graph neural networks

Personalized single-cell transcriptomics reveals molecular diversity in Alzheimer’s disease

A personalized functional genomics atlas for Alzheimer’s disease that uses knowledge-guided graph neural networks to analyze donor-level functional genomics, identify disease subpopulations and trajectories, and link genetic variants to gene regulation is summarized.

AI-based characterization of Alzheimer's disease phenotypes from population-scale single-cell data.

The complexity of Alzheimer's disease (AD) manifests in diverse clinical phenotypes, including cognitive impairment and neuropsychiatric symptoms. However, the etiology of these phenotypes remains elusive. To address this, the PsychAD project generated a population-level single-nucleus RNA sequencing dataset comprising...

Transferable FB-GNN-MBE framework for potential energy surfaces: Data-adaptive transfer learning in deep learned many-body expansion theory

Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks. However, if the system exceeds hundreds of atoms, first-principles quantum mechanical (QM) modeling becomes impractical. In this study, we develop...

Lorentz-equivariance without limitations

Lorentz Local Canonicalization (LLoCa) ensures exact Lorentz-equivariance for arbitrary neural networks with minimal computational overhead. For the LHC, it equivariantly predicts local reference frames for each particle and propagates any-order tensorial information between them. We apply it to graph networks and tran...

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (slides) - Slides

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (video)

Abstract B002: Computational Discovery of Evolutionary Synthetic Lethality Networks for Personalized Gene Therapy and Small-Molecule Design in Treatment-Resistant Pediatric High-Grade Glioma

An integrative computational framework is developed to reconstruct resistance evolution, identify state-specific therapeutic vulnerabilities, and design personalized gene therapy and blood-brain barrier (BBB)-penetrant small-molecule therapeutics targeting treatment-resistant pediatric glioma.

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (slides) - Slides

Abstract B004: Targeting Transient Cell-State Dependencies in Glioblastoma Through Single-Cell Evolutionary Modeling and Structure-Based Drug Design

A computational framework is presented that reconstructs the evolutionary trajectory of glioblastoma cells under treatment pressure and uses that trajectory to identify druggable targets that exist only transiently, during state transition, to isolate signaling dependencies that appear specifically during the resistanc...

Structured PREreview of "Training, learning and inference: unified dynamics of neural systems"

This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22899514. Does the introduction explain the objective of the research presented in the preprint? Yes The introduction clearly establishes the motivation, core research ob...

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (slides) - Slides

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (slides) - Slides