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New AI methods reconstruct 3D tissue gene expression from sparse data · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two research papers propose novel methods for reconstructing 3D spatial transcriptomics data from sparse samples. The first, HIM-INR, uses histology-informed implicit neural representations to jointly reconstruct gene expression and histological images, improving accuracy as missing data increases. The second, HoloTea, employs a 3D-guided flow-matching framework that leverages adjacent tissue sections' gene expression and histology to infer missing data, scaling effectively to large datasets and improving 3D expression accuracy. AI

IMPACT These methods could accelerate biological discovery by enabling more comprehensive 3D tissue analysis from limited experimental data.

RANK_REASON Two academic papers published on arXiv presenting new computational methods for biological data analysis.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New AI methods reconstruct 3D tissue gene expression from sparse data · 2 sources tracked

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Two academic papers published on arXiv presenting new computational methods for biological data analysis.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Kaito Shiku, Ryoma Bise, Ahtisham Fazeel Abbasi, Yuichiro Iwashita, Shumpei Takezaki, Andreas Dengel, Muhammad Nabeel Asim ·

    Continuous 3D Spatial Transcriptomics Reconstruction via Histology-Informed Implicit Neural Representations

    arXiv:2611.14129v1 Announce Type: cross Abstract: Three-dimensional spatial transcriptomics (3D ST) enables the analysis of gene-expression patterns across tissue depths. However, 3D ST measurements are acquired from discrete 2D tissue sections, which are often sparsely sampled d…

  2. arXiv cs.CV TIER_1 English(EN) · Mohammad Vali Sanian, Arshia Hemmat, Amirhossein Vahidi, Jonas Maaskola, Jimmy Tsz Hang Lee, Martin Prete, Stanislaw Makarchuk, Yeliz Demirci, Nana-Jane Chipampe, Muzlifah Haniffa, Omer Bayraktar, Lassi Paavolainen, Mohammad Lotfollahi ·

    3D-Guided Scalable Flow Matching for Generating Volumetric Tissue Spatial Transcriptomics from Serial Histology

    arXiv:2511.14613v3 Announce Type: replace Abstract: A 3D transcriptomic profile of a tissue can give a holistic view of its organization in health and disease, but spatial transcriptomics (ST) is costly, so usually only some serial sections are profiled, while H&amp;E is availabl…

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