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Researchers at Karolinska Institutet and KTH have developed a computational method that can reveal how cells change and specialize in the body. The study, which has been published in the journal PNAS, ...

Introduction: Optimizing fracturing parameters under multi-factor, complex conditions remains challenging in low-permeability reservoirs. Methods: We extract stage-aware construction-curve features, ...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

Bayesian Networks (BNs) are probabilistic models widely used for decision-making under uncertainty, relying on Conditional Probability Tables (CPTs) to represent the dependencies among variables.

Department of Intelligent Energy and Industry, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea Department of Intelligent Energy and Industry, Chung-Ang University, 84 ...

Artificial neural networks are machine learning models that have been applied to various genomic problems, with the ability to learn non-linear relationships and model high-dimensional data. These ...

Researchers have successfully employed an algorithm to identify potential mutations which increase disease risk in the noncoding regions our DNA, which make up the vast majority of the human genome.