Predicting non-coding RNA-based regulatory networks in cancer metastasis using a heterogeneous hierarchical graph transformer.. If you have the appropriate software installed, you can download article ...

Wang, L. and Zhu, D. (2021) Tackling Ordinal Regression Problem for Heterogeneous Data Sparse and Deep Multi-Task Learning Approaches. Data Mining and Knowledge Discovery, 35, 1134-1161.

We previously introduced a “range corrected” Δ−machine learning potential (ΔMLP) that used deep neural networks to improve the accuracy of combined quantum mechanical/molecular mechanical (QM/MM) ...

Inspired by self-supervised learning, it uses a cross-view contrastive learning technique, splitting the graph into spatial and temporal views, designing specific graph neural networks, and using ...

Keywords: drug target identities, deep learning, molecular heterogeneous graph transformer, biological entity graph, machine learning Citation: Jiang X, Wen L, Li W, Que D and Ming L (2025) DTGHAT: ...

Besides, heterogeneous graph neural network is applied to mine the rich connectivity patterns implicit in the above HBN. For the second limitation, we design the complementary inter-view and ...

Drug repositioning, which identifies new therapeutic potential of approved drugs, is instrumental in accelerating drug discovery. Recently, to alleviate the effect of data sparsity on predicting ...

Contribute to Graph-and-Geometric-Learning/MTBench development by creating an account on GitHub.

Drug-drug interactions influence drug efficacy and patient prognosis, providing substantial research value. Some existing methods struggle with the challenges posed by sparse networks or lack the ...