A deep learning classifier for local ancestry inference Article Swipe
Matthew Aguirre
,
Jan Sokol
,
Guhan Venkataraman
,
Alexander Ioannidis
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.02081
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.02081
Local ancestry inference (LAI) identifies the ancestry of each segment of an individual's genome and is an important step in medical and population genetic studies of diverse cohorts. Several techniques have been used for LAI, including Hidden Markov Models and Random Forests. Here, we formulate the LAI task as an image segmentation problem and develop a new LAI tool using a deep convolutional neural network with an encoder-decoder architecture. We train our model using complete genome sequences from 982 unadmixed individuals from each of five continental ancestry groups, and we evaluate it using simulated admixed data derived from an additional 279 individuals selected from the same populations. We show that our model is able to learn admixture as a zero-shot task, yielding ancestry assignments that are nearly as accurate as those from the existing gold standard tool, RFMix.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2011.02081
- https://arxiv.org/pdf/2011.02081
- OA Status
- green
- References
- 19
- Related Works
- 20
- OpenAlex ID
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https://openalex.org/W3096823898Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2011.02081Digital Object Identifier
- Title
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A deep learning classifier for local ancestry inferenceWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
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2020-11-04Full publication date if available
- Authors
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Matthew Aguirre, Jan Sokol, Guhan Venkataraman, Alexander IoannidisList of authors in order
- Landing page
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https://arxiv.org/abs/2011.02081Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2011.02081Direct link to full text PDF
- 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://arxiv.org/pdf/2011.02081Direct OA link when available
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Inference, Classifier (UML), Artificial intelligence, Convolutional neural network, Random forest, Computer science, Segmentation, Machine learning, Population, Deep learning, Pattern recognition (psychology), Sociology, DemographyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.five | 84 |
| abstract_inverted_index.from | 77, 81, 97, 103, 131 |
| abstract_inverted_index.gold | 134 |
| abstract_inverted_index.have | 30 |
| abstract_inverted_index.same | 105 |
| abstract_inverted_index.show | 108 |
| abstract_inverted_index.step | 18 |
| abstract_inverted_index.task | 47 |
| abstract_inverted_index.that | 109, 124 |
| abstract_inverted_index.tool | 58 |
| abstract_inverted_index.used | 32 |
| abstract_inverted_index.with | 65 |
| abstract_inverted_index.(LAI) | 3 |
| abstract_inverted_index.Here, | 42 |
| abstract_inverted_index.Local | 0 |
| abstract_inverted_index.image | 50 |
| abstract_inverted_index.learn | 115 |
| abstract_inverted_index.model | 72, 111 |
| abstract_inverted_index.task, | 120 |
| abstract_inverted_index.those | 130 |
| abstract_inverted_index.tool, | 136 |
| abstract_inverted_index.train | 70 |
| abstract_inverted_index.using | 59, 73, 92 |
| abstract_inverted_index.Hidden | 36 |
| abstract_inverted_index.Markov | 37 |
| abstract_inverted_index.Models | 38 |
| abstract_inverted_index.RFMix. | 137 |
| abstract_inverted_index.Random | 40 |
| abstract_inverted_index.genome | 13, 75 |
| abstract_inverted_index.nearly | 126 |
| abstract_inverted_index.neural | 63 |
| abstract_inverted_index.Several | 28 |
| abstract_inverted_index.admixed | 94 |
| abstract_inverted_index.derived | 96 |
| abstract_inverted_index.develop | 54 |
| abstract_inverted_index.diverse | 26 |
| abstract_inverted_index.genetic | 23 |
| abstract_inverted_index.groups, | 87 |
| abstract_inverted_index.medical | 20 |
| abstract_inverted_index.network | 64 |
| abstract_inverted_index.problem | 52 |
| abstract_inverted_index.segment | 9 |
| abstract_inverted_index.studies | 24 |
| abstract_inverted_index.Forests. | 41 |
| abstract_inverted_index.accurate | 128 |
| abstract_inverted_index.ancestry | 1, 6, 86, 122 |
| abstract_inverted_index.cohorts. | 27 |
| abstract_inverted_index.complete | 74 |
| abstract_inverted_index.evaluate | 90 |
| abstract_inverted_index.existing | 133 |
| abstract_inverted_index.selected | 102 |
| abstract_inverted_index.standard | 135 |
| abstract_inverted_index.yielding | 121 |
| abstract_inverted_index.admixture | 116 |
| abstract_inverted_index.formulate | 44 |
| abstract_inverted_index.important | 17 |
| abstract_inverted_index.including | 35 |
| abstract_inverted_index.inference | 2 |
| abstract_inverted_index.sequences | 76 |
| abstract_inverted_index.simulated | 93 |
| abstract_inverted_index.unadmixed | 79 |
| abstract_inverted_index.zero-shot | 119 |
| abstract_inverted_index.additional | 99 |
| abstract_inverted_index.identifies | 4 |
| abstract_inverted_index.population | 22 |
| abstract_inverted_index.techniques | 29 |
| abstract_inverted_index.assignments | 123 |
| abstract_inverted_index.continental | 85 |
| abstract_inverted_index.individuals | 80, 101 |
| abstract_inverted_index.individual's | 12 |
| abstract_inverted_index.populations. | 106 |
| abstract_inverted_index.segmentation | 51 |
| abstract_inverted_index.architecture. | 68 |
| abstract_inverted_index.convolutional | 62 |
| abstract_inverted_index.encoder-decoder | 67 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |