Kazuhiro Takemoto

In single-cell foundation models, predicting the effect of overexpressing genes had been limited to one gene at a time or to enumerating a few hundred combinations. A Kyushu Institute of Technology team recast choosing which genes to overexpress as a discrete adversarial attack on gene tokens and searched combinations with Greedy Coordinate Gradient (GCG), a search built for language models. In Geneformer and scGPT, GCG raised similarity to the target cell state above gradient-pool greedy at equal forward-pass cost, with the margin growing with set size; measured against published reprogramming factors, however, ranking by single-gene effect recovered more of them.

In single-cell foundation models, predicting the effect of overexpressing genes had been limited to one gene at a time or to enumerating a few hundred combinations. A Kyushu Institute of Technology team recast choosing which genes to overexpress as a discrete adversarial attack on gene tokens and searched combinations with Greedy Coordinate Gradient (GCG), a search built for language models. In Geneformer and scGPT, GCG raised similarity to the target cell state above gradient-pool greedy at equal forward-pass cost, with the margin growing with set size; measured against published reprogramming factors, however, ranking by single-gene effect recovered more of them.