Protein subcellular localization prediction based on compartment-specific features and structure conservation View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-09-08

AUTHORS

Emily Chia-Yu Su, Hua-Sheng Chiu, Allan Lo, Jenn-Kang Hwang, Ting-Yi Sung, Wen-Lian Hsu

ABSTRACT

BACKGROUND: Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins. RESULTS: We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%. CONCLUSION: Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes. More... »

PAGES

330-330

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-8-330

DOI

http://dx.doi.org/10.1186/1471-2105-8-330

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005996969

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17825110


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0601", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biochemistry and Cell Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Amino Acid Sequence", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bacterial Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Expression Profiling", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gram-Negative Bacteria", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Molecular Sequence Data", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sequence Alignment", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sequence Analysis, Protein", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sequence Homology, Amino Acid", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Structure-Activity Relationship", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Subcellular Fractions", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Institute of Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.260539.b", 
          "name": [
            "Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan", 
            "Institute of Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Su", 
        "givenName": "Emily Chia-Yu", 
        "id": "sg:person.01143414205.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01143414205.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.28665.3f", 
          "name": [
            "Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chiu", 
        "givenName": "Hua-Sheng", 
        "id": "sg:person.01257642605.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01257642605.97"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Life Sciences, National Tsing Hua University, Hsinchu, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.38348.34", 
          "name": [
            "Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan", 
            "Department of Life Sciences, National Tsing Hua University, Hsinchu, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lo", 
        "givenName": "Allan", 
        "id": "sg:person.01021224703.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01021224703.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.260539.b", 
          "name": [
            "Institute of Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hwang", 
        "givenName": "Jenn-Kang", 
        "id": "sg:person.01364151270.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01364151270.35"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.28665.3f", 
          "name": [
            "Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sung", 
        "givenName": "Ting-Yi", 
        "id": "sg:person.01005525217.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01005525217.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.28665.3f", 
          "name": [
            "Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hsu", 
        "givenName": "Wen-Lian", 
        "id": "sg:person.0750360063.66", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0750360063.66"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-1-4757-2440-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027312764", 
          "https://doi.org/10.1007/978-1-4757-2440-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-6-174", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050649071", 
          "https://doi.org/10.1186/1471-2105-6-174"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrmicro1494", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052392582", 
          "https://doi.org/10.1038/nrmicro1494"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-7-s5-s11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016000516", 
          "https://doi.org/10.1186/1471-2105-7-s5-s11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2180-5-58", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017810504", 
          "https://doi.org/10.1186/1471-2180-5-58"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/2983", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026051292", 
          "https://doi.org/10.1038/2983"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-4-28", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038813802", 
          "https://doi.org/10.1186/1471-2105-4-28"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-6-167", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044447875", 
          "https://doi.org/10.1186/1471-2105-6-167"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2007-09-08", 
    "datePublishedReg": "2007-09-08", 
    "description": "BACKGROUND: Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins.\nRESULTS: We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%.\nCONCLUSION: Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/1471-2105-8-330", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "8"
      }
    ], 
    "keywords": [
      "translocation pathway", 
      "subcellular localization", 
      "homology approach", 
      "localization prediction", 
      "high-throughput proteomic analysis", 
      "homology-based methods", 
      "three-way data split procedure", 
      "Gram-negative bacteria", 
      "protein subcellular localization", 
      "subcellular localization prediction", 
      "biological features", 
      "protein subcellular localization prediction", 
      "large-scale analysis", 
      "genome annotation", 
      "structural conservation", 
      "unknown proteins", 
      "homologous sequences", 
      "proteomic analysis", 
      "sequence identity", 
      "sequence homology", 
      "structure conservation", 
      "non-redundant data set", 
      "protein function prediction", 
      "function prediction", 
      "secondary structure", 
      "protein", 
      "query protein", 
      "homology", 
      "drug discovery", 
      "conservation", 
      "localization", 
      "pathway", 
      "sequence", 
      "experimental approach", 
      "structural similarity comparison", 
      "computational approach", 
      "proteome", 
      "bacteria", 
      "extensive study", 
      "similarity comparison", 
      "annotation", 
      "low levels", 
      "lack of information", 
      "data sets", 
      "discovery", 
      "useful indicator", 
      "identity", 
      "analysis", 
      "addition", 
      "experimental design", 
      "development", 
      "features", 
      "levels", 
      "prediction accuracy", 
      "set", 
      "structure", 
      "prediction method", 
      "prediction", 
      "number", 
      "approach", 
      "low coverage", 
      "lack", 
      "study", 
      "data", 
      "support vector machine model", 
      "results", 
      "advanced analysis", 
      "comparison", 
      "information", 
      "model", 
      "indicators", 
      "determination", 
      "method", 
      "vector machine model", 
      "hybrid prediction method", 
      "predictive performance", 
      "coverage", 
      "benchmark data sets", 
      "evaluation data sets", 
      "SVM model", 
      "assessment", 
      "split procedure", 
      "training data", 
      "overall accuracy", 
      "binary classifiers", 
      "machine model", 
      "overestimation", 
      "hybrid method", 
      "procedure", 
      "improvement", 
      "accuracy", 
      "design", 
      "classifier", 
      "performance", 
      "significant improvement", 
      "problem", 
      "structural homology approach", 
      "Gram-negative bacteria translocation pathways", 
      "bacteria translocation pathways", 
      "accurate prediction accuracy", 
      "data split procedure", 
      "compartment-specific features"
    ], 
    "name": "Protein subcellular localization prediction based on compartment-specific features and structure conservation", 
    "pagination": "330-330", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1005996969"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2105-8-330"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "17825110"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2105-8-330", 
      "https://app.dimensions.ai/details/publication/pub.1005996969"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-01-01T18:17", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_454.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/1471-2105-8-330"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-8-330'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-8-330'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-8-330'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-8-330'


 

This table displays all metadata directly associated to this object as RDF triples.

278 TRIPLES      22 PREDICATES      146 URIs      130 LITERALS      17 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2105-8-330 schema:about N09d35844922a44a38dbeda75607274e5
2 N27f04374c80b469aac004bfd03dd4ab3
3 N2ee6c171aa644595add5d16baf66682d
4 N3f00c9279f0c4190a88dad3e01f6bcc1
5 N6297f134ca1f41a69b3d3f16786125e7
6 N76bb689f1f1c47389012b092bfd03106
7 N93281aad36da441197e2298385f85b5b
8 Nb03b18a4b0184dc9993aeb737aef03a2
9 Nc55f4d1f787843639c4a490a769a52e6
10 Nc70f80155f0c41c4892e58df73bab072
11 anzsrc-for:06
12 anzsrc-for:0601
13 schema:author Ncdd0821a6337458fb66afb19770ab00e
14 schema:citation sg:pub.10.1007/978-1-4757-2440-0
15 sg:pub.10.1038/2983
16 sg:pub.10.1038/nrmicro1494
17 sg:pub.10.1186/1471-2105-4-28
18 sg:pub.10.1186/1471-2105-6-167
19 sg:pub.10.1186/1471-2105-6-174
20 sg:pub.10.1186/1471-2105-7-s5-s11
21 sg:pub.10.1186/1471-2180-5-58
22 schema:datePublished 2007-09-08
23 schema:datePublishedReg 2007-09-08
24 schema:description BACKGROUND: Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins. RESULTS: We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%. CONCLUSION: Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes.
25 schema:genre article
26 schema:inLanguage en
27 schema:isAccessibleForFree true
28 schema:isPartOf N68fa4890b57443bcb0b3a36a5e1a0f5d
29 Nec914fc409d54a6a9ea04ef117e6532b
30 sg:journal.1023786
31 schema:keywords Gram-negative bacteria
32 Gram-negative bacteria translocation pathways
33 SVM model
34 accuracy
35 accurate prediction accuracy
36 addition
37 advanced analysis
38 analysis
39 annotation
40 approach
41 assessment
42 bacteria
43 bacteria translocation pathways
44 benchmark data sets
45 binary classifiers
46 biological features
47 classifier
48 comparison
49 compartment-specific features
50 computational approach
51 conservation
52 coverage
53 data
54 data sets
55 data split procedure
56 design
57 determination
58 development
59 discovery
60 drug discovery
61 evaluation data sets
62 experimental approach
63 experimental design
64 extensive study
65 features
66 function prediction
67 genome annotation
68 high-throughput proteomic analysis
69 homologous sequences
70 homology
71 homology approach
72 homology-based methods
73 hybrid method
74 hybrid prediction method
75 identity
76 improvement
77 indicators
78 information
79 lack
80 lack of information
81 large-scale analysis
82 levels
83 localization
84 localization prediction
85 low coverage
86 low levels
87 machine model
88 method
89 model
90 non-redundant data set
91 number
92 overall accuracy
93 overestimation
94 pathway
95 performance
96 prediction
97 prediction accuracy
98 prediction method
99 predictive performance
100 problem
101 procedure
102 protein
103 protein function prediction
104 protein subcellular localization
105 protein subcellular localization prediction
106 proteome
107 proteomic analysis
108 query protein
109 results
110 secondary structure
111 sequence
112 sequence homology
113 sequence identity
114 set
115 significant improvement
116 similarity comparison
117 split procedure
118 structural conservation
119 structural homology approach
120 structural similarity comparison
121 structure
122 structure conservation
123 study
124 subcellular localization
125 subcellular localization prediction
126 support vector machine model
127 three-way data split procedure
128 training data
129 translocation pathway
130 unknown proteins
131 useful indicator
132 vector machine model
133 schema:name Protein subcellular localization prediction based on compartment-specific features and structure conservation
134 schema:pagination 330-330
135 schema:productId N296e61931e6b4b1bb02a20827840c999
136 N405c90ab65624aefbd0c657adbaf5283
137 Nfa21ab7b2b914dbe9313655488b22465
138 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005996969
139 https://doi.org/10.1186/1471-2105-8-330
140 schema:sdDatePublished 2022-01-01T18:17
141 schema:sdLicense https://scigraph.springernature.com/explorer/license/
142 schema:sdPublisher Nc53bbbe0da4a4c75b7a3354c46b45bf9
143 schema:url https://doi.org/10.1186/1471-2105-8-330
144 sgo:license sg:explorer/license/
145 sgo:sdDataset articles
146 rdf:type schema:ScholarlyArticle
147 N09d35844922a44a38dbeda75607274e5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
148 schema:name Subcellular Fractions
149 rdf:type schema:DefinedTerm
150 N0e61e7a416a44ff2860f968a6a41730a rdf:first sg:person.01005525217.26
151 rdf:rest N6802ebfcf9354b83937a491bccfaa7b9
152 N27f04374c80b469aac004bfd03dd4ab3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
153 schema:name Amino Acid Sequence
154 rdf:type schema:DefinedTerm
155 N296e61931e6b4b1bb02a20827840c999 schema:name doi
156 schema:value 10.1186/1471-2105-8-330
157 rdf:type schema:PropertyValue
158 N2ee6c171aa644595add5d16baf66682d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
159 schema:name Bacterial Proteins
160 rdf:type schema:DefinedTerm
161 N3f00c9279f0c4190a88dad3e01f6bcc1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Sequence Alignment
163 rdf:type schema:DefinedTerm
164 N405c90ab65624aefbd0c657adbaf5283 schema:name pubmed_id
165 schema:value 17825110
166 rdf:type schema:PropertyValue
167 N6297f134ca1f41a69b3d3f16786125e7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
168 schema:name Sequence Analysis, Protein
169 rdf:type schema:DefinedTerm
170 N6802ebfcf9354b83937a491bccfaa7b9 rdf:first sg:person.0750360063.66
171 rdf:rest rdf:nil
172 N68fa4890b57443bcb0b3a36a5e1a0f5d schema:volumeNumber 8
173 rdf:type schema:PublicationVolume
174 N76bb689f1f1c47389012b092bfd03106 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
175 schema:name Molecular Sequence Data
176 rdf:type schema:DefinedTerm
177 N93281aad36da441197e2298385f85b5b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
178 schema:name Gene Expression Profiling
179 rdf:type schema:DefinedTerm
180 Nb03b18a4b0184dc9993aeb737aef03a2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
181 schema:name Sequence Homology, Amino Acid
182 rdf:type schema:DefinedTerm
183 Nc53bbbe0da4a4c75b7a3354c46b45bf9 schema:name Springer Nature - SN SciGraph project
184 rdf:type schema:Organization
185 Nc55f4d1f787843639c4a490a769a52e6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
186 schema:name Structure-Activity Relationship
187 rdf:type schema:DefinedTerm
188 Nc60e64b0b9aa4ccaa01b31af1ec0a5b4 rdf:first sg:person.01364151270.35
189 rdf:rest N0e61e7a416a44ff2860f968a6a41730a
190 Nc70f80155f0c41c4892e58df73bab072 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
191 schema:name Gram-Negative Bacteria
192 rdf:type schema:DefinedTerm
193 Ncb43d05867aa4ec98c3fbc62de8d51c8 rdf:first sg:person.01257642605.97
194 rdf:rest Ne0124c15120247dd828e636827ca218d
195 Ncdd0821a6337458fb66afb19770ab00e rdf:first sg:person.01143414205.17
196 rdf:rest Ncb43d05867aa4ec98c3fbc62de8d51c8
197 Ne0124c15120247dd828e636827ca218d rdf:first sg:person.01021224703.58
198 rdf:rest Nc60e64b0b9aa4ccaa01b31af1ec0a5b4
199 Nec914fc409d54a6a9ea04ef117e6532b schema:issueNumber 1
200 rdf:type schema:PublicationIssue
201 Nfa21ab7b2b914dbe9313655488b22465 schema:name dimensions_id
202 schema:value pub.1005996969
203 rdf:type schema:PropertyValue
204 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
205 schema:name Biological Sciences
206 rdf:type schema:DefinedTerm
207 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
208 schema:name Biochemistry and Cell Biology
209 rdf:type schema:DefinedTerm
210 sg:journal.1023786 schema:issn 1471-2105
211 schema:name BMC Bioinformatics
212 schema:publisher Springer Nature
213 rdf:type schema:Periodical
214 sg:person.01005525217.26 schema:affiliation grid-institutes:grid.28665.3f
215 schema:familyName Sung
216 schema:givenName Ting-Yi
217 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01005525217.26
218 rdf:type schema:Person
219 sg:person.01021224703.58 schema:affiliation grid-institutes:grid.38348.34
220 schema:familyName Lo
221 schema:givenName Allan
222 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01021224703.58
223 rdf:type schema:Person
224 sg:person.01143414205.17 schema:affiliation grid-institutes:grid.260539.b
225 schema:familyName Su
226 schema:givenName Emily Chia-Yu
227 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01143414205.17
228 rdf:type schema:Person
229 sg:person.01257642605.97 schema:affiliation grid-institutes:grid.28665.3f
230 schema:familyName Chiu
231 schema:givenName Hua-Sheng
232 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01257642605.97
233 rdf:type schema:Person
234 sg:person.01364151270.35 schema:affiliation grid-institutes:grid.260539.b
235 schema:familyName Hwang
236 schema:givenName Jenn-Kang
237 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01364151270.35
238 rdf:type schema:Person
239 sg:person.0750360063.66 schema:affiliation grid-institutes:grid.28665.3f
240 schema:familyName Hsu
241 schema:givenName Wen-Lian
242 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0750360063.66
243 rdf:type schema:Person
244 sg:pub.10.1007/978-1-4757-2440-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027312764
245 https://doi.org/10.1007/978-1-4757-2440-0
246 rdf:type schema:CreativeWork
247 sg:pub.10.1038/2983 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026051292
248 https://doi.org/10.1038/2983
249 rdf:type schema:CreativeWork
250 sg:pub.10.1038/nrmicro1494 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052392582
251 https://doi.org/10.1038/nrmicro1494
252 rdf:type schema:CreativeWork
253 sg:pub.10.1186/1471-2105-4-28 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038813802
254 https://doi.org/10.1186/1471-2105-4-28
255 rdf:type schema:CreativeWork
256 sg:pub.10.1186/1471-2105-6-167 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044447875
257 https://doi.org/10.1186/1471-2105-6-167
258 rdf:type schema:CreativeWork
259 sg:pub.10.1186/1471-2105-6-174 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050649071
260 https://doi.org/10.1186/1471-2105-6-174
261 rdf:type schema:CreativeWork
262 sg:pub.10.1186/1471-2105-7-s5-s11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016000516
263 https://doi.org/10.1186/1471-2105-7-s5-s11
264 rdf:type schema:CreativeWork
265 sg:pub.10.1186/1471-2180-5-58 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017810504
266 https://doi.org/10.1186/1471-2180-5-58
267 rdf:type schema:CreativeWork
268 grid-institutes:grid.260539.b schema:alternateName Institute of Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan
269 schema:name Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan
270 Institute of Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan
271 rdf:type schema:Organization
272 grid-institutes:grid.28665.3f schema:alternateName Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan
273 schema:name Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan
274 rdf:type schema:Organization
275 grid-institutes:grid.38348.34 schema:alternateName Department of Life Sciences, National Tsing Hua University, Hsinchu, Taiwan
276 schema:name Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan
277 Department of Life Sciences, National Tsing Hua University, Hsinchu, Taiwan
278 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...