{"id":"https://openalex.org/W2577012195","doi":"https://doi.org/10.1109/bibm.2016.7822478","title":"Deep-Learning: Investigating feed-forward deep Neural Networks for modeling high throughput chemical bioactivity data","display_name":"Deep-Learning: Investigating feed-forward deep Neural Networks for modeling high throughput chemical bioactivity data","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2577012195","doi":"https://doi.org/10.1109/bibm.2016.7822478","mag":"2577012195"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2016.7822478","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2016.7822478","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5105352906","display_name":"Jun Huan","orcid":"https://orcid.org/0000-0003-4929-2617"},"institutions":[{"id":"https://openalex.org/I146416000","display_name":"University of Kansas","ror":"https://ror.org/001tmjg57","country_code":"US","type":"education","lineage":["https://openalex.org/I146416000"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jun Huan","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science at the University of Kansas, United States of America"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science at the University of Kansas, United States of America","institution_ids":["https://openalex.org/I146416000"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5105352906"],"corresponding_institution_ids":["https://openalex.org/I146416000"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16609178,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9993000030517578,"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":0.9993000030517578,"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/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.9312999844551086,"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/computer-science","display_name":"Computer science","score":0.7527822256088257},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6910210847854614},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6904861330986023},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6179659366607666},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.559982180595398},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.49986863136291504},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4522925913333893}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7527822256088257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6910210847854614},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6904861330986023},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6179659366607666},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.559982180595398},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.49986863136291504},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4522925913333893}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2016.7822478","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2016.7822478","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W2782041652","https://openalex.org/W4380075502","https://openalex.org/W4377865163","https://openalex.org/W3193857078","https://openalex.org/W2888956734","https://openalex.org/W3000197790","https://openalex.org/W4315865067","https://openalex.org/W2979433843","https://openalex.org/W3208304128"],"abstract_inverted_index":{"Summary":[0],"form":[1],"only":[2],"given.":[3],"In":[4,94,122],"recent":[5,101],"years,":[6],"research":[7],"in":[8,28,57],"Artificial":[9],"Neural":[10,107],"Networks":[11],"(ANNs)":[12],"has":[13,64],"resurged,":[14],"now":[15],"under":[16],"the":[17,49,169],"Deep-Learning":[18,33],"umbrella,":[19],"and":[20,30,44,60,73,111,132,176,197],"grown":[21],"extremely":[22],"popular":[23],"due":[24],"to":[25,42,119,167,173],"major":[26],"breakthroughs":[27],"methodological":[29],"computing":[31],"capabilities.":[32],"methods":[34,67,116],"are":[35,214],"part":[36],"of":[37,54,79,114,171],"representation-learning":[38],"algorithms":[39],"that":[40,182,212],"attempt":[41],"extract":[43],"organize":[45],"discriminative":[46],"information":[47],"from":[48,149],"data.":[50,222],"Recently":[51],"reported":[52,77],"success":[53],"DL":[55],"techniques":[56,82,217],"crowd-sourced":[58],"QSARs":[59],"predictive":[61],"toxicology":[62,74],"competitions":[63],"showcased":[65],"these":[66,115],"as":[68,117,156],"powerful":[69,215],"tools":[70],"for":[71,83,88,218],"drug-discovery":[72],"research.":[75],"Nevertheless,":[76],"applications":[78],"Deep":[80,106],"Learning":[81],"modeling":[84,216,219],"complex":[85,220],"bioactivity":[86,146,221],"data":[87],"small":[89],"molecules":[90],"remain":[91],"still":[92],"limited.":[93],"this":[95],"talk":[96],"I":[97],"will":[98],"present":[99],"our":[100,123],"work":[102],"on":[103],"optimizing":[104],"feed-forward":[105],"Nets":[108],"(DNNs)":[109],"hyperparameters":[110],"performance":[112,170,204],"evaluation":[113],"compared":[118,142],"shallow":[120],"methods.":[121],"study":[124],"48":[125],"DNNs,":[126],"24":[127],"Random":[128],"Forest,":[129],"20":[130],"SVM":[131,175],"6":[133],"Na\u00efve":[134],"Bayes":[135],"arbitrary":[136],"but":[137],"reasonably":[138],"selected":[139],"configurations":[140],"were":[141],"employing":[143],"7":[144],"diverse":[145],"datasets":[147],"assembled":[148],"ChEMBL":[150],"repository":[151],"combined":[152],"with":[153,184],"circular":[154],"fingerprints":[155],"molecular":[157],"descriptors.":[158],"The":[159],"non-parametric":[160],"Wilcoxon":[161],"paired":[162],"singed-rank":[163],"test":[164],"was":[165,180],"employed":[166],"compare":[168],"DNN":[172],"RF,":[174],"NB.":[177],"Overall":[178],"it":[179],"found":[181],"DNNs":[183,213],"2":[185],"hidden":[186,192],"layers,":[187],"2,000":[188],"neurons":[189],"per":[190],"each":[191],"layer,":[193],"ReLU":[194],"activation":[195],"function":[196],"Dropout":[198],"regularization":[199],"technique":[200],"achieved":[201],"strong":[202],"classification":[203],"across":[205],"all":[206],"tested":[207],"datasets.":[208],"Our":[209],"results":[210],"demonstrate":[211]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
