Living matter remains the quintessential puzzle of biological sciences, a question that embodies the intricate complexity and stunning diversity of life forms. A new study suggests that one viable ...

In this study, we propose a novel algorithmic trading model CNN-TA using a 2-D convolutional neural network based on image processing properties. In order to convert financial time series into 2-D ...

Subscribe to Technology Networks’ daily newsletter ... when the body is fighting an infection and start to break molecules down. The products of this molecular breakdown could stimulate activity in ...

Subgraph-conditioned Graph Information Bottleneck (S-CGIB) is a novel architecture for pre-training Graph Neural Networks in molecular property prediction and developed by NS Lab, CUK based on pure ...

This study used machine learning-based models (ML), molecular docking (MD), and molecular dynamics ... Incorporating big chemical data together with artificial neural network algorithms has enhanced ...

This study investigates the potential clinical synergy between the PARP inhibitor niraparib (Zejula) and concomitant statins, exploring their combined effects on progression-free survival (PFS) in ...

Recently, graph convolutional networks (GCNs) have gained prominence in scRNA-seq data clustering because they effectively learn cell representations by capturing the relationship between cells.

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Abstract: Identification of cancer driver genes is crucial for understanding the molecular mechanisms of cancer. To address the limitations of graph convolutional networks-based cancer driver gene ...