Effective expression analysis using gene interaction matrices and convolutional neural networks Article Swipe
Arvind Pillai
,
Piotr Grabowski
,
Bino John
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.09.07.459284
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.09.07.459284
Artificial intelligence recently experienced a renaissance with the advancement of convolutional neural networks (CNNs). CNNs require spatially meaningful matrices ( e.g ., image data) with recurring patterns, limiting its applicability to high-throughput omics data. We present GIM, a simple, CNN-ready framework for omics data to detect both individual and network-level entities of biological importance. Using gene expression data, we show that GIM-CNNs can outperform comparable neural networks in performance and their design facilitates network-level interpretability. GIM-CNNs provide a means to discover novel disease-relevant factors beyond individual genes and their expression, factors that are likely missed by standard differential gene expression approaches.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.09.07.459284
- https://www.biorxiv.org/content/biorxiv/early/2021/09/07/2021.09.07.459284.full.pdf
- OA Status
- green
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3197373123
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3197373123Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2021.09.07.459284Digital Object Identifier
- Title
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Effective expression analysis using gene interaction matrices and convolutional neural networksWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-09-07Full publication date if available
- Authors
-
Arvind Pillai, Piotr Grabowski, Bino JohnList of authors in order
- Landing page
-
https://doi.org/10.1101/2021.09.07.459284Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/09/07/2021.09.07.459284.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2021/09/07/2021.09.07.459284.full.pdfDirect OA link when available
- Concepts
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Interpretability, Convolutional neural network, Computer science, Artificial intelligence, Expression (computer science), Machine learning, Limiting, Artificial neural network, Data mining, Computational biology, Pattern recognition (psychology), Biology, Mechanical engineering, Programming language, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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19Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.expression | 57, 100 |
| abstract_inverted_index.individual | 48, 86 |
| abstract_inverted_index.meaningful | 18 |
| abstract_inverted_index.outperform | 64 |
| abstract_inverted_index.advancement | 9 |
| abstract_inverted_index.approaches. | 101 |
| abstract_inverted_index.experienced | 4 |
| abstract_inverted_index.expression, | 90 |
| abstract_inverted_index.facilitates | 73 |
| abstract_inverted_index.importance. | 54 |
| abstract_inverted_index.performance | 69 |
| abstract_inverted_index.renaissance | 6 |
| abstract_inverted_index.differential | 98 |
| abstract_inverted_index.intelligence | 2 |
| abstract_inverted_index.applicability | 30 |
| abstract_inverted_index.convolutional | 11 |
| abstract_inverted_index.network-level | 50, 74 |
| abstract_inverted_index.high-throughput | 32 |
| abstract_inverted_index.disease-relevant | 83 |
| abstract_inverted_index.interpretability. | 75 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5113997880 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I4210092243 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.09273038 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |