Co-occurrence pattern mining based on a biological approximation scoring matrix
Abstract Mining co-occurrence frequency patterns from multiple sequences is a hot topic in bioinformatics. Many seemingly disorganized constituents repetitively appear under different biological matrices, such as PAM250 and BLOSUM62, which are considered hidden frequent patterns (FPs). A hidden FP w...
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Gespeichert in:
| Autor*in: |
Guo, Dan [verfasserIn] Yuan, Ermao [verfasserIn] Hu, Xuegang [verfasserIn] Wu, Xindong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Pattern Analysis & Applications - Springer-Verlag, 1999, 21(2017), 4 vom: 28. Feb., Seite 977-996 |
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volume:21 ; year:2017 ; number:4 ; day:28 ; month:02 ; pages:977-996 |
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| DOI / URN: |
10.1007/s10044-017-0609-8 |
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| 520 | |a Abstract Mining co-occurrence frequency patterns from multiple sequences is a hot topic in bioinformatics. Many seemingly disorganized constituents repetitively appear under different biological matrices, such as PAM250 and BLOSUM62, which are considered hidden frequent patterns (FPs). A hidden FP with both gap and flexible approximation operations (replacement, deletion or insertion) deepens the difficulty in discovering its true occurrences. To effectively discover co-occurrence FPs (Co-FPs) under these conditions, we design a mining algorithm (co-fp-miner) using the following steps: (1) a biological approximation scoring matrix is designed to discover various deformations of a single FP pattern; (2) a data-driven intersection tactic is used to generate candidate Co-FPs; (3) a deterministic Apriori-like rule is proposed to prune unnecessary Co-FPs; and (4) finally, we employ a backtracking matching scheme to validate true Co-FPs. The co-fp-miner algorithm is an unified framework for both exact and approximate mining on multiple sequences. Experiments on DNA and protein sequences demonstrate that co-fp-miner is more efficient on solutions, time and memory consumption than that of other peers. | ||
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| 700 | 1 | |a Hu, Xuegang |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Xindong |e verfasserin |4 aut | |
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10.1007/s10044-017-0609-8 doi (DE-627)SPR008217564 (SPR)s10044-017-0609-8-e DE-627 ger DE-627 rakwb eng Guo, Dan verfasserin aut Co-occurrence pattern mining based on a biological approximation scoring matrix 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mining co-occurrence frequency patterns from multiple sequences is a hot topic in bioinformatics. Many seemingly disorganized constituents repetitively appear under different biological matrices, such as PAM250 and BLOSUM62, which are considered hidden frequent patterns (FPs). A hidden FP with both gap and flexible approximation operations (replacement, deletion or insertion) deepens the difficulty in discovering its true occurrences. To effectively discover co-occurrence FPs (Co-FPs) under these conditions, we design a mining algorithm (co-fp-miner) using the following steps: (1) a biological approximation scoring matrix is designed to discover various deformations of a single FP pattern; (2) a data-driven intersection tactic is used to generate candidate Co-FPs; (3) a deterministic Apriori-like rule is proposed to prune unnecessary Co-FPs; and (4) finally, we employ a backtracking matching scheme to validate true Co-FPs. The co-fp-miner algorithm is an unified framework for both exact and approximate mining on multiple sequences. Experiments on DNA and protein sequences demonstrate that co-fp-miner is more efficient on solutions, time and memory consumption than that of other peers. Co-occurrence pattern (dpeaa)DE-He213 Pattern mining (dpeaa)DE-He213 Approximate (dpeaa)DE-He213 Gap (dpeaa)DE-He213 Edit distance matrix (dpeaa)DE-He213 Yuan, Ermao verfasserin aut Hu, Xuegang verfasserin aut Wu, Xindong verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 21(2017), 4 vom: 28. Feb., Seite 977-996 (DE-627)SPR008209189 nnns volume:21 year:2017 number:4 day:28 month:02 pages:977-996 https://dx.doi.org/10.1007/s10044-017-0609-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2017 4 28 02 977-996 |
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10.1007/s10044-017-0609-8 doi (DE-627)SPR008217564 (SPR)s10044-017-0609-8-e DE-627 ger DE-627 rakwb eng Guo, Dan verfasserin aut Co-occurrence pattern mining based on a biological approximation scoring matrix 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mining co-occurrence frequency patterns from multiple sequences is a hot topic in bioinformatics. Many seemingly disorganized constituents repetitively appear under different biological matrices, such as PAM250 and BLOSUM62, which are considered hidden frequent patterns (FPs). A hidden FP with both gap and flexible approximation operations (replacement, deletion or insertion) deepens the difficulty in discovering its true occurrences. To effectively discover co-occurrence FPs (Co-FPs) under these conditions, we design a mining algorithm (co-fp-miner) using the following steps: (1) a biological approximation scoring matrix is designed to discover various deformations of a single FP pattern; (2) a data-driven intersection tactic is used to generate candidate Co-FPs; (3) a deterministic Apriori-like rule is proposed to prune unnecessary Co-FPs; and (4) finally, we employ a backtracking matching scheme to validate true Co-FPs. The co-fp-miner algorithm is an unified framework for both exact and approximate mining on multiple sequences. Experiments on DNA and protein sequences demonstrate that co-fp-miner is more efficient on solutions, time and memory consumption than that of other peers. Co-occurrence pattern (dpeaa)DE-He213 Pattern mining (dpeaa)DE-He213 Approximate (dpeaa)DE-He213 Gap (dpeaa)DE-He213 Edit distance matrix (dpeaa)DE-He213 Yuan, Ermao verfasserin aut Hu, Xuegang verfasserin aut Wu, Xindong verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 21(2017), 4 vom: 28. Feb., Seite 977-996 (DE-627)SPR008209189 nnns volume:21 year:2017 number:4 day:28 month:02 pages:977-996 https://dx.doi.org/10.1007/s10044-017-0609-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2017 4 28 02 977-996 |
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10.1007/s10044-017-0609-8 doi (DE-627)SPR008217564 (SPR)s10044-017-0609-8-e DE-627 ger DE-627 rakwb eng Guo, Dan verfasserin aut Co-occurrence pattern mining based on a biological approximation scoring matrix 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mining co-occurrence frequency patterns from multiple sequences is a hot topic in bioinformatics. Many seemingly disorganized constituents repetitively appear under different biological matrices, such as PAM250 and BLOSUM62, which are considered hidden frequent patterns (FPs). A hidden FP with both gap and flexible approximation operations (replacement, deletion or insertion) deepens the difficulty in discovering its true occurrences. To effectively discover co-occurrence FPs (Co-FPs) under these conditions, we design a mining algorithm (co-fp-miner) using the following steps: (1) a biological approximation scoring matrix is designed to discover various deformations of a single FP pattern; (2) a data-driven intersection tactic is used to generate candidate Co-FPs; (3) a deterministic Apriori-like rule is proposed to prune unnecessary Co-FPs; and (4) finally, we employ a backtracking matching scheme to validate true Co-FPs. The co-fp-miner algorithm is an unified framework for both exact and approximate mining on multiple sequences. Experiments on DNA and protein sequences demonstrate that co-fp-miner is more efficient on solutions, time and memory consumption than that of other peers. Co-occurrence pattern (dpeaa)DE-He213 Pattern mining (dpeaa)DE-He213 Approximate (dpeaa)DE-He213 Gap (dpeaa)DE-He213 Edit distance matrix (dpeaa)DE-He213 Yuan, Ermao verfasserin aut Hu, Xuegang verfasserin aut Wu, Xindong verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 21(2017), 4 vom: 28. Feb., Seite 977-996 (DE-627)SPR008209189 nnns volume:21 year:2017 number:4 day:28 month:02 pages:977-996 https://dx.doi.org/10.1007/s10044-017-0609-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2017 4 28 02 977-996 |
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10.1007/s10044-017-0609-8 doi (DE-627)SPR008217564 (SPR)s10044-017-0609-8-e DE-627 ger DE-627 rakwb eng Guo, Dan verfasserin aut Co-occurrence pattern mining based on a biological approximation scoring matrix 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mining co-occurrence frequency patterns from multiple sequences is a hot topic in bioinformatics. Many seemingly disorganized constituents repetitively appear under different biological matrices, such as PAM250 and BLOSUM62, which are considered hidden frequent patterns (FPs). A hidden FP with both gap and flexible approximation operations (replacement, deletion or insertion) deepens the difficulty in discovering its true occurrences. To effectively discover co-occurrence FPs (Co-FPs) under these conditions, we design a mining algorithm (co-fp-miner) using the following steps: (1) a biological approximation scoring matrix is designed to discover various deformations of a single FP pattern; (2) a data-driven intersection tactic is used to generate candidate Co-FPs; (3) a deterministic Apriori-like rule is proposed to prune unnecessary Co-FPs; and (4) finally, we employ a backtracking matching scheme to validate true Co-FPs. The co-fp-miner algorithm is an unified framework for both exact and approximate mining on multiple sequences. Experiments on DNA and protein sequences demonstrate that co-fp-miner is more efficient on solutions, time and memory consumption than that of other peers. Co-occurrence pattern (dpeaa)DE-He213 Pattern mining (dpeaa)DE-He213 Approximate (dpeaa)DE-He213 Gap (dpeaa)DE-He213 Edit distance matrix (dpeaa)DE-He213 Yuan, Ermao verfasserin aut Hu, Xuegang verfasserin aut Wu, Xindong verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 21(2017), 4 vom: 28. Feb., Seite 977-996 (DE-627)SPR008209189 nnns volume:21 year:2017 number:4 day:28 month:02 pages:977-996 https://dx.doi.org/10.1007/s10044-017-0609-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2017 4 28 02 977-996 |
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10.1007/s10044-017-0609-8 doi (DE-627)SPR008217564 (SPR)s10044-017-0609-8-e DE-627 ger DE-627 rakwb eng Guo, Dan verfasserin aut Co-occurrence pattern mining based on a biological approximation scoring matrix 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mining co-occurrence frequency patterns from multiple sequences is a hot topic in bioinformatics. Many seemingly disorganized constituents repetitively appear under different biological matrices, such as PAM250 and BLOSUM62, which are considered hidden frequent patterns (FPs). A hidden FP with both gap and flexible approximation operations (replacement, deletion or insertion) deepens the difficulty in discovering its true occurrences. To effectively discover co-occurrence FPs (Co-FPs) under these conditions, we design a mining algorithm (co-fp-miner) using the following steps: (1) a biological approximation scoring matrix is designed to discover various deformations of a single FP pattern; (2) a data-driven intersection tactic is used to generate candidate Co-FPs; (3) a deterministic Apriori-like rule is proposed to prune unnecessary Co-FPs; and (4) finally, we employ a backtracking matching scheme to validate true Co-FPs. The co-fp-miner algorithm is an unified framework for both exact and approximate mining on multiple sequences. Experiments on DNA and protein sequences demonstrate that co-fp-miner is more efficient on solutions, time and memory consumption than that of other peers. Co-occurrence pattern (dpeaa)DE-He213 Pattern mining (dpeaa)DE-He213 Approximate (dpeaa)DE-He213 Gap (dpeaa)DE-He213 Edit distance matrix (dpeaa)DE-He213 Yuan, Ermao verfasserin aut Hu, Xuegang verfasserin aut Wu, Xindong verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 21(2017), 4 vom: 28. Feb., Seite 977-996 (DE-627)SPR008209189 nnns volume:21 year:2017 number:4 day:28 month:02 pages:977-996 https://dx.doi.org/10.1007/s10044-017-0609-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2017 4 28 02 977-996 |
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Abstract Mining co-occurrence frequency patterns from multiple sequences is a hot topic in bioinformatics. Many seemingly disorganized constituents repetitively appear under different biological matrices, such as PAM250 and BLOSUM62, which are considered hidden frequent patterns (FPs). A hidden FP with both gap and flexible approximation operations (replacement, deletion or insertion) deepens the difficulty in discovering its true occurrences. To effectively discover co-occurrence FPs (Co-FPs) under these conditions, we design a mining algorithm (co-fp-miner) using the following steps: (1) a biological approximation scoring matrix is designed to discover various deformations of a single FP pattern; (2) a data-driven intersection tactic is used to generate candidate Co-FPs; (3) a deterministic Apriori-like rule is proposed to prune unnecessary Co-FPs; and (4) finally, we employ a backtracking matching scheme to validate true Co-FPs. The co-fp-miner algorithm is an unified framework for both exact and approximate mining on multiple sequences. Experiments on DNA and protein sequences demonstrate that co-fp-miner is more efficient on solutions, time and memory consumption than that of other peers. |
| abstractGer |
Abstract Mining co-occurrence frequency patterns from multiple sequences is a hot topic in bioinformatics. Many seemingly disorganized constituents repetitively appear under different biological matrices, such as PAM250 and BLOSUM62, which are considered hidden frequent patterns (FPs). A hidden FP with both gap and flexible approximation operations (replacement, deletion or insertion) deepens the difficulty in discovering its true occurrences. To effectively discover co-occurrence FPs (Co-FPs) under these conditions, we design a mining algorithm (co-fp-miner) using the following steps: (1) a biological approximation scoring matrix is designed to discover various deformations of a single FP pattern; (2) a data-driven intersection tactic is used to generate candidate Co-FPs; (3) a deterministic Apriori-like rule is proposed to prune unnecessary Co-FPs; and (4) finally, we employ a backtracking matching scheme to validate true Co-FPs. The co-fp-miner algorithm is an unified framework for both exact and approximate mining on multiple sequences. Experiments on DNA and protein sequences demonstrate that co-fp-miner is more efficient on solutions, time and memory consumption than that of other peers. |
| abstract_unstemmed |
Abstract Mining co-occurrence frequency patterns from multiple sequences is a hot topic in bioinformatics. Many seemingly disorganized constituents repetitively appear under different biological matrices, such as PAM250 and BLOSUM62, which are considered hidden frequent patterns (FPs). A hidden FP with both gap and flexible approximation operations (replacement, deletion or insertion) deepens the difficulty in discovering its true occurrences. To effectively discover co-occurrence FPs (Co-FPs) under these conditions, we design a mining algorithm (co-fp-miner) using the following steps: (1) a biological approximation scoring matrix is designed to discover various deformations of a single FP pattern; (2) a data-driven intersection tactic is used to generate candidate Co-FPs; (3) a deterministic Apriori-like rule is proposed to prune unnecessary Co-FPs; and (4) finally, we employ a backtracking matching scheme to validate true Co-FPs. The co-fp-miner algorithm is an unified framework for both exact and approximate mining on multiple sequences. Experiments on DNA and protein sequences demonstrate that co-fp-miner is more efficient on solutions, time and memory consumption than that of other peers. |
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Co-occurrence pattern mining based on a biological approximation scoring matrix |
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https://dx.doi.org/10.1007/s10044-017-0609-8 |
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Yuan, Ermao Hu, Xuegang Wu, Xindong |
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10.1007/s10044-017-0609-8 |
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