Tumor Type Prediction based on Residual Attention Model Article Swipe
Yingmeng Wang
,
Y. Tie
,
Li Qi
,
F Wang
,
L Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1914/1/012029
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1914/1/012029
Early detection of tumors is an important part of cancer treatment. In view of the existing algorithms: single data types, low feature extraction efficiency, and low classification network accuracy. A tumor types prediction model based on deep learning is proposed. The network model uses a Variational auto-encoder (VAE) to fuse the RNA expression and DNA methylation data of 32 tumor types, then uses the Hilbert curve to visualize it. Finally fuse data is sent to classification module: embed the attention module in the backbone network ResNet18 framework, convolutional layer instead of fully connected layer. The new sample is used to predict the tumor type. The experimental results show that this network model has excellent performance in tumor type classification and has important guiding significance for the early diagnosis of tumor patients.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/1914/1/012029
- OA Status
- diamond
- References
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- OpenAlex ID
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https://openalex.org/W3164669933Canonical identifier for this work in OpenAlex
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https://doi.org/10.1088/1742-6596/1914/1/012029Digital Object Identifier
- Title
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Tumor Type Prediction based on Residual Attention ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
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2021-05-01Full publication date if available
- Authors
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Yingmeng Wang, Y. Tie, Li Qi, F Wang, L WangList of authors in order
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https://doi.org/10.1088/1742-6596/1914/1/012029Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1088/1742-6596/1914/1/012029Direct OA link when available
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Fuse (electrical), Computer science, Artificial intelligence, Residual, Deep learning, Feature extraction, Layer (electronics), Sample (material), Pattern recognition (psychology), Algorithm, Engineering, Chromatography, Electrical engineering, Chemistry, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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6Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.view | 13 |
| abstract_inverted_index.(VAE) | 48 |
| abstract_inverted_index.Early | 1 |
| abstract_inverted_index.based | 35 |
| abstract_inverted_index.curve | 66 |
| abstract_inverted_index.early | 127 |
| abstract_inverted_index.embed | 78 |
| abstract_inverted_index.fully | 92 |
| abstract_inverted_index.layer | 89 |
| abstract_inverted_index.model | 34, 43, 112 |
| abstract_inverted_index.tumor | 31, 60, 103, 117, 130 |
| abstract_inverted_index.type. | 104 |
| abstract_inverted_index.types | 32 |
| abstract_inverted_index.cancer | 10 |
| abstract_inverted_index.layer. | 94 |
| abstract_inverted_index.module | 81 |
| abstract_inverted_index.sample | 97 |
| abstract_inverted_index.single | 18 |
| abstract_inverted_index.tumors | 4 |
| abstract_inverted_index.types, | 20, 61 |
| abstract_inverted_index.Finally | 70 |
| abstract_inverted_index.Hilbert | 65 |
| abstract_inverted_index.feature | 22 |
| abstract_inverted_index.guiding | 123 |
| abstract_inverted_index.instead | 90 |
| abstract_inverted_index.module: | 77 |
| abstract_inverted_index.network | 28, 42, 85, 111 |
| abstract_inverted_index.predict | 101 |
| abstract_inverted_index.results | 107 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.ResNet18 | 86 |
| abstract_inverted_index.backbone | 84 |
| abstract_inverted_index.existing | 16 |
| abstract_inverted_index.learning | 38 |
| abstract_inverted_index.accuracy. | 29 |
| abstract_inverted_index.attention | 80 |
| abstract_inverted_index.connected | 93 |
| abstract_inverted_index.detection | 2 |
| abstract_inverted_index.diagnosis | 128 |
| abstract_inverted_index.excellent | 114 |
| abstract_inverted_index.important | 7, 122 |
| abstract_inverted_index.patients. | 131 |
| abstract_inverted_index.proposed. | 40 |
| abstract_inverted_index.visualize | 68 |
| abstract_inverted_index.expression | 53 |
| abstract_inverted_index.extraction | 23 |
| abstract_inverted_index.framework, | 87 |
| abstract_inverted_index.prediction | 33 |
| abstract_inverted_index.treatment. | 11 |
| abstract_inverted_index.Variational | 46 |
| abstract_inverted_index.algorithms: | 17 |
| abstract_inverted_index.efficiency, | 24 |
| abstract_inverted_index.methylation | 56 |
| abstract_inverted_index.performance | 115 |
| abstract_inverted_index.auto-encoder | 47 |
| abstract_inverted_index.experimental | 106 |
| abstract_inverted_index.significance | 124 |
| abstract_inverted_index.convolutional | 88 |
| abstract_inverted_index.classification | 27, 76, 119 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.05938347 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |