Articles in 2024

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.