Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses
As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ni, Haiming [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
|---|
| Schlagwörter: |
Frequency chaos game representation |
|---|
| Übergeordnetes Werk: |
Enthalten in: Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia - Chiu, Philip W. ELSEVIER, 2015, New York, NY [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:107 ; year:2021 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jmgm.2021.107942 |
|---|
| Katalog-ID: |
ELV054690897 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV054690897 | ||
| 003 | DE-627 | ||
| 005 | 20230626040622.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210910s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.jmgm.2021.107942 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001460.pica |
| 035 | |a (DE-627)ELV054690897 | ||
| 035 | |a (ELSEVIER)S1093-3263(21)00113-3 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 082 | 0 | 4 | |a 600 |a 670 |q VZ |
| 084 | |a 51.00 |2 bkl | ||
| 100 | 1 | |a Ni, Haiming |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses |
| 264 | 1 | |c 2021transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. | ||
| 520 | |a As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. | ||
| 650 | 7 | |a Genome sequences alignment |2 Elsevier | |
| 650 | 7 | |a Frequency chaos game representation |2 Elsevier | |
| 650 | 7 | |a Phylogenetic tree construction |2 Elsevier | |
| 650 | 7 | |a Perceptual image hashing |2 Elsevier | |
| 700 | 1 | |a Mu, Hongbo |4 oth | |
| 700 | 1 | |a Qi, Dawei |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Chiu, Philip W. ELSEVIER |t Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia |d 2015 |g New York, NY [u.a.] |w (DE-627)ELV013464361 |
| 773 | 1 | 8 | |g volume:107 |g year:2021 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.jmgm.2021.107942 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a GBV_ILN_11 | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_72 | ||
| 912 | |a GBV_ILN_206 | ||
| 912 | |a GBV_ILN_257 | ||
| 912 | |a GBV_ILN_791 | ||
| 912 | |a GBV_ILN_2001 | ||
| 912 | |a GBV_ILN_2002 | ||
| 912 | |a GBV_ILN_2007 | ||
| 912 | |a GBV_ILN_2011 | ||
| 912 | |a GBV_ILN_2012 | ||
| 912 | |a GBV_ILN_2039 | ||
| 912 | |a GBV_ILN_2069 | ||
| 912 | |a GBV_ILN_2127 | ||
| 912 | |a GBV_ILN_2227 | ||
| 936 | b | k | |a 51.00 |j Werkstoffkunde: Allgemeines |q VZ |
| 951 | |a AR | ||
| 952 | |d 107 |j 2021 |h 0 | ||
| author_variant |
h n hn |
|---|---|
| matchkey_str |
nihaimingmuhongboqidawei:2021----:pligrqeccasaeersnainihecpulmghsigoeee |
| hierarchy_sort_str |
2021transfer abstract |
| bklnumber |
51.00 |
| publishDate |
2021 |
| allfields |
10.1016/j.jmgm.2021.107942 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001460.pica (DE-627)ELV054690897 (ELSEVIER)S1093-3263(21)00113-3 DE-627 ger DE-627 rakwb eng 610 VZ 600 670 VZ 51.00 bkl Ni, Haiming verfasserin aut Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. Genome sequences alignment Elsevier Frequency chaos game representation Elsevier Phylogenetic tree construction Elsevier Perceptual image hashing Elsevier Mu, Hongbo oth Qi, Dawei oth Enthalten in Elsevier Chiu, Philip W. ELSEVIER Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia 2015 New York, NY [u.a.] (DE-627)ELV013464361 volume:107 year:2021 pages:0 https://doi.org/10.1016/j.jmgm.2021.107942 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_11 GBV_ILN_20 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_72 GBV_ILN_206 GBV_ILN_257 GBV_ILN_791 GBV_ILN_2001 GBV_ILN_2002 GBV_ILN_2007 GBV_ILN_2011 GBV_ILN_2012 GBV_ILN_2039 GBV_ILN_2069 GBV_ILN_2127 GBV_ILN_2227 51.00 Werkstoffkunde: Allgemeines VZ AR 107 2021 0 |
| spelling |
10.1016/j.jmgm.2021.107942 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001460.pica (DE-627)ELV054690897 (ELSEVIER)S1093-3263(21)00113-3 DE-627 ger DE-627 rakwb eng 610 VZ 600 670 VZ 51.00 bkl Ni, Haiming verfasserin aut Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. Genome sequences alignment Elsevier Frequency chaos game representation Elsevier Phylogenetic tree construction Elsevier Perceptual image hashing Elsevier Mu, Hongbo oth Qi, Dawei oth Enthalten in Elsevier Chiu, Philip W. ELSEVIER Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia 2015 New York, NY [u.a.] (DE-627)ELV013464361 volume:107 year:2021 pages:0 https://doi.org/10.1016/j.jmgm.2021.107942 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_11 GBV_ILN_20 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_72 GBV_ILN_206 GBV_ILN_257 GBV_ILN_791 GBV_ILN_2001 GBV_ILN_2002 GBV_ILN_2007 GBV_ILN_2011 GBV_ILN_2012 GBV_ILN_2039 GBV_ILN_2069 GBV_ILN_2127 GBV_ILN_2227 51.00 Werkstoffkunde: Allgemeines VZ AR 107 2021 0 |
| allfields_unstemmed |
10.1016/j.jmgm.2021.107942 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001460.pica (DE-627)ELV054690897 (ELSEVIER)S1093-3263(21)00113-3 DE-627 ger DE-627 rakwb eng 610 VZ 600 670 VZ 51.00 bkl Ni, Haiming verfasserin aut Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. Genome sequences alignment Elsevier Frequency chaos game representation Elsevier Phylogenetic tree construction Elsevier Perceptual image hashing Elsevier Mu, Hongbo oth Qi, Dawei oth Enthalten in Elsevier Chiu, Philip W. ELSEVIER Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia 2015 New York, NY [u.a.] (DE-627)ELV013464361 volume:107 year:2021 pages:0 https://doi.org/10.1016/j.jmgm.2021.107942 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_11 GBV_ILN_20 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_72 GBV_ILN_206 GBV_ILN_257 GBV_ILN_791 GBV_ILN_2001 GBV_ILN_2002 GBV_ILN_2007 GBV_ILN_2011 GBV_ILN_2012 GBV_ILN_2039 GBV_ILN_2069 GBV_ILN_2127 GBV_ILN_2227 51.00 Werkstoffkunde: Allgemeines VZ AR 107 2021 0 |
| allfieldsGer |
10.1016/j.jmgm.2021.107942 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001460.pica (DE-627)ELV054690897 (ELSEVIER)S1093-3263(21)00113-3 DE-627 ger DE-627 rakwb eng 610 VZ 600 670 VZ 51.00 bkl Ni, Haiming verfasserin aut Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. Genome sequences alignment Elsevier Frequency chaos game representation Elsevier Phylogenetic tree construction Elsevier Perceptual image hashing Elsevier Mu, Hongbo oth Qi, Dawei oth Enthalten in Elsevier Chiu, Philip W. ELSEVIER Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia 2015 New York, NY [u.a.] (DE-627)ELV013464361 volume:107 year:2021 pages:0 https://doi.org/10.1016/j.jmgm.2021.107942 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_11 GBV_ILN_20 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_72 GBV_ILN_206 GBV_ILN_257 GBV_ILN_791 GBV_ILN_2001 GBV_ILN_2002 GBV_ILN_2007 GBV_ILN_2011 GBV_ILN_2012 GBV_ILN_2039 GBV_ILN_2069 GBV_ILN_2127 GBV_ILN_2227 51.00 Werkstoffkunde: Allgemeines VZ AR 107 2021 0 |
| allfieldsSound |
10.1016/j.jmgm.2021.107942 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001460.pica (DE-627)ELV054690897 (ELSEVIER)S1093-3263(21)00113-3 DE-627 ger DE-627 rakwb eng 610 VZ 600 670 VZ 51.00 bkl Ni, Haiming verfasserin aut Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. Genome sequences alignment Elsevier Frequency chaos game representation Elsevier Phylogenetic tree construction Elsevier Perceptual image hashing Elsevier Mu, Hongbo oth Qi, Dawei oth Enthalten in Elsevier Chiu, Philip W. ELSEVIER Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia 2015 New York, NY [u.a.] (DE-627)ELV013464361 volume:107 year:2021 pages:0 https://doi.org/10.1016/j.jmgm.2021.107942 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_11 GBV_ILN_20 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_72 GBV_ILN_206 GBV_ILN_257 GBV_ILN_791 GBV_ILN_2001 GBV_ILN_2002 GBV_ILN_2007 GBV_ILN_2011 GBV_ILN_2012 GBV_ILN_2039 GBV_ILN_2069 GBV_ILN_2127 GBV_ILN_2227 51.00 Werkstoffkunde: Allgemeines VZ AR 107 2021 0 |
| language |
English |
| source |
Enthalten in Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia New York, NY [u.a.] volume:107 year:2021 pages:0 |
| sourceStr |
Enthalten in Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia New York, NY [u.a.] volume:107 year:2021 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Werkstoffkunde: Allgemeines |
| institution |
findex.gbv.de |
| topic_facet |
Genome sequences alignment Frequency chaos game representation Phylogenetic tree construction Perceptual image hashing |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia |
| authorswithroles_txt_mv |
Ni, Haiming @@aut@@ Mu, Hongbo @@oth@@ Qi, Dawei @@oth@@ |
| publishDateDaySort_date |
2021-01-01T00:00:00Z |
| hierarchy_top_id |
ELV013464361 |
| dewey-sort |
3610 |
| id |
ELV054690897 |
| 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">ELV054690897</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626040622.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jmgm.2021.107942</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001460.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV054690897</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1093-3263(21)00113-3</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">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">51.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ni, Haiming</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Genome sequences alignment</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Frequency chaos game representation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Phylogenetic tree construction</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Perceptual image hashing</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mu, Hongbo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qi, Dawei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Chiu, Philip W. ELSEVIER</subfield><subfield code="t">Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia</subfield><subfield code="d">2015</subfield><subfield code="g">New York, NY [u.a.]</subfield><subfield code="w">(DE-627)ELV013464361</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:107</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jmgm.2021.107942</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</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_72</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_257</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_791</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2002</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2069</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2127</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2227</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">51.00</subfield><subfield code="j">Werkstoffkunde: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">107</subfield><subfield code="j">2021</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Ni, Haiming |
| spellingShingle |
Ni, Haiming ddc 610 ddc 600 bkl 51.00 Elsevier Genome sequences alignment Elsevier Frequency chaos game representation Elsevier Phylogenetic tree construction Elsevier Perceptual image hashing Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses |
| authorStr |
Ni, Haiming |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV013464361 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health 600 - Technology 670 - Manufacturing |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ 600 670 VZ 51.00 bkl Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses Genome sequences alignment Elsevier Frequency chaos game representation Elsevier Phylogenetic tree construction Elsevier Perceptual image hashing Elsevier |
| topic |
ddc 610 ddc 600 bkl 51.00 Elsevier Genome sequences alignment Elsevier Frequency chaos game representation Elsevier Phylogenetic tree construction Elsevier Perceptual image hashing |
| topic_unstemmed |
ddc 610 ddc 600 bkl 51.00 Elsevier Genome sequences alignment Elsevier Frequency chaos game representation Elsevier Phylogenetic tree construction Elsevier Perceptual image hashing |
| topic_browse |
ddc 610 ddc 600 bkl 51.00 Elsevier Genome sequences alignment Elsevier Frequency chaos game representation Elsevier Phylogenetic tree construction Elsevier Perceptual image hashing |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
h m hm d q dq |
| hierarchy_parent_title |
Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia |
| hierarchy_parent_id |
ELV013464361 |
| dewey-tens |
610 - Medicine & health 600 - Technology 670 - Manufacturing |
| hierarchy_top_title |
Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV013464361 |
| title |
Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses |
| ctrlnum |
(DE-627)ELV054690897 (ELSEVIER)S1093-3263(21)00113-3 |
| title_full |
Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses |
| author_sort |
Ni, Haiming |
| journal |
Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia |
| journalStr |
Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Ni, Haiming |
| container_volume |
107 |
| class |
610 VZ 600 670 VZ 51.00 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Ni, Haiming |
| doi_str_mv |
10.1016/j.jmgm.2021.107942 |
| dewey-full |
610 600 670 |
| title_sort |
applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses |
| title_auth |
Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses |
| abstract |
As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. |
| abstractGer |
As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. |
| abstract_unstemmed |
As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_11 GBV_ILN_20 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_72 GBV_ILN_206 GBV_ILN_257 GBV_ILN_791 GBV_ILN_2001 GBV_ILN_2002 GBV_ILN_2007 GBV_ILN_2011 GBV_ILN_2012 GBV_ILN_2039 GBV_ILN_2069 GBV_ILN_2127 GBV_ILN_2227 |
| title_short |
Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses |
| url |
https://doi.org/10.1016/j.jmgm.2021.107942 |
| remote_bool |
true |
| author2 |
Mu, Hongbo Qi, Dawei |
| author2Str |
Mu, Hongbo Qi, Dawei |
| ppnlink |
ELV013464361 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1016/j.jmgm.2021.107942 |
| up_date |
2024-07-06T22:25:55.502Z |
| _version_ |
1803870279307362304 |
| 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">ELV054690897</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626040622.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jmgm.2021.107942</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001460.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV054690897</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1093-3263(21)00113-3</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">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">51.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ni, Haiming</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Genome sequences alignment</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Frequency chaos game representation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Phylogenetic tree construction</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Perceptual image hashing</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mu, Hongbo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qi, Dawei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Chiu, Philip W. ELSEVIER</subfield><subfield code="t">Su1569 Standardized Training Program on Diagnosis of Early Gastrointestinal Cancers Using Narrow Band Imaging (NBI) in Asia</subfield><subfield code="d">2015</subfield><subfield code="g">New York, NY [u.a.]</subfield><subfield code="w">(DE-627)ELV013464361</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:107</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jmgm.2021.107942</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</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_72</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_257</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_791</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2002</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2069</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2127</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2227</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">51.00</subfield><subfield code="j">Werkstoffkunde: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">107</subfield><subfield code="j">2021</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.399398 |