{"id":"https://openalex.org/W2020092573","doi":"https://doi.org/10.1145/2500863.2500865","title":"Heuristic approaches for time-lagged biclustering","display_name":"Heuristic approaches for time-lagged biclustering","publication_year":2013,"publication_date":"2013-08-01","ids":{"openalex":"https://openalex.org/W2020092573","doi":"https://doi.org/10.1145/2500863.2500865","mag":"2020092573"},"language":"en","primary_location":{"id":"doi:10.1145/2500863.2500865","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2500863.2500865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th International Workshop on Data Mining in Bioinformatics","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/A5017104430","display_name":"Joana P. Gon\u00e7alves","orcid":"https://orcid.org/0000-0001-6072-9627"},"institutions":[{"id":"https://openalex.org/I141596103","display_name":"University of Lisbon","ror":"https://ror.org/01c27hj86","country_code":"PT","type":"education","lineage":["https://openalex.org/I141596103"]},{"id":"https://openalex.org/I203847022","display_name":"Instituto Polit\u00e9cnico de Lisboa","ror":"https://ror.org/04ea70f07","country_code":"PT","type":"education","lineage":["https://openalex.org/I203847022"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Joana P. Gon\u00e7alves","raw_affiliation_strings":["Technical University of Lisbon, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Lisbon, Portugal","institution_ids":["https://openalex.org/I203847022","https://openalex.org/I141596103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089809829","display_name":"Sara C. Madeira","orcid":"https://orcid.org/0000-0002-1459-8096"},"institutions":[{"id":"https://openalex.org/I141596103","display_name":"University of Lisbon","ror":"https://ror.org/01c27hj86","country_code":"PT","type":"education","lineage":["https://openalex.org/I141596103"]},{"id":"https://openalex.org/I203847022","display_name":"Instituto Polit\u00e9cnico de Lisboa","ror":"https://ror.org/04ea70f07","country_code":"PT","type":"education","lineage":["https://openalex.org/I203847022"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Sara C. Madeira","raw_affiliation_strings":["Technical University of Lisbon, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Lisbon, Portugal","institution_ids":["https://openalex.org/I203847022","https://openalex.org/I141596103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1496,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5632993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.996999979019165,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.996999979019165,"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/T10621","display_name":"Gene Regulatory Network Analysis","score":0.995199978351593,"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/T10885","display_name":"Gene expression and cancer classification","score":0.994700014591217,"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/biclustering","display_name":"Biclustering","score":0.8202844858169556},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.742142379283905},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6509127020835876},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.6235640048980713},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5401278138160706},{"id":"https://openalex.org/keywords/time-complexity","display_name":"Time complexity","score":0.4751015901565552},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4523507058620453},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4385243058204651},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.404696524143219},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40440455079078674},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.15096935629844666},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.14955633878707886}],"concepts":[{"id":"https://openalex.org/C144817290","wikidata":"https://www.wikidata.org/wiki/Q2976575","display_name":"Biclustering","level":5,"score":0.8202844858169556},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.742142379283905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6509127020835876},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.6235640048980713},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5401278138160706},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.4751015901565552},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4523507058620453},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4385243058204651},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.404696524143219},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40440455079078674},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.15096935629844666},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.14955633878707886},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2500863.2500865","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2500863.2500865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th International Workshop on Data Mining in Bioinformatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G2910422460","display_name":null,"funder_award_id":"PEst-OE/EEI/LA0021/2013, PTDC/EIA-EIA/111239/2009","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"}],"funders":[{"id":"https://openalex.org/F4320334779","display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","ror":"https://ror.org/00snfqn58"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W938539187","https://openalex.org/W1942039323","https://openalex.org/W1990061958","https://openalex.org/W2037400115","https://openalex.org/W2055550250","https://openalex.org/W2061425021","https://openalex.org/W2096145311","https://openalex.org/W2103017472","https://openalex.org/W2111360319","https://openalex.org/W2133271603","https://openalex.org/W2137596615","https://openalex.org/W2137683543","https://openalex.org/W2144544802","https://openalex.org/W2144738674","https://openalex.org/W2150718242"],"related_works":["https://openalex.org/W1974340769","https://openalex.org/W2900595096","https://openalex.org/W4289277241","https://openalex.org/W2979322793","https://openalex.org/W2765801824","https://openalex.org/W2188068678","https://openalex.org/W2157302779","https://openalex.org/W4236723217","https://openalex.org/W1581805909","https://openalex.org/W2965402291"],"abstract_inverted_index":{"Identifying":[0],"patterns":[1,67,101,117,145,156],"in":[2,8,68,146],"temporal":[3],"data":[4],"supports":[5],"complex":[6],"analyses":[7],"several":[9],"domains,":[10],"including":[11],"stock":[12],"markets":[13],"(finance)":[14],"and":[15,21,55,108],"social":[16],"interactions":[17],"(social":[18],"science).":[19],"Clinical":[20],"biological":[22,53],"applications,":[23],"such":[24],"as":[25],"monitoring":[26],"patient":[27],"response":[28,88],"to":[29,46,61,83,104,127,161],"treatment":[30],"or":[31],"characterizing":[32],"activity":[33],"at":[34],"the":[35,50,64,80,122,128,139,152,162,185],"molecular":[36],"level,":[37],"are":[38,171],"also":[39],"of":[40,52,58,66,141,154,165],"interest.":[41],"In":[42],"particular,":[43],"researchers":[44],"seek":[45],"gain":[47],"insight":[48,120],"into":[49,121],"dynamics":[51],"processes,":[54],"potential":[56],"perturbations":[57],"these":[59],"leading":[60,126],"disease,":[62],"through":[63],"discovery":[65,140],"time":[69,96,149,182],"series":[70],"gene":[71,147],"expression":[72,148],"data.":[73],"For":[74],"many":[75],"years,":[76],"clustering":[77,91],"has":[78],"remained":[79],"standard":[81],"technique":[82],"group":[84],"genes":[85],"exhibiting":[86],"similar":[87],"profiles.":[89],"However,":[90],"defines":[92],"similarity":[93],"across":[94],"all":[95],"points,":[97],"focusing":[98],"on":[99],"global":[100],"which":[102],"tend":[103],"characterize":[105],"rather":[106,179],"broad":[107],"unspecific":[109],"responses.":[110],"It":[111],"is":[112],"widely":[113],"believed":[114],"that":[115],"local":[116,144],"offer":[118],"additional":[119],"underlying":[123],"intricate":[124],"events":[125],"overall":[129],"observed":[130],"behavior.":[131],"Efficient":[132],"biclustering":[133],"algorithms":[134],"have":[135],"been":[136],"devised":[137],"for":[138,184],"temporally":[142],"aligned":[143],"series,":[150],"but":[151],"extraction":[153],"time-lagged":[155],"remains":[157],"a":[158],"challenge":[159],"due":[160],"combinatorial":[163],"explosion":[164],"pattern":[166],"occurrence":[167],"combinations":[168],"when":[169],"delays":[170],"considered.":[172],"We":[173],"present":[174],"heuristic":[175],"approaches":[176],"enabling":[177],"polynomial":[178],"than":[180],"exponential":[181],"solutions":[183],"problem.":[186]},"counts_by_year":[{"year":2014,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}