DeepGAMI: Deep auxiliary learning for multi-modal integration and estimation to improve genotype-phenotype prediction Article Swipe
Pramod Bharadwaj Chandrashekar
,
Chenfeng He
,
Ting Jin
,
Sayali Alatkar
,
Saniya Khullar
,
Daifeng Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.6568486
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.6568486
Genotype-phenotype association is found in many biological systems such as brains and brain diseases. However, predicting phenotypes from genotypes remains challenging, primarily due to complex underlying molecular and cellular mechanisms. Emerging multi-modal data enables studying such mechanisms at different scales. However, most of these approaches fail to incorporate biology into the machine learning models. Due to the black-box nature of many machine learning techniques, it is challenging to integrate these multi-modalities and interpret the results for biological insights, especially when some modality is missing. \n\nTo this end, we developed DeepGAMI, an interpretable deep learning model to improve genotype-phenotype prediction from multi-modal data. DeepGAMI uses prior biological knowledge to define the neural network architecture. Notably, it embeds an auxiliary-learning layer for cross-modal imputation while training the model from multi-modal data. Using this pre-trained layer, we can impute latent features of additional modalities and thus enable predicting phenotypes from a single modality only. Finally, the model uses integrated gradient approach to prioritize multi-modal features and links for phenotypes. We applied DeepGAMI to (1) population-level bulk and cell-type-specific genotype and gene expression data for Schizophrenia (SCZ) cohort, (2) genotype and gene expression data for Alzheimer's Disease (AD) cohort, and (3) recent single-cell multi-modal data comprising transcriptomics and electrophysiology for neuronal cells in the mouse visual cortex. We found that DeepGAMI outperforms existing state-of-the-art methods and provides a profound understanding of gene regulatory mechanisms at cellular resolution from genotype to phenotype. DeepGAMI is an open-source tool and is available at https://github.com/daifengwanglab/DeepGAMI.
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- en
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- OpenAlex ID
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https://openalex.org/W4281253966Canonical identifier for this work in OpenAlex
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https://doi.org/10.5281/zenodo.6568486Digital Object Identifier
- Title
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DeepGAMI: Deep auxiliary learning for multi-modal integration and estimation to improve genotype-phenotype predictionWork title
- Type
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articleOpenAlex work type
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enPrimary language
- Publication year
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2022Year of publication
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2022-05-21Full publication date if available
- Authors
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Pramod Bharadwaj Chandrashekar, Chenfeng He, Ting Jin, Sayali Alatkar, Saniya Khullar, Daifeng WangList of authors in order
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https://zenodo.org/record/6568486Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://zenodo.org/record/6568486Direct OA link when available
- Concepts
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Modal, Estimation, Genotype, Deep learning, Phenotype, Artificial intelligence, Computer science, Computational biology, Machine learning, Biology, Genetics, Engineering, Gene, Chemistry, Polymer chemistry, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 23, 46, 55, 67, 95, 107, 158, 169, 235 |
| abstract_inverted_index.we | 87, 133 |
| abstract_inverted_index.(1) | 170 |
| abstract_inverted_index.(2) | 184 |
| abstract_inverted_index.(3) | 196 |
| abstract_inverted_index.Due | 54 |
| abstract_inverted_index.and | 11, 27, 71, 141, 162, 173, 176, 186, 195, 203, 221, 242 |
| abstract_inverted_index.can | 134 |
| abstract_inverted_index.due | 22 |
| abstract_inverted_index.for | 75, 119, 164, 180, 190, 205 |
| abstract_inverted_index.the | 50, 56, 73, 109, 124, 152, 209 |
| abstract_inverted_index.(AD) | 193 |
| abstract_inverted_index.bulk | 172 |
| abstract_inverted_index.data | 32, 179, 189, 200 |
| abstract_inverted_index.deep | 92 |
| abstract_inverted_index.end, | 86 |
| abstract_inverted_index.fail | 45 |
| abstract_inverted_index.from | 17, 99, 126, 146, 233 |
| abstract_inverted_index.gene | 177, 187, 227 |
| abstract_inverted_index.into | 49 |
| abstract_inverted_index.many | 5, 60 |
| abstract_inverted_index.most | 41 |
| abstract_inverted_index.some | 80 |
| abstract_inverted_index.such | 8, 35 |
| abstract_inverted_index.that | 215 |
| abstract_inverted_index.this | 85, 130 |
| abstract_inverted_index.thus | 142 |
| abstract_inverted_index.tool | 241 |
| abstract_inverted_index.uses | 103, 154 |
| abstract_inverted_index.when | 79 |
| abstract_inverted_index.(SCZ) | 182 |
| abstract_inverted_index.Using | 129 |
| abstract_inverted_index.brain | 12 |
| abstract_inverted_index.cells | 207 |
| abstract_inverted_index.data. | 101, 128 |
| abstract_inverted_index.found | 3, 214 |
| abstract_inverted_index.layer | 118 |
| abstract_inverted_index.links | 163 |
| abstract_inverted_index.model | 94, 125, 153 |
| abstract_inverted_index.mouse | 210 |
| abstract_inverted_index.only. | 150 |
| abstract_inverted_index.prior | 104 |
| abstract_inverted_index.these | 43, 69 |
| abstract_inverted_index.while | 122 |
| abstract_inverted_index.\n\nTo | 84 |
| abstract_inverted_index.brains | 10 |
| abstract_inverted_index.define | 108 |
| abstract_inverted_index.embeds | 115 |
| abstract_inverted_index.enable | 143 |
| abstract_inverted_index.impute | 135 |
| abstract_inverted_index.latent | 136 |
| abstract_inverted_index.layer, | 132 |
| abstract_inverted_index.nature | 58 |
| abstract_inverted_index.neural | 110 |
| abstract_inverted_index.recent | 197 |
| abstract_inverted_index.single | 148 |
| abstract_inverted_index.visual | 211 |
| abstract_inverted_index.Disease | 192 |
| abstract_inverted_index.applied | 167 |
| abstract_inverted_index.biology | 48 |
| abstract_inverted_index.cohort, | 183, 194 |
| abstract_inverted_index.complex | 24 |
| abstract_inverted_index.cortex. | 212 |
| abstract_inverted_index.enables | 33 |
| abstract_inverted_index.improve | 96 |
| abstract_inverted_index.machine | 51, 61 |
| abstract_inverted_index.methods | 220 |
| abstract_inverted_index.models. | 53 |
| abstract_inverted_index.network | 111 |
| abstract_inverted_index.remains | 19 |
| abstract_inverted_index.results | 74 |
| abstract_inverted_index.scales. | 39 |
| abstract_inverted_index.systems | 7 |
| abstract_inverted_index.DeepGAMI | 102, 168, 216, 237 |
| abstract_inverted_index.Emerging | 30 |
| abstract_inverted_index.Finally, | 151 |
| abstract_inverted_index.However, | 14, 40 |
| abstract_inverted_index.Notably, | 113 |
| abstract_inverted_index.approach | 157 |
| abstract_inverted_index.cellular | 28, 231 |
| abstract_inverted_index.existing | 218 |
| abstract_inverted_index.features | 137, 161 |
| abstract_inverted_index.genotype | 175, 185, 234 |
| abstract_inverted_index.gradient | 156 |
| abstract_inverted_index.learning | 52, 62, 93 |
| abstract_inverted_index.missing. | 83 |
| abstract_inverted_index.modality | 81, 149 |
| abstract_inverted_index.neuronal | 206 |
| abstract_inverted_index.profound | 224 |
| abstract_inverted_index.provides | 222 |
| abstract_inverted_index.studying | 34 |
| abstract_inverted_index.training | 123 |
| abstract_inverted_index.DeepGAMI, | 89 |
| abstract_inverted_index.available | 244 |
| abstract_inverted_index.black-box | 57 |
| abstract_inverted_index.developed | 88 |
| abstract_inverted_index.different | 38 |
| abstract_inverted_index.diseases. | 13 |
| abstract_inverted_index.genotypes | 18 |
| abstract_inverted_index.insights, | 77 |
| abstract_inverted_index.integrate | 68 |
| abstract_inverted_index.interpret | 72 |
| abstract_inverted_index.knowledge | 106 |
| abstract_inverted_index.molecular | 26 |
| abstract_inverted_index.primarily | 21 |
| abstract_inverted_index.additional | 139 |
| abstract_inverted_index.approaches | 44 |
| abstract_inverted_index.biological | 6, 76, 105 |
| abstract_inverted_index.comprising | 201 |
| abstract_inverted_index.especially | 78 |
| abstract_inverted_index.expression | 178, 188 |
| abstract_inverted_index.imputation | 121 |
| abstract_inverted_index.integrated | 155 |
| abstract_inverted_index.mechanisms | 36, 229 |
| abstract_inverted_index.modalities | 140 |
| abstract_inverted_index.phenotype. | 236 |
| abstract_inverted_index.phenotypes | 16, 145 |
| abstract_inverted_index.predicting | 15, 144 |
| abstract_inverted_index.prediction | 98 |
| abstract_inverted_index.prioritize | 159 |
| abstract_inverted_index.regulatory | 228 |
| abstract_inverted_index.resolution | 232 |
| abstract_inverted_index.underlying | 25 |
| abstract_inverted_index.Alzheimer's | 191 |
| abstract_inverted_index.association | 1 |
| abstract_inverted_index.challenging | 66 |
| abstract_inverted_index.cross-modal | 120 |
| abstract_inverted_index.incorporate | 47 |
| abstract_inverted_index.mechanisms. | 29 |
| abstract_inverted_index.multi-modal | 31, 100, 127, 160, 199 |
| abstract_inverted_index.open-source | 240 |
| abstract_inverted_index.outperforms | 217 |
| abstract_inverted_index.phenotypes. | 165 |
| abstract_inverted_index.pre-trained | 131 |
| abstract_inverted_index.single-cell | 198 |
| abstract_inverted_index.techniques, | 63 |
| abstract_inverted_index.challenging, | 20 |
| abstract_inverted_index.Schizophrenia | 181 |
| abstract_inverted_index.architecture. | 112 |
| abstract_inverted_index.interpretable | 91 |
| abstract_inverted_index.understanding | 225 |
| abstract_inverted_index.transcriptomics | 202 |
| abstract_inverted_index.multi-modalities | 70 |
| abstract_inverted_index.population-level | 171 |
| abstract_inverted_index.state-of-the-art | 219 |
| abstract_inverted_index.electrophysiology | 204 |
| abstract_inverted_index.Genotype-phenotype | 0 |
| abstract_inverted_index.auxiliary-learning | 117 |
| abstract_inverted_index.cell-type-specific | 174 |
| abstract_inverted_index.genotype-phenotype | 97 |
| abstract_inverted_index.https://github.com/daifengwanglab/DeepGAMI. | 246 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.21834114 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |