Articles

UMedPT, a foundational model for biomedical imaging, has been trained on a variety of medical tasks with different types of label. It has achieved high performance with less training data in various clinical applications.

In this study, the authors present a virtual node graph neural network to enable the prediction of material properties with variable output dimensions. This method offers fast and accurate predictions of phonon band structures in complex solids.

TCRen predicts TCR specificity by modeling the TCR–peptide–MHC structure and estimating the TCR–peptide interaction energy using a statistical potential. The use of structural information allows TCRen to generalize to unseen epitopes, such as cancer neoepitopes.

Here a conformality-assisted tracing method is proposed to devise free-form and three-dimensional conformal metamaterials, featuring accuracy and efficiency in handling complex geometry and adaptability to various diffusion and wave fields.

The downselection of compounds for synthesis is a key challenge in molecular design cycles that typically relies on expert chemist intuition. Fromer and Coley propose a cost-aware method to automatically select compounds and synthetic routes.

Nonlinear optical computations have been essential yet challenging for developing optical neural networks with appreciable expressivity. In this paper, light scattering is combined with optical nonlinearity to empower a high-performance, large-scale nonlinear photonic neural system.

A self-consistent iterative procedure is proposed to compute the committor function for rare events, via a variational principle, and extensively sample the transition state ensemble, allowing for the identification of the relevant variables in the process.

This paper introduces an algorithm to uncover laws of skill acquisition from naturally occurring data. By combining deep learning and symbolic regression, it accurately identifies cognitive states and extracts algebraic equations.

The authors develop the tool RESHAPE to share reference panels in a safer way. The genome–phenome links in reference panels can generate re-identification threats and RESHAPE breaks these links by shuffling haplotypes while preserving imputation accuracy.

A method based on a vector-quantized variational autoencoder, called CASTLE, can interpretably extract discrete latent embeddings and quantitatively generate the cell-type-specific feature spectrum for single-cell chromatin accessibility sequencing data.

Cooperation is not merely a dyadic phenomenon, it also includes multi-way social interactions. A mathematical framework is developed to study how the structure of higher-order interactions influences cooperative behavior.

This study introduces SANGO, a method for accurate single-cell annotation leveraging genomic sequences around accessibility peaks within single-cell ATAC sequencing data. SANGO consistently outperforms existing methods across diverse datasets for identification of cell type and detection of unknown tumor cells. SANGO enables the discovery of cell-type-specific functional insights through expression enrichment, cis-regulatory chromatin interactions and motif enrichment analyses.

A method is developed for the directional optimization of multiple properties without prior knowledge on their nature. Using a large ligand dataset, diverse metal complexes are found along the Pareto front of vast chemical spaces.

Andre Berndt and colleagues introduce a machine learning approach to enhance the biophysical characteristics of genetically encoded fluorescent indicators, deriving and testing in vitro new GCaMP mutations that surpass the performance of existing fast GCaMP indicators.

M-OFDFT is a deep learning implementation of orbital-free density functional theory (OFDFT) that achieves DFT-level accuracy on molecular systems with lower cost complexity, and can extrapolate to much larger molecules than those seen during training.

An optimization algorithm is used to discover guest molecules based on knowing only the structure of the host. The molecules are represented as 3D volumes, optimized to improve host–guest interaction and converted into SMILES using a transformer model.

SCORPION is an algorithm to model gene regulatory networks based on single-cell data. The authors show that SCORPION outperforms other methods, accurately detects transcription factor activity and can potentially help with the discovery of disease markers.

Automated algorithm discovery has been difficult for artificial intelligence given the immense search space of possible functions. Here explainable neural networks are used to discover algorithms that outperform those designed by humans.

DNA microscopy reconstructs the spatial organization of a sample from a neighborhood graph. In this work, MinIPath efficiently corrects errors from these graphs that distort the reconstruction, both in simulated and experimental data.

The authors develop a general method that combines machine learning and physics to construct macroscopic dynamics directly from microscopic observations, leading to an intuitive understanding of polymer stretching in elongational flow.