Ligia Maria Nascimento Arantes, Andrey Oeiras Pedroso, Mayra Gonçalves Menegueti, Elucir Gir, Eliã Pinheiro Botelho, Ana Cristina de Oliveira E Silva, Renata Karina Reis
Factors Associated with Late Diagnosis of Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS) in a University Hospital in Brazil: Challenges to Achieving the 2030 Target
Viruses
drug repurposing offers viable strategy discovering new drugs therapeutic targets analysis druggene interactions however traditional experimental methods plagued costliness inefficiency despite graph convolutional network gcnbased models stateoftheart performance prediction reliance supervised learning makes vulnerable data sparsity common challenge drug discovery complicating model development study propose sgcldga novel computational model leveraging graph neural networks contrastive learning predict unknown druggene associations sgcldga employs gcns extract vector representations drugs genes original bipartite graph subsequently singular value decomposition svd employed enhance graph generate multiple views model performs contrastive learning across views optimizing vector representations contrastive loss function better distinguish positive negative samples final step involves utilizing inner product calculations determine association scores drugs genes experimental results dgidb40 dataset demonstrate sgcldgas superior performance compared six stateoftheart methods ablation studies case analyses validate significance contrastive learning svd highlighting sgcldgas potential discovering new druggene associations code dataset sgcldga freely available httpsgithubcomonemelonsgcldga