Inference of Genetic Regulatory Networks via Best-Fit Extensions View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2006-01-01

AUTHORS

Harri Lähdesmäki , Ilya Shmulevich , Olli Yli-Harja , Jaakko Astola

ABSTRACT

5. ConclusionsThe ability to efficiently infer the structure of Boolean networks has immense potential for understanding the regulatory interactions in real genetic networks. We have considered a learning strategy that is well suited for situations in which inconsistencies in observations are likely to occur. This strategy produces a Boolean network that makes as few misclassifications as possible and is a generalization of the well-known Consistency Problem. We have focused on the computational complexity of this problem. It turns out that for many function classes, the Best-Fit Extension Problem for Boolean networks is polynomial-time solvable, including those networks having bounded indegree and those in which no assumptions whatsoever about the functions are made. This promising result provides motivation for developing efficient algorithms for inferring network structures from gene expression data. More... »

PAGES

259-278

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/0-387-26288-1_13

DOI

http://dx.doi.org/10.1007/0-387-26288-1_13

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036449427


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/2008/0802", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Computation Theory and Mathematics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Cancer Genomics Laboratory, Department of Pathology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA", 
          "id": "http://www.grid.ac/institutes/grid.240145.6", 
          "name": [
            "Cancer Genomics Laboratory, Department of Pathology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "L\u00e4hdesm\u00e4ki", 
        "givenName": "Harri", 
        "id": "sg:person.01306776600.47", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01306776600.47"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cancer Genomics Laboratory, Department of Pathology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA", 
          "id": "http://www.grid.ac/institutes/grid.240145.6", 
          "name": [
            "Cancer Genomics Laboratory, Department of Pathology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shmulevich", 
        "givenName": "Ilya", 
        "id": "sg:person.01354314446.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354314446.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Signal Processing, Tampere University of Technology, Tampere, Finland", 
          "id": "http://www.grid.ac/institutes/grid.502801.e", 
          "name": [
            "Institute of Signal Processing, Tampere University of Technology, Tampere, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yli-Harja", 
        "givenName": "Olli", 
        "id": "sg:person.01134121530.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134121530.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Signal Processing, Tampere University of Technology, Tampere, Finland", 
          "id": "http://www.grid.ac/institutes/grid.502801.e", 
          "name": [
            "Institute of Signal Processing, Tampere University of Technology, Tampere, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Astola", 
        "givenName": "Jaakko", 
        "id": "sg:person.01134141351.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134141351.02"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2006-01-01", 
    "datePublishedReg": "2006-01-01", 
    "description": "5. ConclusionsThe ability to efficiently infer the structure of Boolean networks has immense potential for understanding the regulatory interactions in real genetic networks. We have considered a learning strategy that is well suited for situations in which inconsistencies in observations are likely to occur. This strategy produces a Boolean network that makes as few misclassifications as possible and is a generalization of the well-known Consistency Problem. We have focused on the computational complexity of this problem. It turns out that for many function classes, the Best-Fit Extension Problem for Boolean networks is polynomial-time solvable, including those networks having bounded indegree and those in which no assumptions whatsoever about the functions are made. This promising result provides motivation for developing efficient algorithms for inferring network structures from gene expression data.", 
    "editor": [
      {
        "familyName": "Zhang", 
        "givenName": "Wei", 
        "type": "Person"
      }, 
      {
        "familyName": "Shmulevich", 
        "givenName": "Ilya", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/0-387-26288-1_13", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-0-387-26287-1", 
        "978-0-387-26288-8"
      ], 
      "name": "Computational and Statistical Approaches to Genomics", 
      "type": "Book"
    }, 
    "keywords": [
      "Boolean networks", 
      "real genetic networks", 
      "computational complexity", 
      "efficient algorithm", 
      "consistency problem", 
      "network", 
      "genetic regulatory networks", 
      "network structure", 
      "gene expression data", 
      "learning strategies", 
      "promising results", 
      "expression data", 
      "algorithm", 
      "extension problem", 
      "function classes", 
      "complexity", 
      "genetic networks", 
      "inference", 
      "regulatory networks", 
      "immense potential", 
      "extension", 
      "strategies", 
      "inconsistencies", 
      "misclassification", 
      "generalization", 
      "situation", 
      "class", 
      "data", 
      "motivation", 
      "regulatory interactions", 
      "assumption", 
      "structure", 
      "results", 
      "ability", 
      "function", 
      "interaction", 
      "potential", 
      "observations", 
      "problem", 
      "ConclusionsThe ability", 
      "Fit Extension Problem", 
      "Best-Fit Extensions"
    ], 
    "name": "Inference of Genetic Regulatory Networks via Best-Fit Extensions", 
    "pagination": "259-278", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1036449427"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/0-387-26288-1_13"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/0-387-26288-1_13", 
      "https://app.dimensions.ai/details/publication/pub.1036449427"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:22", 
    "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_381.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/0-387-26288-1_13"
  }
]
 

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.10.1007/0-387-26288-1_13'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/0-387-26288-1_13'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/0-387-26288-1_13'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/0-387-26288-1_13'


 

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

131 TRIPLES      23 PREDICATES      67 URIs      60 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/0-387-26288-1_13 schema:about anzsrc-for:08
2 anzsrc-for:0802
3 schema:author N02dd0a3cad4f4afbb6409a7cbcdef6dd
4 schema:datePublished 2006-01-01
5 schema:datePublishedReg 2006-01-01
6 schema:description 5. ConclusionsThe ability to efficiently infer the structure of Boolean networks has immense potential for understanding the regulatory interactions in real genetic networks. We have considered a learning strategy that is well suited for situations in which inconsistencies in observations are likely to occur. This strategy produces a Boolean network that makes as few misclassifications as possible and is a generalization of the well-known Consistency Problem. We have focused on the computational complexity of this problem. It turns out that for many function classes, the Best-Fit Extension Problem for Boolean networks is polynomial-time solvable, including those networks having bounded indegree and those in which no assumptions whatsoever about the functions are made. This promising result provides motivation for developing efficient algorithms for inferring network structures from gene expression data.
7 schema:editor Nc5f9fc3d218b45b0a30ef4f78d2cbd56
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N4515fcb97ee54c42a3321ad41a6e746b
12 schema:keywords Best-Fit Extensions
13 Boolean networks
14 ConclusionsThe ability
15 Fit Extension Problem
16 ability
17 algorithm
18 assumption
19 class
20 complexity
21 computational complexity
22 consistency problem
23 data
24 efficient algorithm
25 expression data
26 extension
27 extension problem
28 function
29 function classes
30 gene expression data
31 generalization
32 genetic networks
33 genetic regulatory networks
34 immense potential
35 inconsistencies
36 inference
37 interaction
38 learning strategies
39 misclassification
40 motivation
41 network
42 network structure
43 observations
44 potential
45 problem
46 promising results
47 real genetic networks
48 regulatory interactions
49 regulatory networks
50 results
51 situation
52 strategies
53 structure
54 schema:name Inference of Genetic Regulatory Networks via Best-Fit Extensions
55 schema:pagination 259-278
56 schema:productId N3d76d43ae56e40f48efbbc1f46297306
57 Nadb66dc5d5bc45659219f9a881d5df6c
58 schema:publisher N0ee53dce5bc040f89d3f7ae7ad526fb7
59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036449427
60 https://doi.org/10.1007/0-387-26288-1_13
61 schema:sdDatePublished 2022-01-01T19:22
62 schema:sdLicense https://scigraph.springernature.com/explorer/license/
63 schema:sdPublisher Na4b91b73b94b45b2891b520276d607a1
64 schema:url https://doi.org/10.1007/0-387-26288-1_13
65 sgo:license sg:explorer/license/
66 sgo:sdDataset chapters
67 rdf:type schema:Chapter
68 N02dd0a3cad4f4afbb6409a7cbcdef6dd rdf:first sg:person.01306776600.47
69 rdf:rest N0b38c2ada0df405ba83386ee10b1ee14
70 N0b38c2ada0df405ba83386ee10b1ee14 rdf:first sg:person.01354314446.15
71 rdf:rest Nd30c79fd9e8f43ce89fd6e03ce951c87
72 N0ee53dce5bc040f89d3f7ae7ad526fb7 schema:name Springer Nature
73 rdf:type schema:Organisation
74 N152df15355fa4f5e94908258d02fa508 schema:familyName Shmulevich
75 schema:givenName Ilya
76 rdf:type schema:Person
77 N1f57c8e468ff4b808b5c61351684b263 schema:familyName Zhang
78 schema:givenName Wei
79 rdf:type schema:Person
80 N3d76d43ae56e40f48efbbc1f46297306 schema:name dimensions_id
81 schema:value pub.1036449427
82 rdf:type schema:PropertyValue
83 N4515fcb97ee54c42a3321ad41a6e746b schema:isbn 978-0-387-26287-1
84 978-0-387-26288-8
85 schema:name Computational and Statistical Approaches to Genomics
86 rdf:type schema:Book
87 N78e8857c2023495b9ff0d6c42600c2cc rdf:first N152df15355fa4f5e94908258d02fa508
88 rdf:rest rdf:nil
89 Na4b91b73b94b45b2891b520276d607a1 schema:name Springer Nature - SN SciGraph project
90 rdf:type schema:Organization
91 Nadb66dc5d5bc45659219f9a881d5df6c schema:name doi
92 schema:value 10.1007/0-387-26288-1_13
93 rdf:type schema:PropertyValue
94 Nae2a4f7b303f47c4b876242d4a7d3845 rdf:first sg:person.01134141351.02
95 rdf:rest rdf:nil
96 Nc5f9fc3d218b45b0a30ef4f78d2cbd56 rdf:first N1f57c8e468ff4b808b5c61351684b263
97 rdf:rest N78e8857c2023495b9ff0d6c42600c2cc
98 Nd30c79fd9e8f43ce89fd6e03ce951c87 rdf:first sg:person.01134121530.92
99 rdf:rest Nae2a4f7b303f47c4b876242d4a7d3845
100 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
101 schema:name Information and Computing Sciences
102 rdf:type schema:DefinedTerm
103 anzsrc-for:0802 schema:inDefinedTermSet anzsrc-for:
104 schema:name Computation Theory and Mathematics
105 rdf:type schema:DefinedTerm
106 sg:person.01134121530.92 schema:affiliation grid-institutes:grid.502801.e
107 schema:familyName Yli-Harja
108 schema:givenName Olli
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134121530.92
110 rdf:type schema:Person
111 sg:person.01134141351.02 schema:affiliation grid-institutes:grid.502801.e
112 schema:familyName Astola
113 schema:givenName Jaakko
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134141351.02
115 rdf:type schema:Person
116 sg:person.01306776600.47 schema:affiliation grid-institutes:grid.240145.6
117 schema:familyName Lähdesmäki
118 schema:givenName Harri
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01306776600.47
120 rdf:type schema:Person
121 sg:person.01354314446.15 schema:affiliation grid-institutes:grid.240145.6
122 schema:familyName Shmulevich
123 schema:givenName Ilya
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354314446.15
125 rdf:type schema:Person
126 grid-institutes:grid.240145.6 schema:alternateName Cancer Genomics Laboratory, Department of Pathology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA
127 schema:name Cancer Genomics Laboratory, Department of Pathology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA
128 rdf:type schema:Organization
129 grid-institutes:grid.502801.e schema:alternateName Institute of Signal Processing, Tampere University of Technology, Tampere, Finland
130 schema:name Institute of Signal Processing, Tampere University of Technology, Tampere, Finland
131 rdf:type schema:Organization
 




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


...