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New COAST framework enhances gene expression prediction in spatial transcriptomics

By PulseAugur Editorial · [2 sources] ·

Researchers have developed COAST, a novel framework for predicting gene expression in spatial transcriptomics using histology images. This context-aware differential learning approach leverages both local and global context features, incorporating a Transformer encoder to capture intricate patterns. COAST is trained using a combined objective of absolute expression regression and signed differential regression between target and context spots, showing improved performance on multiple datasets. AI

IMPACT This framework could improve the accuracy and efficiency of gene expression analysis in biological research.

RANK_REASON The cluster contains an academic paper detailing a new machine learning framework for a specific scientific domain.

Read on arXiv cs.LG →

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

New COAST framework enhances gene expression prediction in spatial transcriptomics

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Keunho Byeon, Sunhong Park, Jeewoo Lim, Jin Tae Kwak ·

    COAST: Context-Aware Differential Learning for Gene Expression Prediction in Spatial Transcriptomics

    arXiv:2607.09166v1 Announce Type: new Abstract: Spatial transcriptomics enables profiling of spatial gene expression but is limited by high cost and low throughput, motivating prediction from H&amp;E histopathology images. Existing context-aware methods mainly supervise absolute …

  2. arXiv cs.LG TIER_1 English(EN) · Jin Tae Kwak ·

    COAST: Context-Aware Differential Learning for Gene Expression Prediction in Spatial Transcriptomics

    Spatial transcriptomics enables profiling of spatial gene expression but is limited by high cost and low throughput, motivating prediction from H&E histopathology images. Existing context-aware methods mainly supervise absolute expression, while relative expression relationships …

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