Statistical methods for detecting latent periodicity patterns in biological sequences: The case of small-size samples View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2009-06

AUTHORS

M. B. Chaley, N. N. Nazipova, V. A. Kutyrkin

ABSTRACT

An original spectral-statistical approach for detecting latent periodicity in biological sequences is proposed. This approach can be applied under conditions of limited statistical sample. It allows one to avoid redundancy and instability when identifying the latent periodicity structure. The optimality of the periodicity-pattern-size estimates obtained for approximate tandem repeats on the basis of the spectral-statistical approach is demonstrated in practical examples. More... »

PAGES

358-367

Identifiers

URI

http://scigraph.springernature.com/pub.10.1134/s1054661809020217

DOI

http://dx.doi.org/10.1134/s1054661809020217

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003595627


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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Russian Academy of Sciences", 
          "id": "https://www.grid.ac/institutes/grid.4886.2", 
          "name": [
            "Institute of Mathematical Biology Problems, Russian Academy of Sciences, Institutskaya ul. 4, Pushchino, Moscow oblast, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chaley", 
        "givenName": "M. B.", 
        "id": "sg:person.01340665320.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01340665320.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Russian Academy of Sciences", 
          "id": "https://www.grid.ac/institutes/grid.4886.2", 
          "name": [
            "Institute of Mathematical Biology Problems, Russian Academy of Sciences, Institutskaya ul. 4, Pushchino, Moscow oblast, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nazipova", 
        "givenName": "N. N.", 
        "id": "sg:person.0712415313.62", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0712415313.62"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Bauman Moscow State Technical University", 
          "id": "https://www.grid.ac/institutes/grid.61569.3d", 
          "name": [
            "Moscow State Technical University n.a. N.E. Bauman, ul. Vtoraya Baumanskaya 5, 107005, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kutyrkin", 
        "givenName": "V. A.", 
        "id": "sg:person.01303174670.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01303174670.40"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/bies.10324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002860395"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0375-9601(03)00641-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004343633"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0375-9601(03)00641-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004343633"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s008940050122", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007337251", 
          "https://doi.org/10.1007/s008940050122"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.mbs.2007.10.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009158998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bti059", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010538229"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-7-336", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012634589", 
          "https://doi.org/10.1186/1471-2105-7-336"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-7-336", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012634589", 
          "https://doi.org/10.1186/1471-2105-7-336"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-5193(86)80060-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018681514"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/dnares/dsl004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019656483"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btk032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022495316"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00239-002-2462-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023701288", 
          "https://doi.org/10.1007/s00239-002-2462-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jtbi.2000.2127", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025368249"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-6-145", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027047224", 
          "https://doi.org/10.1186/1471-2105-6-145"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-6-145", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027047224", 
          "https://doi.org/10.1186/1471-2105-6-145"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-5193(81)90274-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029993712"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/molbev/msk022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030992317"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/27.2.573", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035372973"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bth103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035869545"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkg617", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037102854"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/dnares/6.3.153", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039190731"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1110/ps.9.6.1203", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039572381"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/18.4.634", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041370610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-5-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041586173", 
          "https://doi.org/10.1186/1471-2105-5-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-5-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041586173", 
          "https://doi.org/10.1186/1471-2105-5-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1050-3862(96)80001-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044367984"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/18.suppl_1.s31", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047709202"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2180-1-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048910095", 
          "https://doi.org/10.1186/1471-2180-1-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/18.suppl_2.s44", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051874023"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0097-8485(97)00022-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052320188"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/106652701300099038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059204869"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/cmb.2006.13.946", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059245516"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2009-06", 
    "datePublishedReg": "2009-06-01", 
    "description": "An original spectral-statistical approach for detecting latent periodicity in biological sequences is proposed. This approach can be applied under conditions of limited statistical sample. It allows one to avoid redundancy and instability when identifying the latent periodicity structure. The optimality of the periodicity-pattern-size estimates obtained for approximate tandem repeats on the basis of the spectral-statistical approach is demonstrated in practical examples.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1134/s1054661809020217", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136125", 
        "issn": [
          "1054-6618", 
          "1555-6212"
        ], 
        "name": "Pattern Recognition and Image Analysis", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "19"
      }
    ], 
    "name": "Statistical methods for detecting latent periodicity patterns in biological sequences: The case of small-size samples", 
    "pagination": "358-367", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "1120ea0b0b3fdc60f640bbb7d67b096fdbb7c25a85fb93354cceb2c4f6603f39"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1134/s1054661809020217"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1003595627"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1134/s1054661809020217", 
      "https://app.dimensions.ai/details/publication/pub.1003595627"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T22:37", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8690_00000536.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1134%2FS1054661809020217"
  }
]
 

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.10.1134/s1054661809020217'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1134/s1054661809020217'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1134/s1054661809020217'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1134/s1054661809020217'


 

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

168 TRIPLES      21 PREDICATES      55 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1134/s1054661809020217 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Nf750a788b74945b48a54ca0cc9c76c78
4 schema:citation sg:pub.10.1007/s00239-002-2462-1
5 sg:pub.10.1007/s008940050122
6 sg:pub.10.1186/1471-2105-5-4
7 sg:pub.10.1186/1471-2105-6-145
8 sg:pub.10.1186/1471-2105-7-336
9 sg:pub.10.1186/1471-2180-1-2
10 https://doi.org/10.1002/bies.10324
11 https://doi.org/10.1006/jtbi.2000.2127
12 https://doi.org/10.1016/0022-5193(81)90274-5
13 https://doi.org/10.1016/j.mbs.2007.10.008
14 https://doi.org/10.1016/s0022-5193(86)80060-1
15 https://doi.org/10.1016/s0097-8485(97)00022-3
16 https://doi.org/10.1016/s0375-9601(03)00641-8
17 https://doi.org/10.1016/s1050-3862(96)80001-5
18 https://doi.org/10.1089/106652701300099038
19 https://doi.org/10.1089/cmb.2006.13.946
20 https://doi.org/10.1093/bioinformatics/18.4.634
21 https://doi.org/10.1093/bioinformatics/18.suppl_1.s31
22 https://doi.org/10.1093/bioinformatics/18.suppl_2.s44
23 https://doi.org/10.1093/bioinformatics/bth103
24 https://doi.org/10.1093/bioinformatics/bti059
25 https://doi.org/10.1093/bioinformatics/btk032
26 https://doi.org/10.1093/dnares/6.3.153
27 https://doi.org/10.1093/dnares/dsl004
28 https://doi.org/10.1093/molbev/msk022
29 https://doi.org/10.1093/nar/27.2.573
30 https://doi.org/10.1093/nar/gkg617
31 https://doi.org/10.1110/ps.9.6.1203
32 schema:datePublished 2009-06
33 schema:datePublishedReg 2009-06-01
34 schema:description An original spectral-statistical approach for detecting latent periodicity in biological sequences is proposed. This approach can be applied under conditions of limited statistical sample. It allows one to avoid redundancy and instability when identifying the latent periodicity structure. The optimality of the periodicity-pattern-size estimates obtained for approximate tandem repeats on the basis of the spectral-statistical approach is demonstrated in practical examples.
35 schema:genre research_article
36 schema:inLanguage en
37 schema:isAccessibleForFree false
38 schema:isPartOf N43fa70151d0240a7ac2d101f31f76bbd
39 Nd606037748a7407086d69fc41fa4fd9f
40 sg:journal.1136125
41 schema:name Statistical methods for detecting latent periodicity patterns in biological sequences: The case of small-size samples
42 schema:pagination 358-367
43 schema:productId N95e3f502afab429892856d757aac1b11
44 Nb0d2539a6e2142c1b84335276974eb95
45 Nb15f7afaeb2f40c5a805e8e2506478ec
46 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003595627
47 https://doi.org/10.1134/s1054661809020217
48 schema:sdDatePublished 2019-04-10T22:37
49 schema:sdLicense https://scigraph.springernature.com/explorer/license/
50 schema:sdPublisher N75a0ac81832848c19a4f1836434aff2c
51 schema:url http://link.springer.com/10.1134%2FS1054661809020217
52 sgo:license sg:explorer/license/
53 sgo:sdDataset articles
54 rdf:type schema:ScholarlyArticle
55 N43fa70151d0240a7ac2d101f31f76bbd schema:issueNumber 2
56 rdf:type schema:PublicationIssue
57 N4c95bfc265ec4f2091966930b855070a rdf:first sg:person.01303174670.40
58 rdf:rest rdf:nil
59 N75a0ac81832848c19a4f1836434aff2c schema:name Springer Nature - SN SciGraph project
60 rdf:type schema:Organization
61 N95e3f502afab429892856d757aac1b11 schema:name dimensions_id
62 schema:value pub.1003595627
63 rdf:type schema:PropertyValue
64 Nb0d2539a6e2142c1b84335276974eb95 schema:name doi
65 schema:value 10.1134/s1054661809020217
66 rdf:type schema:PropertyValue
67 Nb15f7afaeb2f40c5a805e8e2506478ec schema:name readcube_id
68 schema:value 1120ea0b0b3fdc60f640bbb7d67b096fdbb7c25a85fb93354cceb2c4f6603f39
69 rdf:type schema:PropertyValue
70 Nb191672292084ba584fbdaebafa5d5eb rdf:first sg:person.0712415313.62
71 rdf:rest N4c95bfc265ec4f2091966930b855070a
72 Nd606037748a7407086d69fc41fa4fd9f schema:volumeNumber 19
73 rdf:type schema:PublicationVolume
74 Nf750a788b74945b48a54ca0cc9c76c78 rdf:first sg:person.01340665320.14
75 rdf:rest Nb191672292084ba584fbdaebafa5d5eb
76 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
77 schema:name Mathematical Sciences
78 rdf:type schema:DefinedTerm
79 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
80 schema:name Statistics
81 rdf:type schema:DefinedTerm
82 sg:journal.1136125 schema:issn 1054-6618
83 1555-6212
84 schema:name Pattern Recognition and Image Analysis
85 rdf:type schema:Periodical
86 sg:person.01303174670.40 schema:affiliation https://www.grid.ac/institutes/grid.61569.3d
87 schema:familyName Kutyrkin
88 schema:givenName V. A.
89 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01303174670.40
90 rdf:type schema:Person
91 sg:person.01340665320.14 schema:affiliation https://www.grid.ac/institutes/grid.4886.2
92 schema:familyName Chaley
93 schema:givenName M. B.
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01340665320.14
95 rdf:type schema:Person
96 sg:person.0712415313.62 schema:affiliation https://www.grid.ac/institutes/grid.4886.2
97 schema:familyName Nazipova
98 schema:givenName N. N.
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0712415313.62
100 rdf:type schema:Person
101 sg:pub.10.1007/s00239-002-2462-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023701288
102 https://doi.org/10.1007/s00239-002-2462-1
103 rdf:type schema:CreativeWork
104 sg:pub.10.1007/s008940050122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007337251
105 https://doi.org/10.1007/s008940050122
106 rdf:type schema:CreativeWork
107 sg:pub.10.1186/1471-2105-5-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041586173
108 https://doi.org/10.1186/1471-2105-5-4
109 rdf:type schema:CreativeWork
110 sg:pub.10.1186/1471-2105-6-145 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027047224
111 https://doi.org/10.1186/1471-2105-6-145
112 rdf:type schema:CreativeWork
113 sg:pub.10.1186/1471-2105-7-336 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012634589
114 https://doi.org/10.1186/1471-2105-7-336
115 rdf:type schema:CreativeWork
116 sg:pub.10.1186/1471-2180-1-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048910095
117 https://doi.org/10.1186/1471-2180-1-2
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1002/bies.10324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002860395
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1006/jtbi.2000.2127 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025368249
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/0022-5193(81)90274-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029993712
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/j.mbs.2007.10.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009158998
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/s0022-5193(86)80060-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018681514
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1016/s0097-8485(97)00022-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052320188
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/s0375-9601(03)00641-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004343633
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/s1050-3862(96)80001-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044367984
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1089/106652701300099038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059204869
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1089/cmb.2006.13.946 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059245516
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1093/bioinformatics/18.4.634 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041370610
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1093/bioinformatics/18.suppl_1.s31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047709202
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1093/bioinformatics/18.suppl_2.s44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051874023
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1093/bioinformatics/bth103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035869545
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1093/bioinformatics/bti059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010538229
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1093/bioinformatics/btk032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022495316
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1093/dnares/6.3.153 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039190731
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1093/dnares/dsl004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019656483
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1093/molbev/msk022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030992317
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1093/nar/27.2.573 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035372973
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1093/nar/gkg617 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037102854
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1110/ps.9.6.1203 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039572381
162 rdf:type schema:CreativeWork
163 https://www.grid.ac/institutes/grid.4886.2 schema:alternateName Russian Academy of Sciences
164 schema:name Institute of Mathematical Biology Problems, Russian Academy of Sciences, Institutskaya ul. 4, Pushchino, Moscow oblast, Russia
165 rdf:type schema:Organization
166 https://www.grid.ac/institutes/grid.61569.3d schema:alternateName Bauman Moscow State Technical University
167 schema:name Moscow State Technical University n.a. N.E. Bauman, ul. Vtoraya Baumanskaya 5, 107005, Moscow, Russia
168 rdf:type schema:Organization
 




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


...