Best-Fit in Linear Time for Non-generative Population Simulation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Niina Haiminen , Claude Lebreton , Laxmi Parida

ABSTRACT

Constructing populations with pre-specified characteristics is a fundamental problem in population genetics and other applied areas. We present a novel non-generative approach that deconstructs the desired population into essential local constraints and then builds the output bottom-up. This is achieved using primarily best-fit techniques from discrete methods, which ensures accuracy of the output. Also, the algorithms are fast, i.e., linear, or even sublinear, in the size of the output. The non-generative approach also results in high sensitivity in the algotihms. Since the accuracy and sensitivity of the population simulation is critical to the quality of the output of the applications that use them, we believe that these algorithms will provide a strong foundation to the methods in these studies. More... »

PAGES

247-262

Book

TITLE

Algorithms in Bioinformatics

ISBN

978-3-662-44752-9
978-3-662-44753-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-662-44753-6_19

DOI

http://dx.doi.org/10.1007/978-3-662-44753-6_19

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1019593946


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/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Computational Biology Center, IBM T. J. Watson Research, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Computational Biology Center, IBM T. J. Watson Research, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Haiminen", 
        "givenName": "Niina", 
        "id": "sg:person.0746114007.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0746114007.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Limagrain Europe, Centre de Recherche de Chappes, France", 
          "id": "http://www.grid.ac/institutes/grid.464033.6", 
          "name": [
            "Limagrain Europe, Centre de Recherche de Chappes, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lebreton", 
        "givenName": "Claude", 
        "id": "sg:person.01342563076.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01342563076.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Computational Biology Center, IBM T. J. Watson Research, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Computational Biology Center, IBM T. J. Watson Research, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Parida", 
        "givenName": "Laxmi", 
        "id": "sg:person.01336557015.68", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01336557015.68"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2014", 
    "datePublishedReg": "2014-01-01", 
    "description": "Constructing populations with pre-specified characteristics is a fundamental problem in population genetics and other applied areas. We present a novel non-generative approach that deconstructs the desired population into essential local constraints and then builds the output bottom-up. This is achieved using primarily best-fit techniques from discrete methods, which ensures accuracy of the output. Also, the algorithms are fast, i.e., linear, or even sublinear, in the size of the output. The non-generative approach also results in high sensitivity in the algotihms. Since the accuracy and sensitivity of the population simulation is critical to the quality of the output of the applications that use them, we believe that these algorithms will provide a strong foundation to the methods in these studies.", 
    "editor": [
      {
        "familyName": "Brown", 
        "givenName": "Dan", 
        "type": "Person"
      }, 
      {
        "familyName": "Morgenstern", 
        "givenName": "Burkhard", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-662-44753-6_19", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-662-44752-9", 
        "978-3-662-44753-6"
      ], 
      "name": "Algorithms in Bioinformatics", 
      "type": "Book"
    }, 
    "keywords": [
      "non-generative approach", 
      "discrete method", 
      "best fit technique", 
      "linear time", 
      "local constraints", 
      "fundamental problem", 
      "population simulations", 
      "algorithm", 
      "simulations", 
      "pre-specified characteristics", 
      "linear", 
      "accuracy", 
      "output", 
      "constraints", 
      "problem", 
      "population genetics", 
      "approach", 
      "applications", 
      "technique", 
      "foundation", 
      "size", 
      "time", 
      "characteristics", 
      "sensitivity", 
      "high sensitivity", 
      "quality", 
      "area", 
      "strong foundation", 
      "study", 
      "population", 
      "genetics", 
      "method"
    ], 
    "name": "Best-Fit in Linear Time for Non-generative Population Simulation", 
    "pagination": "247-262", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1019593946"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-662-44753-6_19"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-662-44753-6_19", 
      "https://app.dimensions.ai/details/publication/pub.1019593946"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-10-01T06:59", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/chapter/chapter_434.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-662-44753-6_19"
  }
]
 

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.10.1007/978-3-662-44753-6_19'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-662-44753-6_19'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-662-44753-6_19'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-662-44753-6_19'


 

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

113 TRIPLES      22 PREDICATES      57 URIs      50 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-662-44753-6_19 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author N0a88409aa93e4e0c85f02b10a9f44ba0
4 schema:datePublished 2014
5 schema:datePublishedReg 2014-01-01
6 schema:description Constructing populations with pre-specified characteristics is a fundamental problem in population genetics and other applied areas. We present a novel non-generative approach that deconstructs the desired population into essential local constraints and then builds the output bottom-up. This is achieved using primarily best-fit techniques from discrete methods, which ensures accuracy of the output. Also, the algorithms are fast, i.e., linear, or even sublinear, in the size of the output. The non-generative approach also results in high sensitivity in the algotihms. Since the accuracy and sensitivity of the population simulation is critical to the quality of the output of the applications that use them, we believe that these algorithms will provide a strong foundation to the methods in these studies.
7 schema:editor N8a634ae75d9b4213b236515bc16440f5
8 schema:genre chapter
9 schema:isAccessibleForFree false
10 schema:isPartOf N1da8a10edff847d18cc6ddaacd42d50a
11 schema:keywords accuracy
12 algorithm
13 applications
14 approach
15 area
16 best fit technique
17 characteristics
18 constraints
19 discrete method
20 foundation
21 fundamental problem
22 genetics
23 high sensitivity
24 linear
25 linear time
26 local constraints
27 method
28 non-generative approach
29 output
30 population
31 population genetics
32 population simulations
33 pre-specified characteristics
34 problem
35 quality
36 sensitivity
37 simulations
38 size
39 strong foundation
40 study
41 technique
42 time
43 schema:name Best-Fit in Linear Time for Non-generative Population Simulation
44 schema:pagination 247-262
45 schema:productId N6df08924f4534c77bbf2b7ab7d064cc3
46 Na05ab07c35924fa59dc8cc708e0fa268
47 schema:publisher Naeb53d3a7f644ee9bf9d6e363630f78a
48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019593946
49 https://doi.org/10.1007/978-3-662-44753-6_19
50 schema:sdDatePublished 2022-10-01T06:59
51 schema:sdLicense https://scigraph.springernature.com/explorer/license/
52 schema:sdPublisher N688c70002d9e4e84a1eb903796c5b04d
53 schema:url https://doi.org/10.1007/978-3-662-44753-6_19
54 sgo:license sg:explorer/license/
55 sgo:sdDataset chapters
56 rdf:type schema:Chapter
57 N01285247c22249cbb9e26906edb09823 rdf:first sg:person.01342563076.00
58 rdf:rest N1c25d3b984df4e9b855e397c2d11d6aa
59 N0a88409aa93e4e0c85f02b10a9f44ba0 rdf:first sg:person.0746114007.76
60 rdf:rest N01285247c22249cbb9e26906edb09823
61 N1c25d3b984df4e9b855e397c2d11d6aa rdf:first sg:person.01336557015.68
62 rdf:rest rdf:nil
63 N1da8a10edff847d18cc6ddaacd42d50a schema:isbn 978-3-662-44752-9
64 978-3-662-44753-6
65 schema:name Algorithms in Bioinformatics
66 rdf:type schema:Book
67 N6241ca0ea2354ab7bfa4e8a8617cea6c schema:familyName Morgenstern
68 schema:givenName Burkhard
69 rdf:type schema:Person
70 N688c70002d9e4e84a1eb903796c5b04d schema:name Springer Nature - SN SciGraph project
71 rdf:type schema:Organization
72 N6df08924f4534c77bbf2b7ab7d064cc3 schema:name doi
73 schema:value 10.1007/978-3-662-44753-6_19
74 rdf:type schema:PropertyValue
75 N8a634ae75d9b4213b236515bc16440f5 rdf:first Nb4dd22e9884d4e5496cab3c372ac3a6f
76 rdf:rest Ne9610bff98194afda5b3335576d475c2
77 Na05ab07c35924fa59dc8cc708e0fa268 schema:name dimensions_id
78 schema:value pub.1019593946
79 rdf:type schema:PropertyValue
80 Naeb53d3a7f644ee9bf9d6e363630f78a schema:name Springer Nature
81 rdf:type schema:Organisation
82 Nb4dd22e9884d4e5496cab3c372ac3a6f schema:familyName Brown
83 schema:givenName Dan
84 rdf:type schema:Person
85 Ne9610bff98194afda5b3335576d475c2 rdf:first N6241ca0ea2354ab7bfa4e8a8617cea6c
86 rdf:rest rdf:nil
87 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
88 schema:name Mathematical Sciences
89 rdf:type schema:DefinedTerm
90 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
91 schema:name Statistics
92 rdf:type schema:DefinedTerm
93 sg:person.01336557015.68 schema:affiliation grid-institutes:None
94 schema:familyName Parida
95 schema:givenName Laxmi
96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01336557015.68
97 rdf:type schema:Person
98 sg:person.01342563076.00 schema:affiliation grid-institutes:grid.464033.6
99 schema:familyName Lebreton
100 schema:givenName Claude
101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01342563076.00
102 rdf:type schema:Person
103 sg:person.0746114007.76 schema:affiliation grid-institutes:None
104 schema:familyName Haiminen
105 schema:givenName Niina
106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0746114007.76
107 rdf:type schema:Person
108 grid-institutes:None schema:alternateName Computational Biology Center, IBM T. J. Watson Research, USA
109 schema:name Computational Biology Center, IBM T. J. Watson Research, USA
110 rdf:type schema:Organization
111 grid-institutes:grid.464033.6 schema:alternateName Limagrain Europe, Centre de Recherche de Chappes, France
112 schema:name Limagrain Europe, Centre de Recherche de Chappes, France
113 rdf:type schema:Organization
 




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


...