Discovering pan-correlation patterns from time course data sets by efficient mining algorithms
Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Qian [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018 |
|---|
| Schlagwörter: |
|---|
| Systematik: |
|
|---|
| Anmerkung: |
© Springer-Verlag GmbH Austria, part of Springer Nature 2018 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Computing - Springer Vienna, 1966, 100(2018), 4 vom: 21. März, Seite 421-437 |
|---|---|
| Übergeordnetes Werk: |
volume:100 ; year:2018 ; number:4 ; day:21 ; month:03 ; pages:421-437 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00607-018-0606-9 |
|---|
| Katalog-ID: |
OLC206142967X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC206142967X | ||
| 003 | DE-627 | ||
| 005 | 20230502133814.0 | ||
| 007 | tu | ||
| 008 | 200819s2018 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00607-018-0606-9 |2 doi | |
| 035 | |a (DE-627)OLC206142967X | ||
| 035 | |a (DE-He213)s00607-018-0606-9-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 084 | |a SA 4220 |q VZ |2 rvk | ||
| 084 | |a SA 4220 |q VZ |2 rvk | ||
| 100 | 1 | |a Liu, Qian |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Discovering pan-correlation patterns from time course data sets by efficient mining algorithms |
| 264 | 1 | |c 2018 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag GmbH Austria, part of Springer Nature 2018 | ||
| 520 | |a Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely. | ||
| 650 | 4 | |a Pan-correlation pattern | |
| 650 | 4 | |a Time-course data | |
| 650 | 4 | |a Positive correlation patterns | |
| 650 | 4 | |a Negative correlation patterns | |
| 650 | 4 | |a Time-lagged positive correlation patterns | |
| 650 | 4 | |a Time-lagged negative correlation patterns | |
| 700 | 1 | |a Ghosh, Shameek |4 aut | |
| 700 | 1 | |a Li, Jinyan |4 aut | |
| 700 | 1 | |a Wong, Limsoon |4 aut | |
| 700 | 1 | |a Ramamohanarao, Kotagiri |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Computing |d Springer Vienna, 1966 |g 100(2018), 4 vom: 21. März, Seite 421-437 |w (DE-627)129534927 |w (DE-600)215907-7 |w (DE-576)014963949 |x 0010-485X |7 nnns |
| 773 | 1 | 8 | |g volume:100 |g year:2018 |g number:4 |g day:21 |g month:03 |g pages:421-437 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00607-018-0606-9 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OPC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_4323 | ||
| 936 | r | v | |a SA 4220 |
| 936 | r | v | |a SA 4220 |
| 951 | |a AR | ||
| 952 | |d 100 |j 2018 |e 4 |b 21 |c 03 |h 421-437 | ||
| author_variant |
q l ql s g sg j l jl l w lw k r kr |
|---|---|
| matchkey_str |
article:0010485X:2018----::icvrnpnorltoptenfotmcusdtstbef |
| hierarchy_sort_str |
2018 |
| publishDate |
2018 |
| allfields |
10.1007/s00607-018-0606-9 doi (DE-627)OLC206142967X (DE-He213)s00607-018-0606-9-p DE-627 ger DE-627 rakwb eng 004 VZ SA 4220 VZ rvk SA 4220 VZ rvk Liu, Qian verfasserin aut Discovering pan-correlation patterns from time course data sets by efficient mining algorithms 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2018 Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely. Pan-correlation pattern Time-course data Positive correlation patterns Negative correlation patterns Time-lagged positive correlation patterns Time-lagged negative correlation patterns Ghosh, Shameek aut Li, Jinyan aut Wong, Limsoon aut Ramamohanarao, Kotagiri aut Enthalten in Computing Springer Vienna, 1966 100(2018), 4 vom: 21. März, Seite 421-437 (DE-627)129534927 (DE-600)215907-7 (DE-576)014963949 0010-485X nnns volume:100 year:2018 number:4 day:21 month:03 pages:421-437 https://doi.org/10.1007/s00607-018-0606-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4323 SA 4220 SA 4220 AR 100 2018 4 21 03 421-437 |
| spelling |
10.1007/s00607-018-0606-9 doi (DE-627)OLC206142967X (DE-He213)s00607-018-0606-9-p DE-627 ger DE-627 rakwb eng 004 VZ SA 4220 VZ rvk SA 4220 VZ rvk Liu, Qian verfasserin aut Discovering pan-correlation patterns from time course data sets by efficient mining algorithms 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2018 Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely. Pan-correlation pattern Time-course data Positive correlation patterns Negative correlation patterns Time-lagged positive correlation patterns Time-lagged negative correlation patterns Ghosh, Shameek aut Li, Jinyan aut Wong, Limsoon aut Ramamohanarao, Kotagiri aut Enthalten in Computing Springer Vienna, 1966 100(2018), 4 vom: 21. März, Seite 421-437 (DE-627)129534927 (DE-600)215907-7 (DE-576)014963949 0010-485X nnns volume:100 year:2018 number:4 day:21 month:03 pages:421-437 https://doi.org/10.1007/s00607-018-0606-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4323 SA 4220 SA 4220 AR 100 2018 4 21 03 421-437 |
| allfields_unstemmed |
10.1007/s00607-018-0606-9 doi (DE-627)OLC206142967X (DE-He213)s00607-018-0606-9-p DE-627 ger DE-627 rakwb eng 004 VZ SA 4220 VZ rvk SA 4220 VZ rvk Liu, Qian verfasserin aut Discovering pan-correlation patterns from time course data sets by efficient mining algorithms 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2018 Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely. Pan-correlation pattern Time-course data Positive correlation patterns Negative correlation patterns Time-lagged positive correlation patterns Time-lagged negative correlation patterns Ghosh, Shameek aut Li, Jinyan aut Wong, Limsoon aut Ramamohanarao, Kotagiri aut Enthalten in Computing Springer Vienna, 1966 100(2018), 4 vom: 21. März, Seite 421-437 (DE-627)129534927 (DE-600)215907-7 (DE-576)014963949 0010-485X nnns volume:100 year:2018 number:4 day:21 month:03 pages:421-437 https://doi.org/10.1007/s00607-018-0606-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4323 SA 4220 SA 4220 AR 100 2018 4 21 03 421-437 |
| allfieldsGer |
10.1007/s00607-018-0606-9 doi (DE-627)OLC206142967X (DE-He213)s00607-018-0606-9-p DE-627 ger DE-627 rakwb eng 004 VZ SA 4220 VZ rvk SA 4220 VZ rvk Liu, Qian verfasserin aut Discovering pan-correlation patterns from time course data sets by efficient mining algorithms 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2018 Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely. Pan-correlation pattern Time-course data Positive correlation patterns Negative correlation patterns Time-lagged positive correlation patterns Time-lagged negative correlation patterns Ghosh, Shameek aut Li, Jinyan aut Wong, Limsoon aut Ramamohanarao, Kotagiri aut Enthalten in Computing Springer Vienna, 1966 100(2018), 4 vom: 21. März, Seite 421-437 (DE-627)129534927 (DE-600)215907-7 (DE-576)014963949 0010-485X nnns volume:100 year:2018 number:4 day:21 month:03 pages:421-437 https://doi.org/10.1007/s00607-018-0606-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4323 SA 4220 SA 4220 AR 100 2018 4 21 03 421-437 |
| allfieldsSound |
10.1007/s00607-018-0606-9 doi (DE-627)OLC206142967X (DE-He213)s00607-018-0606-9-p DE-627 ger DE-627 rakwb eng 004 VZ SA 4220 VZ rvk SA 4220 VZ rvk Liu, Qian verfasserin aut Discovering pan-correlation patterns from time course data sets by efficient mining algorithms 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2018 Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely. Pan-correlation pattern Time-course data Positive correlation patterns Negative correlation patterns Time-lagged positive correlation patterns Time-lagged negative correlation patterns Ghosh, Shameek aut Li, Jinyan aut Wong, Limsoon aut Ramamohanarao, Kotagiri aut Enthalten in Computing Springer Vienna, 1966 100(2018), 4 vom: 21. März, Seite 421-437 (DE-627)129534927 (DE-600)215907-7 (DE-576)014963949 0010-485X nnns volume:100 year:2018 number:4 day:21 month:03 pages:421-437 https://doi.org/10.1007/s00607-018-0606-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4323 SA 4220 SA 4220 AR 100 2018 4 21 03 421-437 |
| language |
English |
| source |
Enthalten in Computing 100(2018), 4 vom: 21. März, Seite 421-437 volume:100 year:2018 number:4 day:21 month:03 pages:421-437 |
| sourceStr |
Enthalten in Computing 100(2018), 4 vom: 21. März, Seite 421-437 volume:100 year:2018 number:4 day:21 month:03 pages:421-437 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Pan-correlation pattern Time-course data Positive correlation patterns Negative correlation patterns Time-lagged positive correlation patterns Time-lagged negative correlation patterns |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Computing |
| authorswithroles_txt_mv |
Liu, Qian @@aut@@ Ghosh, Shameek @@aut@@ Li, Jinyan @@aut@@ Wong, Limsoon @@aut@@ Ramamohanarao, Kotagiri @@aut@@ |
| publishDateDaySort_date |
2018-03-21T00:00:00Z |
| hierarchy_top_id |
129534927 |
| dewey-sort |
14 |
| id |
OLC206142967X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC206142967X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502133814.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00607-018-0606-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC206142967X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00607-018-0606-9-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 4220</subfield><subfield code="q">VZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 4220</subfield><subfield code="q">VZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Qian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Discovering pan-correlation patterns from time course data sets by efficient mining algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Austria, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pan-correlation pattern</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time-course data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Positive correlation patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Negative correlation patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time-lagged positive correlation patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time-lagged negative correlation patterns</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ghosh, Shameek</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jinyan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wong, Limsoon</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ramamohanarao, Kotagiri</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computing</subfield><subfield code="d">Springer Vienna, 1966</subfield><subfield code="g">100(2018), 4 vom: 21. März, Seite 421-437</subfield><subfield code="w">(DE-627)129534927</subfield><subfield code="w">(DE-600)215907-7</subfield><subfield code="w">(DE-576)014963949</subfield><subfield code="x">0010-485X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:100</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:4</subfield><subfield code="g">day:21</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:421-437</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00607-018-0606-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 4220</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 4220</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">100</subfield><subfield code="j">2018</subfield><subfield code="e">4</subfield><subfield code="b">21</subfield><subfield code="c">03</subfield><subfield code="h">421-437</subfield></datafield></record></collection>
|
| author |
Liu, Qian |
| spellingShingle |
Liu, Qian ddc 004 rvk SA 4220 misc Pan-correlation pattern misc Time-course data misc Positive correlation patterns misc Negative correlation patterns misc Time-lagged positive correlation patterns misc Time-lagged negative correlation patterns Discovering pan-correlation patterns from time course data sets by efficient mining algorithms |
| authorStr |
Liu, Qian |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129534927 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0010-485X |
| topic_title |
004 VZ SA 4220 VZ rvk Discovering pan-correlation patterns from time course data sets by efficient mining algorithms Pan-correlation pattern Time-course data Positive correlation patterns Negative correlation patterns Time-lagged positive correlation patterns Time-lagged negative correlation patterns |
| topic |
ddc 004 rvk SA 4220 misc Pan-correlation pattern misc Time-course data misc Positive correlation patterns misc Negative correlation patterns misc Time-lagged positive correlation patterns misc Time-lagged negative correlation patterns |
| topic_unstemmed |
ddc 004 rvk SA 4220 misc Pan-correlation pattern misc Time-course data misc Positive correlation patterns misc Negative correlation patterns misc Time-lagged positive correlation patterns misc Time-lagged negative correlation patterns |
| topic_browse |
ddc 004 rvk SA 4220 misc Pan-correlation pattern misc Time-course data misc Positive correlation patterns misc Negative correlation patterns misc Time-lagged positive correlation patterns misc Time-lagged negative correlation patterns |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Computing |
| hierarchy_parent_id |
129534927 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Computing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129534927 (DE-600)215907-7 (DE-576)014963949 |
| title |
Discovering pan-correlation patterns from time course data sets by efficient mining algorithms |
| ctrlnum |
(DE-627)OLC206142967X (DE-He213)s00607-018-0606-9-p |
| title_full |
Discovering pan-correlation patterns from time course data sets by efficient mining algorithms |
| author_sort |
Liu, Qian |
| journal |
Computing |
| journalStr |
Computing |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2018 |
| contenttype_str_mv |
txt |
| container_start_page |
421 |
| author_browse |
Liu, Qian Ghosh, Shameek Li, Jinyan Wong, Limsoon Ramamohanarao, Kotagiri |
| container_volume |
100 |
| class |
004 VZ SA 4220 VZ rvk |
| format_se |
Aufsätze |
| author-letter |
Liu, Qian |
| doi_str_mv |
10.1007/s00607-018-0606-9 |
| dewey-full |
004 |
| title_sort |
discovering pan-correlation patterns from time course data sets by efficient mining algorithms |
| title_auth |
Discovering pan-correlation patterns from time course data sets by efficient mining algorithms |
| abstract |
Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely. © Springer-Verlag GmbH Austria, part of Springer Nature 2018 |
| abstractGer |
Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely. © Springer-Verlag GmbH Austria, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely. © Springer-Verlag GmbH Austria, part of Springer Nature 2018 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4323 |
| container_issue |
4 |
| title_short |
Discovering pan-correlation patterns from time course data sets by efficient mining algorithms |
| url |
https://doi.org/10.1007/s00607-018-0606-9 |
| remote_bool |
false |
| author2 |
Ghosh, Shameek Li, Jinyan Wong, Limsoon Ramamohanarao, Kotagiri |
| author2Str |
Ghosh, Shameek Li, Jinyan Wong, Limsoon Ramamohanarao, Kotagiri |
| ppnlink |
129534927 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00607-018-0606-9 |
| up_date |
2024-07-04T03:34:39.689Z |
| _version_ |
1803617912440750080 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC206142967X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502133814.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00607-018-0606-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC206142967X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00607-018-0606-9-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 4220</subfield><subfield code="q">VZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 4220</subfield><subfield code="q">VZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Qian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Discovering pan-correlation patterns from time course data sets by efficient mining algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Austria, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pan-correlation pattern</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time-course data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Positive correlation patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Negative correlation patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time-lagged positive correlation patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time-lagged negative correlation patterns</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ghosh, Shameek</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jinyan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wong, Limsoon</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ramamohanarao, Kotagiri</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computing</subfield><subfield code="d">Springer Vienna, 1966</subfield><subfield code="g">100(2018), 4 vom: 21. März, Seite 421-437</subfield><subfield code="w">(DE-627)129534927</subfield><subfield code="w">(DE-600)215907-7</subfield><subfield code="w">(DE-576)014963949</subfield><subfield code="x">0010-485X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:100</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:4</subfield><subfield code="g">day:21</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:421-437</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00607-018-0606-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 4220</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 4220</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">100</subfield><subfield code="j">2018</subfield><subfield code="e">4</subfield><subfield code="b">21</subfield><subfield code="c">03</subfield><subfield code="h">421-437</subfield></datafield></record></collection>
|
| score |
7.3980417 |