Artificial Intelligence Medical Compendium

DNA encodes the cell's phenotype, but the same DNA sequence is interpreted differently in every cell type, developmental stage, and activity state. Existing deep-learning models solve only half of this puzzle: sequence-to-function models read DNA but... read more 

Protein engineering is shifting from model-based variant scoring to deciding what to measure next. This exposes a limitation of fixed-dataset benchmarks, which rank predictors without showing when their advantage survives as evidence accumulates. We ... read more 

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) mass spectrometry (MS) is central to routine clinical microbiology for species identification. Its combination with machine learning is constrained by fragmented public datasets, ... read more 

Large language model agents can plan and execute scientific analyses, but successful task completion does not establish whether analytical decisions remain consistent across independent sessions or whether computations follow the parameters specified... read more 

Proteins associated with the extracellular face of the plasma membrane ("surface proteins" or the "surfaceome") are important therapeutic targets because they can direct therapeutics to specific cell types and deliver them into cells. However, experi... read more 

Longitudinal intravital imaging is essential for understanding how neural populations evolve across behavioral states, yet converting large-scale recordings into biological insight remains a fundamental challenge because neural signals are degraded b... read more 

Identifying lifespan-extending compounds is an important objective in ageing research, but experimental screening remains costly and time consuming. This study evaluates supervised machine learning for predicting lifespan-extending compounds in Caeno... read more 

The ability to genetically program new catalytic elements into proteins greatly expands the range of chemistry accessible with engineered enzymes, as exemplified by the recent development of energy-transfer photoenzymes featuring organic photosensiti... read more 

Semantic segmentation of whole-slide images (WSIs) quantifies tissue composition and architecture, but existing models require extensive annotation and lose accuracy as the number of tissue classes increases or on heterogeneous, cross-scanner dataset... read more 

Deep learning has driven rapid progress in medical image analysis, with methods for tasks such as organ segmentation, anomaly detection, and radiology report generation now matching or surpassing expert performance on several benchmarks. While many m... read more