Electroencephalogram-based brain-computer interfaces (BCIs) hold promise for healthcare applications but are hindered by cross-subject variability and limited data. This article proposes a multi-task ...

Despite advances in sequencing technologies, genome-scale datasets often contain missing bases and genomic segments, hindering downstream analyses. Genotype imputation addresses this issue and has ...

The RNA medicine revolution has been spurred by lipid nanoparticles (LNPs). The effectiveness of an LNP is determined by its lipid components and their ratios; however, experimental optimization is ...

Hyperspectral unmixing methods are essential to exploit the capabilities of Raman spectroscopy for nondestructive, unbiased chemical characterization in a wide array of domains, from biology, ...

Machine learning has the potential to provide tremendous value to life sciences by providing models that aid in the discovery of new molecules and reduce the time for new products to come to market.

Humans make tens of thousands of decisions every day. Project Azua aims to develop machine learning solutions for efficient decision making that demonstrate human expert-level performance across all ...

This paper presents In-Context Operator Networks (ICON), a neural network approach that can learn new operators from prompted data during the inference stage without requiring any weight updates.

The latest version includes an experimental GPT explainer. This explainer leverages the outcomes produced by SHAP and MACE to formulate the input prompt for ChatGPT. Subsequently, ChatGPT analyzes ...

Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear ...