Spatial Transcriptomics is a novel technology that aligns his-
tology images with spatially resolved gene expression profiles. Although
groundbreaking, it struggles with gene capture yielding high corruption
in acquired data. Given potential applications, recent efforts have fo-
cused on predicting transcriptomic profiles solely from histology images.
However, differences in databases, preprocessing techniques, and train-
ing hyperparameters hinder a fair comparison between methods. To ad-
dress these challenges, we present a systematically curated and processed
database collected from 26 public sources, representing an 8.6-fold in-
crease compared to previous works. Additionally, we propose a state-
of-the-art transformer-based completion technique for inferring missing
gene expression, which significantly boosts the performance of transcrip-
tomic profile predictions across all datasets. Altogether, our contribu-
tions constitute the most comprehensive benchmark of gene expression
prediction from histology images to date and a stepping stone for future
research on spatial transcriptomics.