Abstract Therapeutics Data Commons (tdcommons. ai) is an open science initiative with
unified datasets, AI models, and benchmarks to support research across therapeutic …

In silico modeling of transcriptional responses to perturbations is crucial for advancing our
understanding of cellular processes and disease mechanisms. We present PertEval-scFM, a …

The rapid advancement of high-throughput sequencing and other assay technologies has
resulted in the generation of large and complex multi-omics datasets, offering …

Despite substantial efforts, deep learning has not yet delivered a transformative impact on
elucidating regulatory biology, particularly in the realm of predicting gene expression …

Due to the abundance of single cell RNA-seq data, a number of methods for predicting
expression after perturbation have recently been published. Expression prediction methods …

Sequence models generate counterfactuals by modifying parts of a sequence based on a
given condition, enabling reasoning about" what if" scenarios. While these models excel at …

Accurately predicting cellular responses to perturbations is essential for understanding cell
behaviour in both healthy and diseased states. While perturbation data is ideal for building …

In cell line perturbation experiments, a collection of cells is perturbed with external agents
and responses such as protein expression measured. Due to cost constraints, only a small …

The advancement of novel combinatorial CRISPR screening technologies enables the
identification of synergistic gene combinations on a large scale. This is crucial for …

Rationally identifying variables responsible for changes to a biological system can enable
myriad applications in disease understanding and cell engineering. From a causality …