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Found 15 papers, 7 papers with code
Targeted AMP generation through controlled diffusion with efficient embeddings
Deep learning-based antimicrobial peptide (AMP) discovery faces critical challenges such as low experimental hit rates as well as the need for nuanced controllability and efficient modeling of peptide properties.
Unified Guidance for Geometry-Conditioned Molecular Generation
Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative models and, in particular, diffusion models.
Centaur: a foundation model of human cognition
Establishing a unified theory of cognition has been a major goal of psychology.
How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities
Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease.
Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen
Generative modeling of single-cell RNA-seq data is crucial for tasks like trajectory inference, batch effect removal, and simulation of realistic cellular data.
Disentangled Representation Learning with the Gromov-Monge Gap
However, matching the prior while preserving geometric features is challenging, as a mapping that fully preserves these features while aligning the data distribution with the prior does not exist in general.
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
We hence establish UOT-FM as a principled method for unpaired image translation.
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics
These challenges have spurred the development of neural network-based solvers, known as neural OT solvers, that parameterize OT maps.
MAGNet: Motif-Agnostic Generation of Molecules from Shapes
Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions.
The power of motifs as inductive bias for learning molecular distributions
Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules.
Uncertainty Quantification for Atlas-Level Cell Type Transfer
Here, for the first time, we introduce uncertainty quantification methods for cell type classification on single-cell reference atlases.
Sparsity in Continuous-Depth Neural Networks
Neural Ordinary Differential Equations (NODEs) have proven successful in learning dynamical systems in terms of accurately recovering the observed trajectories.
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation
One of the most common methods for analyzing CT and MRI images for diagnosis and follow-up treatment is liver segmentation.
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells.
Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
A statistical analysis of these measurements requires annotation of mitosis events, which is currently a tedious and time-consuming task that has to be performed manually.
