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Stem Cell Research

Latest AI and machine learning research in stem cell research for healthcare professionals.

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Adaptive Experiments Under High-Dimensional and Data Sparse Settings: Applications for Educational Platforms

In online educational platforms, adaptive experiment designs play a critical role in personalizing learning pathways, instructional sequencing, and content recommendations. Traditional adaptive policies, such as Thompson Sampling, struggle with scalability in high-dimensional and sparse settings such as when there are large amount of treatments (arms) and limited resources such as funding and ti...

Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast Cancer Patients

This study explores how causal inference models, specifically the Linear Non-Gaussian Acyclic Model (LiNGAM), can extract causal relationships between demographic factors, treatments, conditions, and outcomes from observational patient data, enabling insights beyond correlation. Unlike traditional randomized controlled trials (RCTs), which establish causal relationships within narrowly defined p...

Boundary constraints can determine pattern emergence

The robust patterning of cell fates during embryonic development requires precise coordination of signalling gradients within defined spatial constrai...

Neural xenografts contribute to long-term recovery in stroke via molecular graft-host crosstalk

Stroke is a leading cause of disability and death due to the brain’s limited ability to regenerate damaged neural circuits. To date, stroke patients h...

Transformer-based Deep Learning for Glycan Structure Inference from Tandem Mass Spectrometry

Glycans play critical roles in diverse biological processes, but their structural analysis by tandem mass spectrometry (MS/MS) remains a major challen...

Machine Learning–Guided Differentiation Therapy Targets Cancer Stem Cells in Colorectal Cancers

Despite advances in artificial intelligence (AI) within cancer research, its application toward realizing differentiation therapy in solid tumors rema...

OmniPert: A Deep Learning Foundation Model for Predicting Responses to Genetic and Chemical Perturbations in Single Cancer Cells

In cancer, intra- and inter-patient heterogeneity presents a significant challenge for therapeutic management, as patients with apparently similar pro...

Machine-Learning-Assisted Exploration of High Entropy-Atom Nanozyme for Anti-Tumor Immunotherapy by Enhancing Enzyme Activity and Disrupting Dual Energy Metabolism

Despite its potential in cancer therapy, single-atom nanozyme (SAzyme) faces challenges like low atomic loading and rapid cancer metabolism. Here, a h...

Computational Tracking of Cell Origins Using CellSexID from Single-Cell Transcriptomes

Cell tracking in chimeric models is essential yet challenging, particularly in developmental biology, regenerative medicine, and transplantation resea...

Mechanistically Informed Machine Learning Links Non-Canonical TCA Cycle Activity to Warburg Metabolism and Hallmarks of Malignancy

Cancer cells undergo extensive metabolic rewiring to support growth, survival, and phenotypic plasticity. A non-canonical variant of the tricarboxylic...

Colorectal cancer heterogeneity co-evolves with tumor architecture to determine disease outcome

Intratumoral heterogeneity, originating from genetic, epigenetic, and phenotypic cellular diversity, is pervasive in cancer. As these heterogeneous st...

CART-GPT: A T Cell-Informed AI Linguistic Framework for Interpreting Neurotoxicity and Therapeutic Outcomes in CAR-T Therapy

Chimeric antigen receptor (CAR) T cell therapy holds transformative potential for hematologic malignancies, yet predicting patient-specific treatment ...

Deep Learning for Predicting Stem Cell Efficiency for use in Beta Cell Differentiation

Recent clinical trial data have shown that cell therapy holds curative potential for type-1 diabetes, however the large amounts of lab-grown cells req...

Non-segmented unsupervised learning of multispectral whole slide images for robust analysis of tissue repair and regeneration

Analyzing whole tissue architecture remains challenging due to the inherent complexity of multicellular organization, variable morphology, and the lim...

Perceptual predictions track subjective, over objective, statistical structure

Predictive processes in cognition and the brain are usually modelled as tracking objective event probabilities. However, we know little about how our ...

Single-cell transcriptomics and machine learning reveal RNF144B and C5AR1 as immune-related biomarkers and therapeutic targets in myocardial infarction

Myocardial infarction (MI) is a life-threatening cardiovascular disease characterized by high morbidity and mortality. Although advances in clinical m...

Single-cell neural network classifiers reveal that PM21 NK cell expansion is dependent on B cell signaling

In the field drug development ML/AI methods are being applied to improve drug production speed, costs, and reliability. In allogenic NK cell therapy p...

α-SMA/VCAM-1 Dual-Targeted Nanoplatform Improves Drug Release and Therapeutic Efficacy in Liver Fibrosis

Liver fibrosis is a progressive pathological condition characterized by hepatic stellate cell (HSC) activation and vascular endothelial dysfunction, w...

Advanced Deep Learning Enables Prediction of Allogeneic Stem Cell Mobilization Success

Hematopoietic stem and progenitor cell (HSPC) transplantation offers a potentially curative therapy for aggressive hematologic malignancies and bone m...

STARNet enables spatially resolved inference of gene regulatory networks from spatial multi-omics data

Biological tissues are composed of distinct microenvironments that spatially orchestrate gene expression and cell identity. However, the regulatory pr...

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