Stem Cell Research

CellProphet: Dissecting Virtual Cell Differentiation through AI-Powered Dynamic Gene Regulatory Network Inference

The AI Virtual Cell (AIVC) framework promises to revolutionize biological research through high-fidelity simulations of cellular behaviors and responses to perturbations. Central to realizing this vision is the ability to model cell differentiation dynamics, which requires accurate inference of gene regulatory network (GRN) that govern cell fate decisions. However, existing computational approache...

Efficacy and safety evaluation of artificial intelligence-identified antimicrobial peptides for use against avian pathogenic Escherichia coli in the poultry industry

The overuse of antibiotics in both veterinary and human medicine has resulted in the emergence of antibiotic-resistant bacteria, prompting a search for effective alternatives. Antimicrobial peptides (AMP) are short, often cationic, peptide-based molecules with antimicrobial and immunomodulatory activity, which makes them promising alternatives to conventional antibiotics in poultry production. Fro...

Stable Maintenance of Two-Cell-Like Cells from Embryonic Stem Cells Reveals Chromatin and Super Enhancer Regulation of MERVL Elements

Mouse embryonic stem cells (ESCs) occasionally transit into a rare two-cell-like (2C) state characterized by transient activation of endogenous retrov...

FUSED: Cross-Domain Integration of Foundation Models for Cancer Drug Response Prediction

AI-driven methods for predicting drug responses hold promise for advancing personalized cancer therapy, but cancer heterogeneity and the high cost of ...

Single-cell lineage trajectory defines CDK inhibitor-sensitive cells-of-origin in esophageal squamous cell cancer

Understanding the cells of origin is essential for overcoming therapy resistance in esophageal squamous cell carcinoma (ESCC). We utilized machine lea...

Deep multiplexed 50-marker imaging of circulating tumor cells expands actionable biomarker profiling for precision oncology

Liquid biopsy-derived circulating tumor cells (CTCs) offer a minimally invasive avenue for precision oncology by enabling longitudinal monitoring of a...

Reinforcement Failing guides the discovery of emergent physical dynamics in adaptive tumor therapy

Artificial intelligence is revolutionizing scientific discovery in medicine, with reinforcement learning (RL) emerging as a promising tool for optimiz...

A Machine Learning Model Optimized for Local Data Stratifies Patients for the Adoptive Cell Therapy with Tumor Infiltrating Lymphocytes in Bladder Tumors

Adoptive cell therapy with tumor-infiltrating lymphocytes (ACT-TILs) involves autologous TILs that are expanded ex vivo and then reinfused into the pa...

Organelle-Aware Representation Learning Enables Label-Free Detection of Mitochondrial Dysfunction in Live Human Neurons

Mitochondrial dysfunction is a convergent hallmark of neurodegenerative diseases and represents a promising biomarker for early diagnosis and therapy....

Base-editing a single missense mutation in A20 enhances CAR-T cell efficacy

T cell exhaustion limits the efficacy of cancer immunotherapies. Here, we performed genome-wide loss-of-function screening in repetitively stimulated ...

Iron Deficiency Impairs Mitochondrial Energetics and Early Axonal Growth and Branching in Developing Hippocampal Neurons

Each stage of neuronal development (i.e., proliferation, differentiation, migration, neurite outgrowth and synapse formation) requires functional and ...

Large-scale synthetic data enable digital twins of human excitable cells

Individual variability shapes how diseases manifest, how patients respond to therapy, and how rare phenotypes arise. Conventional experimental approac...

Using artificial intelligence to optimize ecological restoration for climate and biodiversity

The restoration of degraded ecosystems is critical for mitigating climate change and reversing biodiversity loss. Depending on the primary objective –...

Cell type-independent timekeeping gene modules enable embryonic stage prediction in zebrafish

Gene expression changes across embryonic development reflect both differentiation and genes whose expression varies strictly with developmental time, ...

Inferring virtual cell environments using multi-agent reinforcement learning

Single cells interact continuously to form a cell environment that drives key biological processes. Cells and cell environments are highly dynamic acr...

Viral protease-Initiated Pyroptosis Activator mRNA therapy as a Universal Antiviral Strategy

Although therapeutic drugs targeting gasdermin (GSDM)-mediated pyroptosis have made remarkable progress in treating various diseases, their potential ...

A memory-driven reinforcement learning model of phenotypic adaptation for anticipating therapeutic resistance in prostate cancer

While contemporary cancer treatment strategies have significantly prolonged the lives of patients, therapeutic resistance remains a predominant cause ...

Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage

Cells must adopt flexible regulatory strategies to make decisions regarding their fate, including differentiation, apoptosis, or survival in the face ...

CochleaNet: deep learning-based image analysis for cochlear connectomics and gene therapy

With the emergence of gene and optogenetic therapies targeting deafness, the comprehensive analysis of the molecular anatomy and physiology of the coc...

Reinforcement learning enables single-cell foundation models to learn cellular differentiation

While single-cell foundation models excel at static representation learning and single-step perturbation prediction, their capacity to model and contr...