Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts

A recent study published in Nature — Machine Learning demonstrates that knowledge-guided adaptation of pathology foundation models significantly enhances cross-domain generalization and demographic fairness in medical diagnostics. This advancement is crucial for improving the accuracy of pathology assessments across diverse populations.

A new machine learning model named GerNA-Bind has been developed to decipher RNA-ligand binding specificity, as reported in Nature — Machine Learning. This model aims to enhance the understanding of how RNA interacts with various ligands, which is crucial for advancing research in molecular biology and drug discovery.

A recent study published in Nature — Machine Learning has introduced an optimized MobileNet model designed for lightweight and accurate detection of plant leaf diseases. This advancement leverages machine learning techniques to improve agricultural diagnostics, enabling faster and more efficient identification of plant health issues.

FIDDLE, a deep learning method for predicting chemical formulas from tandem mass spectra, has been introduced in a recent publication by Nature — Machine Learning. This innovative approach aims to enhance the accuracy of chemical formula predictions, which is crucial for various applications in chemistry and related fields.

A recent study published in Nature — Machine Learning introduces a bio-friendly and high-precision super-resolution imaging technique through self-supervised reconstruction structured illumination microscopy. This innovative approach aims to enhance imaging capabilities while minimizing environmental impact, marking a significant advancement in the field of imaging technology.

GlyContact has developed a method for analyzing glycan 3D structures at scale, utilizing advanced machine learning techniques to enhance the understanding of glycan interactions and their biological significance. This innovation represents a significant step forward in glycomics research, enabling more comprehensive studies of glycans in various biological contexts.

A systematic synthesis of open visual datasets for data-centric artificial intelligence (AI) in construction monitoring has been conducted, highlighting the reliance of the construction industry on visual data for AI and machine learning applications. The study reveals significant variability in the quality and characteristics of existing datasets, which hampers effective utilization in real-world scenarios.

A recent study published in Nature — Machine Learning explores the biologically-informed integration of drug representations for breast cancer treatment using deep learning techniques. This innovative approach aims to enhance the efficacy of treatment strategies by leveraging advanced computational methods to better understand drug interactions and tumor biology.