110915","doi":"https://doi.org/10.23919/eusipco47968.2020.9287617","title":"Independent Vector Analysis for Molecular Data Fusion: Application to Property Prediction and Knowledge Discovery of Energetic Materials","display_name":"Independent Vector Analysis for Molecular Data Fusion: Application to Property Prediction and Knowledge Discovery of Energetic Materials","publication_year":2020,"publication_date":"2020-12-18","ids":{"openalex":"https://openalex.org/W3118110915","doi":"https://doi.org/10.23919/eusipco47968.2020.9287617","mag":"3118110915"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco47968.2020.9287617","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287617","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5006915006","display_name":"Zois Boukouvalas","orcid":"https://orcid.org/0000-0002-5131-1891"},"institutions":[{"id":"https://openalex.org/I181401687","display_name":"American University","ror":"https://ror.org/052w4zt36","country_code":"US","type":"education","lineage":["https://openalex.org/I181401687"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zois Boukouvalas","raw_affiliation_strings":["American University, Washington, DC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"American University, Washington, DC","institution_ids":["https://openalex.org/I181401687"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078827360","display_name":"Monica Puerto","orcid":null},"institutions":[{"id":"https://openalex.org/I181401687","display_name":"American University","ror":"https://ror.org/052w4zt36","country_code":"US","type":"education","lineage":["https://openalex.org/I181401687"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Monica Puerto","raw_affiliation_strings":["American University, Washington, DC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"American University, Washington, DC","institution_ids":["https://openalex.org/I181401687"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029117077","display_name":"Daniel C. Elton","orcid":"https://orcid.org/0000-0003-0249-1387"},"institutions":[{"id":"https://openalex.org/I4210155647","display_name":"National Institutes of Health Clinical Center","ror":"https://ror.org/04vfsmv21","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210155647"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel C. Elton","raw_affiliation_strings":["Imaging Biomarkers and Computer-Aided Diagnosis Lab, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imaging Biomarkers and Computer-Aided Diagnosis Lab, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD","institution_ids":["https://openalex.org/I4210155647"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101476638","display_name":"Peter W. Chung","orcid":"https://orcid.org/0000-0002-3276-5994"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peter W. Chung","raw_affiliation_strings":["University of Maryland College Park, College Park, MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland College Park, College Park, MD","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036788329","display_name":"Mark Fuge","orcid":"https://orcid.org/0000-0003-3819-8895"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark D. Fuge","raw_affiliation_strings":["University of Maryland College Park, College Park, MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland College Park, College Park, MD","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3596,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.89624866,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1030","last_page":"1034"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9994000196456909,"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":0.9994000196456909,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9980000257492065,"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"}},{"id":"https://openalex.org/T10798","display_name":"Crystallography and molecular interactions","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/1606","display_name":"Physical and Theoretical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6736241579055786},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.5715550184249878},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5658770203590393},{"id":"https://openalex.org/keywords/drug-discovery","display_name":"Drug discovery","score":0.5580054521560669},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5120623707771301},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49547088146209717},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46197807788848877},{"id":"https://openalex.org/keywords/molecular-descriptor","display_name":"Molecular descriptor","score":0.4588054418563843},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4540272057056427},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4502529203891754},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44215118885040283},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4308242201805115},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.4212009608745575},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40766382217407227},{"id":"https://openalex.org/keywords/quantitative-structure\u2013activity-relationship","display_name":"Quantitative structure\u2013activity relationship","score":0.26419761776924133},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.19316697120666504}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6736241579055786},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.5715550184249878},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5658770203590393},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.5580054521560669},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5120623707771301},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49547088146209717},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46197807788848877},{"id":"https://openalex.org/C164923092","wikidata":"https://www.wikidata.org/wiki/Q3705921","display_name":"Molecular descriptor","level":3,"score":0.4588054418563843},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4540272057056427},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4502529203891754},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44215118885040283},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4308242201805115},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.4212009608745575},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40766382217407227},{"id":"https://openalex.org/C164126121","wikidata":"https://www.wikidata.org/wiki/Q766383","display_name":"Quantitative structure\u2013activity relationship","level":2,"score":0.26419761776924133},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.19316697120666504},{"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},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco47968.2020.9287617","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287617","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W112873001","https://openalex.org/W1246381107","https://openalex.org/W1531674615","https://openalex.org/W1755563775","https://openalex.org/W1890834058","https://openalex.org/W1975147762","https://openalex.org/W1991840148","https://openalex.org/W2005876975","https://openalex.org/W2022000422","https://openalex.org/W2033206800","https://openalex.org/W2104489082","https://openalex.org/W2123649031","https://openalex.org/W2133297572","https://openalex.org/W2137234026","https://openalex.org/W2158449659","https://openalex.org/W2296399167","https://openalex.org/W2730628148","https://openalex.org/W2735317774","https://openalex.org/W2784006847","https://openalex.org/W2784918977","https://openalex.org/W2883583109","https://openalex.org/W2883925078","https://openalex.org/W2899400350","https://openalex.org/W2945551948","https://openalex.org/W2990351990","https://openalex.org/W3102449990","https://openalex.org/W3104255923","https://openalex.org/W4295276059","https://openalex.org/W4298091452","https://openalex.org/W6748222190","https://openalex.org/W6752998028","https://openalex.org/W6756509678"],"related_works":["https://openalex.org/W2339806289","https://openalex.org/W2480711475","https://openalex.org/W2368132270","https://openalex.org/W2173185020","https://openalex.org/W109501413","https://openalex.org/W2075134888","https://openalex.org/W4388857868","https://openalex.org/W2946784807","https://openalex.org/W2157505264","https://openalex.org/W2038052791"],"abstract_inverted_index":{"Due":[0],"to":[1,9,95,164],"its":[2],"high":[3],"computational":[4],"speed":[5],"and":[6,13,34,116,134,144,173],"accuracy":[7],"compared":[8],"ab-initio":[10],"quantum":[11],"chemistry":[12],"forcefield":[14],"modeling,":[15],"the":[16,29,61,129,152,168],"prediction":[17,130,153],"of":[18,31,49,112,122,131,148,154],"molecular":[19,50,103,132,171],"properties":[20,133,157],"using":[21],"machine":[22,42],"learning":[23,43],"has":[24],"received":[25],"great":[26],"attention":[27],"in":[28,101],"fields":[30],"materials":[32],"design":[33],"drug":[35],"discovery.":[36,137],"A":[37],"main":[38],"ingredient":[39],"required":[40],"for":[41,67,77,128,135,151,161],"is":[44,70],"a":[45,86,118,142],"training":[46],"dataset":[47,146],"consisting":[48,147],"features-for":[51],"example":[52],"fingerprint":[53],"bits,":[54],"chemical":[55],"descriptors,":[56],"etc.":[57],"that":[58,90],"adequately":[59],"characterize":[60],"corresponding":[62],"molecules.":[63],"However,":[64],"choosing":[65],"features":[66],"any":[68],"application":[69],"highly":[71],"non-trivial,":[72],"since":[73],"no":[74],"\"universal\"":[75],"method":[76],"feature":[78,114],"selection":[79],"exists.":[80],"In":[81],"this":[82,140],"work,":[83],"we":[84],"propose":[85],"data":[87],"fusion":[88],"framework":[89],"uses":[91],"Independent":[92],"Vector":[93],"Analysis":[94],"uncover":[96],"underlying":[97],"complementary":[98],"information":[99],"contained":[100],"different":[102],"featurization":[104],"methods.":[105],"Our":[106],"approach":[107],"takes":[108],"an":[109],"arbitrary":[110],"number":[111],"individual":[113],"vectors":[115],"generates":[117],"low":[119],"dimensional":[120],"set":[121],"features-molecular":[123],"signatures-that":[124],"can":[125],"be":[126],"used":[127],"knowledge":[136],"We":[138],"demonstrate":[139],"on":[141],"small":[143],"diverse":[145],"energetic":[149,156],"compounds":[150],"several":[155],"as":[158,160],"well":[159],"demonstrating":[162],"how":[163],"provide":[165],"insights":[166],"onto":[167],"relationships":[169],"between":[170],"structures":[172],"properties.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}