$\textit{BlockFormer}$ : Transformer-based inference from interaction maps
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918524549595136 |
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| author | Touron, Eloïse Rodrigues, Pedro L. C. Arbel, Julyan Varoquaux, Nelle Arbel, Michael |
| author_facet | Touron, Eloïse Rodrigues, Pedro L. C. Arbel, Julyan Varoquaux, Nelle Arbel, Michael |
| contents | Inference from interaction maps, such as centromere identification from genome-wide chromosome conformation capture techniques -- notably Hi-C -- can be formulated as a generic inverse problem: infer a set of parameters given a map summarizing pairwise interactions between entities through blocks of variable numbers and sizes. In this work, we introduce a data-driven approach that leverages shared structure between these maps, such as global alignment between localized patterns, while handling the variability in number and size of entities arising in real-world data. Our approach relies on a transformer architecture capable of handling such variability and a custom simulator to generate abundant, yet computationally cheap synthetic data for training. Applied to the problem of centromere localization, the method accurately recovers their genomic positions across a wide range of species of various genome sizes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21617 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | $\textit{BlockFormer}$ : Transformer-based inference from interaction maps Touron, Eloïse Rodrigues, Pedro L. C. Arbel, Julyan Varoquaux, Nelle Arbel, Michael Machine Learning Quantitative Methods Inference from interaction maps, such as centromere identification from genome-wide chromosome conformation capture techniques -- notably Hi-C -- can be formulated as a generic inverse problem: infer a set of parameters given a map summarizing pairwise interactions between entities through blocks of variable numbers and sizes. In this work, we introduce a data-driven approach that leverages shared structure between these maps, such as global alignment between localized patterns, while handling the variability in number and size of entities arising in real-world data. Our approach relies on a transformer architecture capable of handling such variability and a custom simulator to generate abundant, yet computationally cheap synthetic data for training. Applied to the problem of centromere localization, the method accurately recovers their genomic positions across a wide range of species of various genome sizes. |
| title | $\textit{BlockFormer}$ : Transformer-based inference from interaction maps |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2605.21617 |