Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders Article Swipe
Rui Shen
,
Jacob A. Alappatt
,
Drew Parker
,
Junghoon Kim
,
Ragini Verma
,
Yusuf Osmanlıoğlu
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-030-60365-6_13
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-030-60365-6_13
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/978-3-030-60365-6_13
- OA Status
- green
- Cited By
- 7
- References
- 28
- Related Works
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- OpenAlex ID
- https://openalex.org/W3092129332
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3092129332Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/978-3-030-60365-6_13Digital Object Identifier
- Title
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Graph Matching Based Connectomic Biomarker with Learning for Brain DisordersWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-01-01Full publication date if available
- Authors
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Rui Shen, Jacob A. Alappatt, Drew Parker, Junghoon Kim, Ragini Verma, Yusuf OsmanlıoğluList of authors in order
- Landing page
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https://doi.org/10.1007/978-3-030-60365-6_13Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://www.ncbi.nlm.nih.gov/pmc/articles/8329857Direct OA link when available
- Concepts
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Computer science, Graph, Matching (statistics), Artificial intelligence, Machine learning, Theoretical computer science, Medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2023: 1, 2022: 2, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2549362566, https://openalex.org/W2076750015, https://openalex.org/W2041270426, https://openalex.org/W2109294083, https://openalex.org/W2111902267, https://openalex.org/W3045153201, https://openalex.org/W1983681808, https://openalex.org/W1965173583, https://openalex.org/W2277151310, https://openalex.org/W6608002586, https://openalex.org/W2591711955, https://openalex.org/W3009981818, https://openalex.org/W3028531034, https://openalex.org/W2890904741, https://openalex.org/W2946339062, https://openalex.org/W2531573594, https://openalex.org/W1968906215, https://openalex.org/W2170607286, https://openalex.org/W2951617899, https://openalex.org/W2970282997, https://openalex.org/W1996405315, https://openalex.org/W4295750005, https://openalex.org/W1997298866, https://openalex.org/W2054226185, https://openalex.org/W4214894679, https://openalex.org/W1533179050, https://openalex.org/W1498940341, https://openalex.org/W2067456724 |
| referenced_works_count | 28 |
| abstract_inverted_index | |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.8762363 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |