AIM: an accurate and explainable model for ATAC to GEX translation and pathway analysis Article Swipe
Quang H. Nguyen
,
Hoang V. Tran
,
Huu Tien Nguyen
,
Phuong Thi Le
,
Phi Le Nguyen
,
Binh P. Nguyen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s12293-025-00442-w
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s12293-025-00442-w
The development of multimodal technologies has enabled the simultaneous measurement of various cellular modalities, such as chromatin accessibility (ATAC), gene expression (GEX), and surface protein abundance in single cells. However, the lack of multimodal datasets requires the development of robust algorithms that can translate data between different modalities. In this study, we present AIM, a framework for accurate and interpretive multimodal translation, specifically designed for the conversion of ATAC data into GEX profiles. AIM introduces a novel two-tier modeling architecture. The upper tier captures the global relationships between ATAC and GEX, generating an initial estimate of gene expression. The lower tier performs a finer-grained analysis by modeling inter-chromosomal interactions to refine the generated GEX representation. This modular structure enhances both the accuracy and adaptability of AIM. Additionally, an integrated attention mechanism provides interpretability by highlighting critical chromatin regions influencing specific gene expressions. Our experimental results demonstrate that AIM achieves state-of-the-art performance, with a per-chromosome RMSE of 0.2206, outperforming existing approaches (0.2232). Furthermore, the attention maps generated by AIM offer a pathway analysis capability, uncovering biologically significant gene-gene interactions such as ARHGAP24-ARAP2 and SYK-PAX5. These findings validate AIM’s effectiveness not only as a data translation tool but also as a platform for deriving mechanistic insights into gene regulatory dynamics.
Related Topics
Concepts
Complex system
Translation (biology)
Computer science
Artificial intelligence
Chemistry
Biochemistry
Messenger RNA
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s12293-025-00442-w
- https://link.springer.com/content/pdf/10.1007/s12293-025-00442-w.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 35
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- 10
- OpenAlex ID
- https://openalex.org/W4408737279
All OpenAlex metadata
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https://doi.org/10.1007/s12293-025-00442-wDigital Object Identifier
- Title
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AIM: an accurate and explainable model for ATAC to GEX translation and pathway analysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-21Full publication date if available
- Authors
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Quang H. Nguyen, Hoang V. Tran, Huu Tien Nguyen, Phuong Thi Le, Phi Le Nguyen, Binh P. NguyenList of authors in order
- Landing page
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https://doi.org/10.1007/s12293-025-00442-wPublisher landing page
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https://link.springer.com/content/pdf/10.1007/s12293-025-00442-w.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s12293-025-00442-w.pdfDirect OA link when available
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Complex system, Translation (biology), Computer science, Artificial intelligence, Chemistry, Biochemistry, Messenger RNA, GeneTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.by | 106, 134, 167 |
| abstract_inverted_index.in | 27 |
| abstract_inverted_index.of | 3, 11, 33, 39, 68, 96, 125, 156 |
| abstract_inverted_index.to | 110 |
| abstract_inverted_index.we | 52 |
| abstract_inverted_index.AIM | 74, 148, 168 |
| abstract_inverted_index.GEX | 72, 114 |
| abstract_inverted_index.Our | 143 |
| abstract_inverted_index.The | 1, 81, 99 |
| abstract_inverted_index.and | 23, 59, 90, 123, 182 |
| abstract_inverted_index.but | 196 |
| abstract_inverted_index.can | 43 |
| abstract_inverted_index.for | 57, 65, 201 |
| abstract_inverted_index.has | 6 |
| abstract_inverted_index.not | 189 |
| abstract_inverted_index.the | 8, 31, 37, 66, 85, 112, 121, 163 |
| abstract_inverted_index.AIM, | 54 |
| abstract_inverted_index.AIM. | 126 |
| abstract_inverted_index.ATAC | 69, 89 |
| abstract_inverted_index.GEX, | 91 |
| abstract_inverted_index.RMSE | 155 |
| abstract_inverted_index.This | 116 |
| abstract_inverted_index.also | 197 |
| abstract_inverted_index.both | 120 |
| abstract_inverted_index.data | 45, 70, 193 |
| abstract_inverted_index.gene | 20, 97, 141, 206 |
| abstract_inverted_index.into | 71, 205 |
| abstract_inverted_index.lack | 32 |
| abstract_inverted_index.maps | 165 |
| abstract_inverted_index.only | 190 |
| abstract_inverted_index.such | 15, 179 |
| abstract_inverted_index.that | 42, 147 |
| abstract_inverted_index.this | 50 |
| abstract_inverted_index.tier | 83, 101 |
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| abstract_inverted_index.These | 184 |
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| abstract_inverted_index.novel | 77 |
| abstract_inverted_index.offer | 169 |
| abstract_inverted_index.upper | 82 |
| abstract_inverted_index.(GEX), | 22 |
| abstract_inverted_index.cells. | 29 |
| abstract_inverted_index.global | 86 |
| abstract_inverted_index.refine | 111 |
| abstract_inverted_index.robust | 40 |
| abstract_inverted_index.single | 28 |
| abstract_inverted_index.study, | 51 |
| abstract_inverted_index.(ATAC), | 19 |
| abstract_inverted_index.0.2206, | 157 |
| abstract_inverted_index.AIM’s | 187 |
| abstract_inverted_index.between | 46, 88 |
| abstract_inverted_index.enabled | 7 |
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| abstract_inverted_index.modular | 117 |
| abstract_inverted_index.pathway | 171 |
| abstract_inverted_index.present | 53 |
| abstract_inverted_index.protein | 25 |
| abstract_inverted_index.regions | 138 |
| abstract_inverted_index.results | 145 |
| abstract_inverted_index.surface | 24 |
| abstract_inverted_index.various | 12 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 30 |
| abstract_inverted_index.accuracy | 122 |
| abstract_inverted_index.accurate | 58 |
| abstract_inverted_index.achieves | 149 |
| abstract_inverted_index.analysis | 105, 172 |
| abstract_inverted_index.captures | 84 |
| abstract_inverted_index.cellular | 13 |
| abstract_inverted_index.critical | 136 |
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| abstract_inverted_index.deriving | 202 |
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| abstract_inverted_index.enhances | 119 |
| abstract_inverted_index.estimate | 95 |
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| abstract_inverted_index.findings | 185 |
| abstract_inverted_index.insights | 204 |
| abstract_inverted_index.modeling | 79, 107 |
| abstract_inverted_index.performs | 102 |
| abstract_inverted_index.platform | 200 |
| abstract_inverted_index.provides | 132 |
| abstract_inverted_index.requires | 36 |
| abstract_inverted_index.specific | 140 |
| abstract_inverted_index.two-tier | 78 |
| abstract_inverted_index.validate | 186 |
| abstract_inverted_index.(0.2232). | 161 |
| abstract_inverted_index.SYK-PAX5. | 183 |
| abstract_inverted_index.abundance | 26 |
| abstract_inverted_index.attention | 130, 164 |
| abstract_inverted_index.chromatin | 17, 137 |
| abstract_inverted_index.different | 47 |
| abstract_inverted_index.dynamics. | 208 |
| abstract_inverted_index.framework | 56 |
| abstract_inverted_index.gene-gene | 177 |
| abstract_inverted_index.generated | 113, 166 |
| abstract_inverted_index.mechanism | 131 |
| abstract_inverted_index.profiles. | 73 |
| abstract_inverted_index.structure | 118 |
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| abstract_inverted_index.algorithms | 41 |
| abstract_inverted_index.approaches | 160 |
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| abstract_inverted_index.expression | 21 |
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| abstract_inverted_index.introduces | 75 |
| abstract_inverted_index.multimodal | 4, 34, 61 |
| abstract_inverted_index.regulatory | 207 |
| abstract_inverted_index.uncovering | 174 |
| abstract_inverted_index.capability, | 173 |
| abstract_inverted_index.demonstrate | 146 |
| abstract_inverted_index.development | 2, 38 |
| abstract_inverted_index.expression. | 98 |
| abstract_inverted_index.influencing | 139 |
| abstract_inverted_index.measurement | 10 |
| abstract_inverted_index.mechanistic | 203 |
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| abstract_inverted_index.modalities. | 48 |
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| abstract_inverted_index.translation | 194 |
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| abstract_inverted_index.adaptability | 124 |
| abstract_inverted_index.biologically | 175 |
| abstract_inverted_index.experimental | 144 |
| abstract_inverted_index.expressions. | 142 |
| abstract_inverted_index.highlighting | 135 |
| abstract_inverted_index.interactions | 109, 178 |
| abstract_inverted_index.interpretive | 60 |
| abstract_inverted_index.performance, | 151 |
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| abstract_inverted_index.technologies | 5 |
| abstract_inverted_index.translation, | 62 |
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| abstract_inverted_index.architecture. | 80 |
| abstract_inverted_index.effectiveness | 188 |
| abstract_inverted_index.finer-grained | 104 |
| abstract_inverted_index.outperforming | 158 |
| abstract_inverted_index.relationships | 87 |
| abstract_inverted_index.ARHGAP24-ARAP2 | 181 |
| abstract_inverted_index.per-chromosome | 154 |
| abstract_inverted_index.representation. | 115 |
| abstract_inverted_index.interpretability | 133 |
| abstract_inverted_index.state-of-the-art | 150 |
| abstract_inverted_index.inter-chromosomal | 108 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5091142923 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I41156924 |
| citation_normalized_percentile.value | 0.77870835 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |