MIC: Mutual Information Based Hierarchical Clustering View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2009

AUTHORS

Alexander Kraskov , Peter Grassberger

ABSTRACT

Clustering is a concept used in a huge variety of applications. We review a conceptually very simple algorithm for hierarchical clustering called in the following the mutual information clustering (MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits its grouping property: The MI between three objects X,Y, and Z is equal to the sum of the MI between X and Y, plus the MI between Z and the combined object (XY). We use MIC both in the Shannon (probabilistic) version of information theory, where the “objects” are probability distributions represented by random samples, and in the Kolmogorov (algorithmic) version, where the “objects” are symbol sequences. We apply our method to the construction of phylogenetic trees from mitochondrial DNA sequences and we reconstruct the fetal ECG from the output of independent components analysis (ICA) applied to the ECG of a pregnant woman. More... »

PAGES

101-123

Book

TITLE

Information Theory and Statistical Learning

ISBN

978-0-387-84815-0
978-0-387-84816-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-0-387-84816-7_5

DOI

http://dx.doi.org/10.1007/978-0-387-84816-7_5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1030096628


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": "University College London", 
          "id": "https://www.grid.ac/institutes/grid.83440.3b", 
          "name": [
            "UCL Institute of Neurology, Queen Square, WC1N 3BG, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kraskov", 
        "givenName": "Alexander", 
        "id": "sg:person.01236123571.67", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01236123571.67"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Calgary", 
          "id": "https://www.grid.ac/institutes/grid.22072.35", 
          "name": [
            "Department of Physics and Astronomy and Institute for Biocomplexity and Informatics, University of Calgary, 2500 University Drive NW, T2N 1N4, Calgary AB, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Grassberger", 
        "givenName": "Peter", 
        "id": "sg:person.0704113004.84", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0704113004.84"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1080/03610919908813564", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000013660"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/oxfordjournals.molbev.a025664", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000153997"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/pl00006389", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002978225", 
          "https://doi.org/10.1007/pl00006389"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/oxfordjournals.molbev.a026379", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004955946"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/17.2.149", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009738168"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-5-118", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016807484", 
          "https://doi.org/10.1186/1471-2105-5-118"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-5-118", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016807484", 
          "https://doi.org/10.1186/1471-2105-5-118"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.1960404", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017077573"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-7152(94)90046-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019135538"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1025044546", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-2606-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025044546", 
          "https://doi.org/10.1007/978-1-4757-2606-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-2606-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025044546", 
          "https://doi.org/10.1007/978-1-4757-2606-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/molbev/msh033", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030011474"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.69.066138", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032496880"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.69.066138", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032496880"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.70.066123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040975266"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.70.066123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040975266"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bfb0091924", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049441366", 
          "https://doi.org/10.1007/bfb0091924"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/03610929508831626", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058336028"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.52.2318", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060718310"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.52.2318", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060718310"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/18.761290", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061100976"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.2004.838101", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061650298"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.2005.844059", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061650455"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/1103036", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062864227"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1209/epl/i2004-10483-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064237240"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icnc.2007.78", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094894288"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0471221317", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098661117"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0471221317", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098661117"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0471200611", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098661155"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0471200611", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098661155"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.2517-6161.1976.tb01566.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110458017"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.2517-6161.1976.tb01566.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110458017"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2009", 
    "datePublishedReg": "2009-01-01", 
    "description": "Clustering is a concept used in a huge variety of applications. We review a conceptually very simple algorithm for hierarchical clustering called in the following the mutual information clustering (MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits its grouping property: The MI between three objects X,Y, and Z is equal to the sum of the MI between X and Y, plus the MI between Z and the combined object (XY). We use MIC both in the Shannon (probabilistic) version of information theory, where the \u201cobjects\u201d are probability distributions represented by random samples, and in the Kolmogorov (algorithmic) version, where the \u201cobjects\u201d are symbol sequences. We apply our method to the construction of phylogenetic trees from mitochondrial DNA sequences and we reconstruct the fetal ECG from the output of independent components analysis (ICA) applied to the ECG of a pregnant woman.", 
    "editor": [
      {
        "familyName": "Emmert-Streib", 
        "givenName": "Frank", 
        "type": "Person"
      }, 
      {
        "familyName": "Dehmer", 
        "givenName": "Matthias", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-0-387-84816-7_5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-0-387-84815-0", 
        "978-0-387-84816-7"
      ], 
      "name": "Information Theory and Statistical Learning", 
      "type": "Book"
    }, 
    "name": "MIC: Mutual Information Based Hierarchical Clustering", 
    "pagination": "101-123", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-0-387-84816-7_5"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "8f60bae8a6958b693c5df78f38e1196942c1b6e52203933825c4a6a335cd39fc"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1030096628"
        ]
      }
    ], 
    "publisher": {
      "location": "Boston, MA", 
      "name": "Springer US", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-0-387-84816-7_5", 
      "https://app.dimensions.ai/details/publication/pub.1030096628"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T06:15", 
    "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/v90/0000000351_0000000351/records_43253_00000000.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-0-387-84816-7_5"
  }
]
 

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.10.1007/978-0-387-84816-7_5'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-0-387-84816-7_5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-0-387-84816-7_5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-0-387-84816-7_5'


 

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

158 TRIPLES      23 PREDICATES      52 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-0-387-84816-7_5 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Na9bdbf2a532b4126804832cac0af4d2c
4 schema:citation sg:pub.10.1007/978-1-4757-2606-0
5 sg:pub.10.1007/bfb0091924
6 sg:pub.10.1007/pl00006389
7 sg:pub.10.1186/1471-2105-5-118
8 https://app.dimensions.ai/details/publication/pub.1025044546
9 https://doi.org/10.1002/0471200611
10 https://doi.org/10.1002/0471221317
11 https://doi.org/10.1016/0167-7152(94)90046-9
12 https://doi.org/10.1080/03610919908813564
13 https://doi.org/10.1080/03610929508831626
14 https://doi.org/10.1093/bioinformatics/17.2.149
15 https://doi.org/10.1093/molbev/msh033
16 https://doi.org/10.1093/oxfordjournals.molbev.a025664
17 https://doi.org/10.1093/oxfordjournals.molbev.a026379
18 https://doi.org/10.1101/gr.1960404
19 https://doi.org/10.1103/physreve.52.2318
20 https://doi.org/10.1103/physreve.69.066138
21 https://doi.org/10.1103/physreve.70.066123
22 https://doi.org/10.1109/18.761290
23 https://doi.org/10.1109/icnc.2007.78
24 https://doi.org/10.1109/tit.2004.838101
25 https://doi.org/10.1109/tit.2005.844059
26 https://doi.org/10.1111/j.2517-6161.1976.tb01566.x
27 https://doi.org/10.1137/1103036
28 https://doi.org/10.1209/epl/i2004-10483-y
29 schema:datePublished 2009
30 schema:datePublishedReg 2009-01-01
31 schema:description Clustering is a concept used in a huge variety of applications. We review a conceptually very simple algorithm for hierarchical clustering called in the following the mutual information clustering (MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits its grouping property: The MI between three objects X,Y, and Z is equal to the sum of the MI between X and Y, plus the MI between Z and the combined object (XY). We use MIC both in the Shannon (probabilistic) version of information theory, where the “objects” are probability distributions represented by random samples, and in the Kolmogorov (algorithmic) version, where the “objects” are symbol sequences. We apply our method to the construction of phylogenetic trees from mitochondrial DNA sequences and we reconstruct the fetal ECG from the output of independent components analysis (ICA) applied to the ECG of a pregnant woman.
32 schema:editor Na72260bb9ed14e4bab3d1ba4ada218cf
33 schema:genre chapter
34 schema:inLanguage en
35 schema:isAccessibleForFree true
36 schema:isPartOf N224cd9c4c6d648d5b6c10befedfe855e
37 schema:name MIC: Mutual Information Based Hierarchical Clustering
38 schema:pagination 101-123
39 schema:productId N31070329208e4a52bd23044d9f5a650e
40 N48f3fcefbc524a51804f4faaa1d4f169
41 Ne90f0548c48a443b8f6f87847aa36ded
42 schema:publisher N804ac7ecd5294887b950f400bcf9aa45
43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030096628
44 https://doi.org/10.1007/978-0-387-84816-7_5
45 schema:sdDatePublished 2019-04-16T06:15
46 schema:sdLicense https://scigraph.springernature.com/explorer/license/
47 schema:sdPublisher N1ce9d52a283c4ca18e964c191ca7ec7e
48 schema:url https://link.springer.com/10.1007%2F978-0-387-84816-7_5
49 sgo:license sg:explorer/license/
50 sgo:sdDataset chapters
51 rdf:type schema:Chapter
52 N1ce9d52a283c4ca18e964c191ca7ec7e schema:name Springer Nature - SN SciGraph project
53 rdf:type schema:Organization
54 N224cd9c4c6d648d5b6c10befedfe855e schema:isbn 978-0-387-84815-0
55 978-0-387-84816-7
56 schema:name Information Theory and Statistical Learning
57 rdf:type schema:Book
58 N31070329208e4a52bd23044d9f5a650e schema:name readcube_id
59 schema:value 8f60bae8a6958b693c5df78f38e1196942c1b6e52203933825c4a6a335cd39fc
60 rdf:type schema:PropertyValue
61 N35412116cc954081b4baf414f9e365ae schema:familyName Emmert-Streib
62 schema:givenName Frank
63 rdf:type schema:Person
64 N48f3fcefbc524a51804f4faaa1d4f169 schema:name dimensions_id
65 schema:value pub.1030096628
66 rdf:type schema:PropertyValue
67 N7c6093400313408bb188d44f65496621 rdf:first Nf0eb90d6386247c688ccdecb5f44ecf0
68 rdf:rest rdf:nil
69 N804ac7ecd5294887b950f400bcf9aa45 schema:location Boston, MA
70 schema:name Springer US
71 rdf:type schema:Organisation
72 Na72260bb9ed14e4bab3d1ba4ada218cf rdf:first N35412116cc954081b4baf414f9e365ae
73 rdf:rest N7c6093400313408bb188d44f65496621
74 Na9bdbf2a532b4126804832cac0af4d2c rdf:first sg:person.01236123571.67
75 rdf:rest Nf1fc33ce41ec4c04bea9e52d4216cdde
76 Ne90f0548c48a443b8f6f87847aa36ded schema:name doi
77 schema:value 10.1007/978-0-387-84816-7_5
78 rdf:type schema:PropertyValue
79 Nf0eb90d6386247c688ccdecb5f44ecf0 schema:familyName Dehmer
80 schema:givenName Matthias
81 rdf:type schema:Person
82 Nf1fc33ce41ec4c04bea9e52d4216cdde rdf:first sg:person.0704113004.84
83 rdf:rest rdf:nil
84 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
85 schema:name Mathematical Sciences
86 rdf:type schema:DefinedTerm
87 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
88 schema:name Statistics
89 rdf:type schema:DefinedTerm
90 sg:person.01236123571.67 schema:affiliation https://www.grid.ac/institutes/grid.83440.3b
91 schema:familyName Kraskov
92 schema:givenName Alexander
93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01236123571.67
94 rdf:type schema:Person
95 sg:person.0704113004.84 schema:affiliation https://www.grid.ac/institutes/grid.22072.35
96 schema:familyName Grassberger
97 schema:givenName Peter
98 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0704113004.84
99 rdf:type schema:Person
100 sg:pub.10.1007/978-1-4757-2606-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025044546
101 https://doi.org/10.1007/978-1-4757-2606-0
102 rdf:type schema:CreativeWork
103 sg:pub.10.1007/bfb0091924 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049441366
104 https://doi.org/10.1007/bfb0091924
105 rdf:type schema:CreativeWork
106 sg:pub.10.1007/pl00006389 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002978225
107 https://doi.org/10.1007/pl00006389
108 rdf:type schema:CreativeWork
109 sg:pub.10.1186/1471-2105-5-118 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016807484
110 https://doi.org/10.1186/1471-2105-5-118
111 rdf:type schema:CreativeWork
112 https://app.dimensions.ai/details/publication/pub.1025044546 schema:CreativeWork
113 https://doi.org/10.1002/0471200611 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098661155
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1002/0471221317 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098661117
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1016/0167-7152(94)90046-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019135538
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1080/03610919908813564 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000013660
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1080/03610929508831626 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058336028
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1093/bioinformatics/17.2.149 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009738168
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1093/molbev/msh033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030011474
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1093/oxfordjournals.molbev.a025664 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000153997
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1093/oxfordjournals.molbev.a026379 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004955946
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1101/gr.1960404 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017077573
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1103/physreve.52.2318 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060718310
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1103/physreve.69.066138 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032496880
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1103/physreve.70.066123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040975266
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1109/18.761290 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061100976
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1109/icnc.2007.78 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094894288
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1109/tit.2004.838101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061650298
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1109/tit.2005.844059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061650455
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1111/j.2517-6161.1976.tb01566.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1110458017
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1137/1103036 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062864227
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1209/epl/i2004-10483-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1064237240
152 rdf:type schema:CreativeWork
153 https://www.grid.ac/institutes/grid.22072.35 schema:alternateName University of Calgary
154 schema:name Department of Physics and Astronomy and Institute for Biocomplexity and Informatics, University of Calgary, 2500 University Drive NW, T2N 1N4, Calgary AB, Canada
155 rdf:type schema:Organization
156 https://www.grid.ac/institutes/grid.83440.3b schema:alternateName University College London
157 schema:name UCL Institute of Neurology, Queen Square, WC1N 3BG, London, UK
158 rdf:type schema:Organization
 




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


...