3Cnet: Pathogenicity prediction of human variants using knowledge transfer with deep recurrent neural networks Article Swipe
Dhong‐gun Won
,
Kyoungyeul Lee
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.09.27.302927
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.09.27.302927
Thanks to the improvement of Next Generation Sequencing (NGS), genome-based diagnosis for rare disease patients become possible. However, accurate interpretation of human variants requires massive amount of knowledge gathered from previous researches and clinical cases. Also, manual analysis for each variant in the genome of patients takes enormous time and effort of clinical experts and medical doctors. Therefore, to reduce the cost of diagnosis, various computational tools have been developed for the pathogenicity prediction of human variants. Nevertheless, there has been the circularity problem of conventional tools, which leads to the overlap of training data and eventually causes overfitting of algorithms. In this research, we developed a pathogenicity predictor, named as 3Cnet, using deep recurrent neural networks which analyzes the amino-acid context of a missense mutation. 3Cnet utilizes knowledge transfer of evolutionary conservation to train insufficient clinical data without overfitting. The performance comparison clearly shows that 3Cnet can find the true disease-causing variant from a large number of missense variants in the genome of a patient with higher sensitivity (recall = 13.9 %) compared to other prediction tools such as REVEL (recall = 7.5 %) or PrimateAI (recall = 6.4 %). Consequently, 3Cnet can improve the diagnostic rate for patients and discover novel pathogenic variants with high probability.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.09.27.302927
- https://www.biorxiv.org/content/biorxiv/early/2020/10/03/2020.09.27.302927.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4244779684
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4244779684Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2020.09.27.302927Digital Object Identifier
- Title
-
3Cnet: Pathogenicity prediction of human variants using knowledge transfer with deep recurrent neural networksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-09-28Full publication date if available
- Authors
-
Dhong‐gun Won, Kyoungyeul LeeList of authors in order
- Landing page
-
https://doi.org/10.1101/2020.09.27.302927Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2020/10/03/2020.09.27.302927.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2020/10/03/2020.09.27.302927.full.pdfDirect OA link when available
- Concepts
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Overfitting, Missense mutation, Pathogenicity, Computer science, Machine learning, Artificial intelligence, Recall, Artificial neural network, Context (archaeology), Genome, Human genome, Mutation, Computational biology, Genetics, Biology, Gene, Philosophy, Paleontology, Microbiology, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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39Number 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.novel | 203 |
| abstract_inverted_index.other | 176 |
| abstract_inverted_index.shows | 145 |
| abstract_inverted_index.takes | 47 |
| abstract_inverted_index.there | 79 |
| abstract_inverted_index.tools | 67, 178 |
| abstract_inverted_index.train | 135 |
| abstract_inverted_index.using | 113 |
| abstract_inverted_index.which | 88, 118 |
| abstract_inverted_index.(NGS), | 9 |
| abstract_inverted_index.3Cnet, | 112 |
| abstract_inverted_index.Thanks | 1 |
| abstract_inverted_index.amount | 26 |
| abstract_inverted_index.become | 16 |
| abstract_inverted_index.cases. | 35 |
| abstract_inverted_index.causes | 98 |
| abstract_inverted_index.effort | 51 |
| abstract_inverted_index.genome | 44, 163 |
| abstract_inverted_index.higher | 168 |
| abstract_inverted_index.manual | 37 |
| abstract_inverted_index.neural | 116 |
| abstract_inverted_index.number | 157 |
| abstract_inverted_index.reduce | 60 |
| abstract_inverted_index.tools, | 87 |
| abstract_inverted_index.(recall | 170, 182, 188 |
| abstract_inverted_index.clearly | 144 |
| abstract_inverted_index.context | 122 |
| abstract_inverted_index.disease | 14 |
| abstract_inverted_index.experts | 54 |
| abstract_inverted_index.improve | 195 |
| abstract_inverted_index.massive | 25 |
| abstract_inverted_index.medical | 56 |
| abstract_inverted_index.overlap | 92 |
| abstract_inverted_index.patient | 166 |
| abstract_inverted_index.problem | 84 |
| abstract_inverted_index.variant | 41, 153 |
| abstract_inverted_index.various | 65 |
| abstract_inverted_index.without | 139 |
| abstract_inverted_index.Abstract | 0 |
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| abstract_inverted_index.accurate | 19 |
| abstract_inverted_index.analysis | 38 |
| abstract_inverted_index.analyzes | 119 |
| abstract_inverted_index.clinical | 34, 53, 137 |
| abstract_inverted_index.compared | 174 |
| abstract_inverted_index.discover | 202 |
| abstract_inverted_index.doctors. | 57 |
| abstract_inverted_index.enormous | 48 |
| abstract_inverted_index.gathered | 29 |
| abstract_inverted_index.missense | 125, 159 |
| abstract_inverted_index.networks | 117 |
| abstract_inverted_index.patients | 15, 46, 200 |
| abstract_inverted_index.previous | 31 |
| abstract_inverted_index.requires | 24 |
| abstract_inverted_index.training | 94 |
| abstract_inverted_index.transfer | 130 |
| abstract_inverted_index.utilizes | 128 |
| abstract_inverted_index.variants | 23, 160, 205 |
| abstract_inverted_index.PrimateAI | 187 |
| abstract_inverted_index.developed | 70, 106 |
| abstract_inverted_index.diagnosis | 11 |
| abstract_inverted_index.knowledge | 28, 129 |
| abstract_inverted_index.mutation. | 126 |
| abstract_inverted_index.possible. | 17 |
| abstract_inverted_index.recurrent | 115 |
| abstract_inverted_index.research, | 104 |
| abstract_inverted_index.variants. | 77 |
| abstract_inverted_index.Generation | 7 |
| abstract_inverted_index.Sequencing | 8 |
| abstract_inverted_index.Therefore, | 58 |
| abstract_inverted_index.amino-acid | 121 |
| abstract_inverted_index.comparison | 143 |
| abstract_inverted_index.diagnosis, | 64 |
| abstract_inverted_index.diagnostic | 197 |
| abstract_inverted_index.eventually | 97 |
| abstract_inverted_index.pathogenic | 204 |
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| abstract_inverted_index.researches | 32 |
| abstract_inverted_index.algorithms. | 101 |
| abstract_inverted_index.circularity | 83 |
| abstract_inverted_index.improvement | 4 |
| abstract_inverted_index.overfitting | 99 |
| abstract_inverted_index.performance | 142 |
| abstract_inverted_index.sensitivity | 169 |
| abstract_inverted_index.conservation | 133 |
| abstract_inverted_index.conventional | 86 |
| abstract_inverted_index.evolutionary | 132 |
| abstract_inverted_index.genome-based | 10 |
| abstract_inverted_index.insufficient | 136 |
| abstract_inverted_index.overfitting. | 140 |
| abstract_inverted_index.probability. | 208 |
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| abstract_inverted_index.Nevertheless, | 78 |
| abstract_inverted_index.computational | 66 |
| abstract_inverted_index.pathogenicity | 73, 108 |
| abstract_inverted_index.interpretation | 20 |
| abstract_inverted_index.disease-causing | 152 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5088228762 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.2352876 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |