{"id":"https://openalex.org/W2289512951","doi":"https://doi.org/10.1109/bmei.2015.7401519","title":"Predicting minimum inhibitory concentration of antimicrobial peptides by the pseudo-amino acid composition and Gaussian kernel regression","display_name":"Predicting minimum inhibitory concentration of antimicrobial peptides by the pseudo-amino acid composition and Gaussian kernel regression","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2289512951","doi":"https://doi.org/10.1109/bmei.2015.7401519","mag":"2289512951"},"language":"en","primary_location":{"id":"doi:10.1109/bmei.2015.7401519","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bmei.2015.7401519","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 8th International Conference on Biomedical Engineering and Informatics (BMEI)","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/A5033602780","display_name":"Xuan Xiao","orcid":"https://orcid.org/0000-0003-1016-7544"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xuan Xiao","raw_affiliation_strings":["Computer Department, Jing-De-Zhen Institute, Jing-De-Zhen, China"],"affiliations":[{"raw_affiliation_string":"Computer Department, Jing-De-Zhen Institute, Jing-De-Zhen, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111630896","display_name":"Zhi\u2010Bing You","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhi-Bing You","raw_affiliation_strings":["Computer Department, Jing-De-Zhen Institute, Jing-De-Zhen, China"],"affiliations":[{"raw_affiliation_string":"Computer Department, Jing-De-Zhen Institute, Jing-De-Zhen, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5033602780"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.09863605,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"301","last_page":"305"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9998000264167786,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9998000264167786,"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/T11103","display_name":"Antimicrobial Peptides and Activities","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2404","display_name":"Microbiology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12576","display_name":"vaccines and immunoinformatics approaches","score":0.9922999739646912,"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/minimum-inhibitory-concentration","display_name":"Minimum inhibitory concentration","score":0.6179152727127075},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5610511302947998},{"id":"https://openalex.org/keywords/antimicrobial","display_name":"Antimicrobial","score":0.5345311164855957},{"id":"https://openalex.org/keywords/amino-acid","display_name":"Amino acid","score":0.45392391085624695},{"id":"https://openalex.org/keywords/inhibitory-postsynaptic-potential","display_name":"Inhibitory postsynaptic potential","score":0.4477354884147644},{"id":"https://openalex.org/keywords/composition","display_name":"Composition (language)","score":0.43930545449256897},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4304659962654114},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.41113996505737305},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3932705521583557},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.3801751732826233},{"id":"https://openalex.org/keywords/biological-system","display_name":"Biological system","score":0.3455794155597687},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32923901081085205},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.32672062516212463},{"id":"https://openalex.org/keywords/biochemistry","display_name":"Biochemistry","score":0.2898569405078888},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.22381779551506042},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.12600097060203552},{"id":"https://openalex.org/keywords/organic-chemistry","display_name":"Organic chemistry","score":0.12294545769691467},{"id":"https://openalex.org/keywords/endocrinology","display_name":"Endocrinology","score":0.07507404685020447}],"concepts":[{"id":"https://openalex.org/C176947019","wikidata":"https://www.wikidata.org/wiki/Q597889","display_name":"Minimum inhibitory concentration","level":3,"score":0.6179152727127075},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5610511302947998},{"id":"https://openalex.org/C4937899","wikidata":"https://www.wikidata.org/wiki/Q68541106","display_name":"Antimicrobial","level":2,"score":0.5345311164855957},{"id":"https://openalex.org/C515207424","wikidata":"https://www.wikidata.org/wiki/Q8066","display_name":"Amino acid","level":2,"score":0.45392391085624695},{"id":"https://openalex.org/C17077164","wikidata":"https://www.wikidata.org/wiki/Q1185869","display_name":"Inhibitory postsynaptic potential","level":2,"score":0.4477354884147644},{"id":"https://openalex.org/C40231798","wikidata":"https://www.wikidata.org/wiki/Q1333743","display_name":"Composition (language)","level":2,"score":0.43930545449256897},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4304659962654114},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41113996505737305},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3932705521583557},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.3801751732826233},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.3455794155597687},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32923901081085205},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32672062516212463},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.2898569405078888},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.22381779551506042},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.12600097060203552},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.12294545769691467},{"id":"https://openalex.org/C134018914","wikidata":"https://www.wikidata.org/wiki/Q162606","display_name":"Endocrinology","level":1,"score":0.07507404685020447},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bmei.2015.7401519","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bmei.2015.7401519","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 8th International Conference on Biomedical Engineering and Informatics (BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W576256462","https://openalex.org/W1977241485","https://openalex.org/W2020598029","https://openalex.org/W2034070267","https://openalex.org/W2035498051","https://openalex.org/W2065845206","https://openalex.org/W2115622227","https://openalex.org/W2132963724","https://openalex.org/W2168765007","https://openalex.org/W2362999435","https://openalex.org/W4256716988"],"related_works":["https://openalex.org/W2974163011","https://openalex.org/W2037381935","https://openalex.org/W2054589298","https://openalex.org/W2436760910","https://openalex.org/W2343911421","https://openalex.org/W2029364535","https://openalex.org/W4320505838","https://openalex.org/W2332184606","https://openalex.org/W4248680266","https://openalex.org/W2048731896"],"abstract_inverted_index":{"Antimicrobial":[0],"peptides":[1,157,164],"(AMPs),":[2],"which":[3,16,73,163],"is":[4,31,62,70,82,107,138,200],"a":[5,12,63,111],"kind":[6,64],"of":[7,29,37,65,78,102,119,128,187,194],"short":[8],"chain":[9],"protein,":[10],"have":[11,17,39],"strong":[13],"antimicrobial":[14,95,104,156],"ability":[15],"antibacterial,":[18],"antifungal,":[19],"antiviral":[20],"effect.":[21],"Over":[22],"the":[23,27,42,51,57,71,76,79,91,100,117,126,139,185,188,192,195],"last":[24],"few":[25],"decades,":[26],"research":[28],"AMPs":[30,47,52],"drawing":[32],"in":[33,41,50,85,162],"large":[34],"scholars,":[35],"many":[36],"whom":[38],"engaged":[40],"profound":[43],"study":[44],"on":[45,121],"predicting":[46],"activity,":[48],"particularly":[49],"classification.":[53],"According":[54,183],"to":[55,88,94,98,125,144,184],"microbiology,":[56],"minimum":[58],"inhibitory":[59],"concentration":[60,69],"(MIC)":[61],"antibacterial":[66],"agent,":[67],"its":[68],"lowest,":[72],"can":[74],"inhibit":[75],"growth":[77],"microorganism.":[80],"MIC":[81,149,158,189],"very":[83],"crucial":[84],"diagnostic":[86],"lab":[87],"prove":[89],"that":[90,191],"microbial":[92],"resistance":[93,120],"agents,":[96],"and":[97,133,142,179,197],"monitor":[99],"activity":[101,118],"new":[103],"agents.":[105],"It":[106],"generally":[108],"considered":[109],"as":[110],"most":[112],"basic":[113],"laboratory":[114],"for":[115],"measuring":[116],"living":[122],"organisms.":[123],"Due":[124],"process":[127],"biological":[129],"experiments":[130],"are":[131],"expensive":[132],"cost":[134],"plenty":[135],"time,":[136],"it":[137],"highest":[140],"favorable":[141],"practicable":[143],"design":[145],"an":[146,155],"efficacious":[147],"computer-based":[148],"prediction":[150],"method.":[151],"In":[152],"this":[153],"paper,":[154],"predictor":[159],"called":[160],"\"MIC\",":[161],"sequence":[165],"were":[166],"formulated":[167],"by":[168],"incorporating":[169],"five":[170],"physicochemical":[171],"properties":[172],"into":[173],"pseudo":[174],"amino":[175],"acid":[176],"composition":[177],"(PseAAC)":[178],"Gaussian":[180],"kernel":[181],"regression.":[182],"result":[186,193,199],"showed":[190],"method":[196],"experimentally":[198],"high":[201],"consistent.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-02-11T14:41:00.668223","created_date":"2025-10-10T00:00:00"}
