Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas
JE Rood, A Hupalowska, A Regev - Cell, 2024 - cell.com
Comprehensively charting the biologically causal circuits that govern the phenotypic space
of human cells has often been viewed as an insurmountable challenge. However, in the last …
of human cells has often been viewed as an insurmountable challenge. However, in the last …
[HTML][HTML] How to build the virtual cell with artificial intelligence: Priorities and opportunities
Cells are essential to understanding health and disease, yet traditional models fall short of
modeling and simulating their function and behavior. Advances in AI and omics offer …
modeling and simulating their function and behavior. Advances in AI and omics offer …
Using machine learning to enhance and accelerate synthetic biology
Engineering synthetic regulatory circuits with precise input-output behavior—a central goal
in synthetic biology—remains encumbered by the inherent molecular complexity of cells …
in synthetic biology—remains encumbered by the inherent molecular complexity of cells …
TDC-2: Multimodal foundation for therapeutic science
Abstract Therapeutics Data Commons (tdcommons. ai) is an open science initiative with
unified datasets, AI models, and benchmarks to support research across therapeutic …
unified datasets, AI models, and benchmarks to support research across therapeutic …
scelmo: Embeddings from language models are good learners for single-cell data analysis
Abstract Various Foundation Models (FMs) have been built based on the pre-training and
fine-tuning framework to analyze single-cell data with different degrees of success. In this …
fine-tuning framework to analyze single-cell data with different degrees of success. In this …
A genome-wide atlas of human cell morphology
A key challenge of the modern genomics era is develo** empirical data-driven
representations of gene function. Here we present the first unbiased morphology-based …
representations of gene function. Here we present the first unbiased morphology-based …
[HTML][HTML] Active learning of enhancers and silencers in the develo** neural retina
Deep learning is a promising strategy for modeling cis-regulatory elements. However,
models trained on genomic sequences often fail to explain why the same transcription factor …
models trained on genomic sequences often fail to explain why the same transcription factor …
Learning to refine domain knowledge for biological network inference
Perturbation experiments allow biologists to discover causal relationships between
variables of interest, but the sparsity and high dimensionality of these data pose significant …
variables of interest, but the sparsity and high dimensionality of these data pose significant …
Simplifying bioinformatics data analysis through conversation
The burgeoning field of bioinformatics has been revolutionized by the rapid growth of omics
data, providing insights into various biological processes. However, the complexity of …
data, providing insights into various biological processes. However, the complexity of …
Predicting perturbation targets with causal differential networks
M Wu, U Padia, SH Murphy, R Barzilay… - arxiv preprint arxiv …, 2024 - arxiv.org
Rationally identifying variables responsible for changes to a biological system can enable
myriad applications in disease understanding and cell engineering. From a causality …
myriad applications in disease understanding and cell engineering. From a causality …