Unsupervised Gene Selection and Clustering Using Simulated Annealing View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2006

AUTHORS

Maurizio Filippone , Francesco Masulli , Stefano Rovetta

ABSTRACT

When applied to genomic data, many popular unsupervised explorative data analysis tools based on clustering algorithms often fail due to their small cardinality and high dimensionality. In this paper we propose a wrapper method for gene selection based on simulated annealing and unsupervised clustering. The proposed approach, even if computationally intensive, permits to select the most relevant features (genes), and to rank their relevance, allowing to improve the results of clustering algorithms. More... »

PAGES

229-235

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11676935_28

DOI

http://dx.doi.org/10.1007/11676935_28

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036239508


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Dipartimento di Informatica e Scienze dell\u2019Informazione, Universit\u00e0 di Genova, Via Dodecaneso 35, I-16146, Genova, Italy", 
          "id": "http://www.grid.ac/institutes/grid.5606.5", 
          "name": [
            "Dipartimento di Informatica e Scienze dell\u2019Informazione, Universit\u00e0 di Genova, Via Dodecaneso 35, I-16146, Genova, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Filippone", 
        "givenName": "Maurizio", 
        "id": "sg:person.07706215665.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07706215665.03"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Dipartimento di Informatica, Universit\u00e0 di Pisa, Largo B. Pontecorvo 3, I-56127, Pisa, Italy", 
          "id": "http://www.grid.ac/institutes/grid.5395.a", 
          "name": [
            "Dipartimento di Informatica, Universit\u00e0 di Pisa, Largo B. Pontecorvo 3, I-56127, Pisa, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Masulli", 
        "givenName": "Francesco", 
        "id": "sg:person.013052261502.67", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013052261502.67"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Dipartimento di Informatica e Scienze dell\u2019Informazione, Universit\u00e0 di Genova, Via Dodecaneso 35, I-16146, Genova, Italy", 
          "id": "http://www.grid.ac/institutes/grid.5606.5", 
          "name": [
            "Dipartimento di Informatica e Scienze dell\u2019Informazione, Universit\u00e0 di Genova, Via Dodecaneso 35, I-16146, Genova, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rovetta", 
        "givenName": "Stefano", 
        "id": "sg:person.015767137221.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015767137221.48"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2006", 
    "datePublishedReg": "2006-01-01", 
    "description": "When applied to genomic data, many popular unsupervised explorative data analysis tools based on clustering algorithms often fail due to their small cardinality and high dimensionality. In this paper we propose a wrapper method for gene selection based on simulated annealing and unsupervised clustering. The proposed approach, even if computationally intensive, permits to select the most relevant features (genes), and to rank their relevance, allowing to improve the results of clustering algorithms.", 
    "editor": [
      {
        "familyName": "Bloch", 
        "givenName": "Isabelle", 
        "type": "Person"
      }, 
      {
        "familyName": "Petrosino", 
        "givenName": "Alfredo", 
        "type": "Person"
      }, 
      {
        "familyName": "Tettamanzi", 
        "givenName": "Andrea G. B.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/11676935_28", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-32529-1", 
        "978-3-540-32530-7"
      ], 
      "name": "Fuzzy Logic and Applications", 
      "type": "Book"
    }, 
    "keywords": [
      "data analysis tools", 
      "wrapper method", 
      "high dimensionality", 
      "unsupervised clustering", 
      "relevant features", 
      "analysis tools", 
      "gene selection", 
      "algorithm", 
      "clustering", 
      "unsupervised gene selection", 
      "genomic data", 
      "smallest cardinality", 
      "dimensionality", 
      "cardinality", 
      "selection", 
      "tool", 
      "features", 
      "method", 
      "data", 
      "annealing", 
      "results", 
      "relevance", 
      "paper", 
      "approach", 
      "popular unsupervised explorative data analysis tools", 
      "unsupervised explorative data analysis tools", 
      "explorative data analysis tools"
    ], 
    "name": "Unsupervised Gene Selection and Clustering Using Simulated Annealing", 
    "pagination": "229-235", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1036239508"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/11676935_28"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/11676935_28", 
      "https://app.dimensions.ai/details/publication/pub.1036239508"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:26", 
    "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/chapter/chapter_67.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/11676935_28"
  }
]
 

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.10.1007/11676935_28'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/11676935_28'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/11676935_28'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/11676935_28'


 

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

114 TRIPLES      23 PREDICATES      53 URIs      46 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/11676935_28 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N3af986b954db4e63b99192364c25cc5c
4 schema:datePublished 2006
5 schema:datePublishedReg 2006-01-01
6 schema:description When applied to genomic data, many popular unsupervised explorative data analysis tools based on clustering algorithms often fail due to their small cardinality and high dimensionality. In this paper we propose a wrapper method for gene selection based on simulated annealing and unsupervised clustering. The proposed approach, even if computationally intensive, permits to select the most relevant features (genes), and to rank their relevance, allowing to improve the results of clustering algorithms.
7 schema:editor N9058d8f72c234382a2448d304053636c
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree true
11 schema:isPartOf N3e3cefbe00ae410babd23c676d547f8c
12 schema:keywords algorithm
13 analysis tools
14 annealing
15 approach
16 cardinality
17 clustering
18 data
19 data analysis tools
20 dimensionality
21 explorative data analysis tools
22 features
23 gene selection
24 genomic data
25 high dimensionality
26 method
27 paper
28 popular unsupervised explorative data analysis tools
29 relevance
30 relevant features
31 results
32 selection
33 smallest cardinality
34 tool
35 unsupervised clustering
36 unsupervised explorative data analysis tools
37 unsupervised gene selection
38 wrapper method
39 schema:name Unsupervised Gene Selection and Clustering Using Simulated Annealing
40 schema:pagination 229-235
41 schema:productId N429e816bc5b543dbae535e729dddf17c
42 N8257e9ad22764870814d5cae07066de3
43 schema:publisher N8045d76040844af885feb19ad6f640b9
44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036239508
45 https://doi.org/10.1007/11676935_28
46 schema:sdDatePublished 2022-01-01T19:26
47 schema:sdLicense https://scigraph.springernature.com/explorer/license/
48 schema:sdPublisher Nfabbffd9c6434146bb429e401e0d2855
49 schema:url https://doi.org/10.1007/11676935_28
50 sgo:license sg:explorer/license/
51 sgo:sdDataset chapters
52 rdf:type schema:Chapter
53 N3af986b954db4e63b99192364c25cc5c rdf:first sg:person.07706215665.03
54 rdf:rest Ne710be9f389e4dcf8d07ac92b06b4f3d
55 N3e3cefbe00ae410babd23c676d547f8c schema:isbn 978-3-540-32529-1
56 978-3-540-32530-7
57 schema:name Fuzzy Logic and Applications
58 rdf:type schema:Book
59 N429e816bc5b543dbae535e729dddf17c schema:name doi
60 schema:value 10.1007/11676935_28
61 rdf:type schema:PropertyValue
62 N486a7ba4cd1f4b2d9172493be948082f schema:familyName Petrosino
63 schema:givenName Alfredo
64 rdf:type schema:Person
65 N8045d76040844af885feb19ad6f640b9 schema:name Springer Nature
66 rdf:type schema:Organisation
67 N8257e9ad22764870814d5cae07066de3 schema:name dimensions_id
68 schema:value pub.1036239508
69 rdf:type schema:PropertyValue
70 N8d1c4a4b941d4d1a97745c531ba2125a schema:familyName Bloch
71 schema:givenName Isabelle
72 rdf:type schema:Person
73 N9058d8f72c234382a2448d304053636c rdf:first N8d1c4a4b941d4d1a97745c531ba2125a
74 rdf:rest Ncc3b8d74dd70462987f8357c9a7b0f13
75 Ncc3b8d74dd70462987f8357c9a7b0f13 rdf:first N486a7ba4cd1f4b2d9172493be948082f
76 rdf:rest Nd2aded5876aa46738e35da6065d2c555
77 Nd2aded5876aa46738e35da6065d2c555 rdf:first Nf2005ac8434440e58a74213ccb6eb8cc
78 rdf:rest rdf:nil
79 Ne710be9f389e4dcf8d07ac92b06b4f3d rdf:first sg:person.013052261502.67
80 rdf:rest Nefee6c68951a4ceaada066d6e37d18f5
81 Nefee6c68951a4ceaada066d6e37d18f5 rdf:first sg:person.015767137221.48
82 rdf:rest rdf:nil
83 Nf2005ac8434440e58a74213ccb6eb8cc schema:familyName Tettamanzi
84 schema:givenName Andrea G. B.
85 rdf:type schema:Person
86 Nfabbffd9c6434146bb429e401e0d2855 schema:name Springer Nature - SN SciGraph project
87 rdf:type schema:Organization
88 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
89 schema:name Information and Computing Sciences
90 rdf:type schema:DefinedTerm
91 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
92 schema:name Artificial Intelligence and Image Processing
93 rdf:type schema:DefinedTerm
94 sg:person.013052261502.67 schema:affiliation grid-institutes:grid.5395.a
95 schema:familyName Masulli
96 schema:givenName Francesco
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013052261502.67
98 rdf:type schema:Person
99 sg:person.015767137221.48 schema:affiliation grid-institutes:grid.5606.5
100 schema:familyName Rovetta
101 schema:givenName Stefano
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015767137221.48
103 rdf:type schema:Person
104 sg:person.07706215665.03 schema:affiliation grid-institutes:grid.5606.5
105 schema:familyName Filippone
106 schema:givenName Maurizio
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07706215665.03
108 rdf:type schema:Person
109 grid-institutes:grid.5395.a schema:alternateName Dipartimento di Informatica, Università di Pisa, Largo B. Pontecorvo 3, I-56127, Pisa, Italy
110 schema:name Dipartimento di Informatica, Università di Pisa, Largo B. Pontecorvo 3, I-56127, Pisa, Italy
111 rdf:type schema:Organization
112 grid-institutes:grid.5606.5 schema:alternateName Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, Via Dodecaneso 35, I-16146, Genova, Italy
113 schema:name Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, Via Dodecaneso 35, I-16146, Genova, Italy
114 rdf:type schema:Organization
 




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


...