A comprehensive review evaluates twenty computational algorithms designed to deconvolve mixed cellular signals from low-resolution spatial transcriptomics data into distinct ...
Researchers introduced OTGCL, an optimal transport augmentation framework that preserves node-level degree structure during graph contrastive learning.
A Kyoto University team has developed Species-OT, a computational method that compares the geometric shapes formed by gene expression patterns ...
Researchers propose a context-aware extension of the Gromov–Wasserstein distance that can compare structured data whose internal geometry changes with conditions ...
A new data-driven framework called STREAM models ICU patients as trajectories through a multidimensional physiological space, identifying state outliers whose ...
A new study applies Wasserstein distance and linear programming to benchmark ten São Paulo river basins against 23 European countries, ...
Researchers have developed CardamomOT, an optimal transport-based framework that jointly infers gene regulatory networks, reconstructs unobserved protein trajectories, and enables ...
A large-scale UK Biobank study shows that five leading machine learning variant annotation methods produce markedly different calibration, power, and ...
Researchers have developed CCOT, a condition-controlled optimal transport framework that predicts single-cell responses to drug perturbations, including to compounds never ...
A large-scale study finds that pretraining tabular self-supervised models on carefully selected subsets of 100 to 1000 unlabeled examples often ...
Embark on a thrilling journey of discovery with Scienmag.com—your ultimate source for cutting-edge breakthroughs. Immerse yourself in a world where curiosity knows no limits and tomorrow’s possibilities become today’s reality!
