Titlebook: Advanced Intelligent Computing in Bioinformatics; 20th International C De-Shuang Huang,Yijie Pan,Qinhu Zhang Conference proceedings 2024 Th

微生物

DeepMHAttGRU-DTI: Prediction of Drug-Target Interactions Based on Knowledge Graph Random Walk Embedduts. Experimental results demonstrate that the node feature vectors obtained using the Monte Carlo Random Walk based on Metropolis-Hastings algorithm (MHRW) based graph embedding algorithm are superior, and the GRU neural network model incorporating multi-head attention mechanism outperforms others.

大方一點(diǎn)

DiagNCF: Diagnosis Neural Collaborative Filtering for Accurate Medical Recommendationelationships between diseases and laboratory test results. We conducted experimental results on several medical datasets, specifically the MIMIC3 dataset, demonstrate that DiagNCF effectively provides accurate and efficient recommendations.

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后天習(xí)得

GSDPI: An Integrated Feature Extraction Framework for Predicting Novel Drug-Protein Interactiond in the reconstructed network to predict novel DPIs. The results demonstrated GSDPI could gain better prediction performance than several state-of-the-art models, achieving prediction accuracies of 0.9840, 0.9846, 0.9767, and 0.9878 on four public datasets, respectively.

Prophylaxis

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0302-9743 4880 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024...The total of 863 regular papers were carefully reviewed and selected from 2189 submissions...The intelligent computing annual conference primari

COMA

MELD

AAHLDMA: Predicting Drug-Microbe Associations Based on Bridge Graph Learningd toxicity. Utilizing microbes in antibacterial development is a new focus, yet understanding their complex interactions with drugs remains a challenge. Identifying microbe-drug associations enhances understanding and accelerates drug development, benefiting research and screening efforts. Given the

Jacket

Adaptive Weight Sampling and Graph Transformer Neural Network Framework for Cell Type Annotation of ingle cells, cell type annotation is the most common computational task in the downstream specific task. Different cell types differ in morphology, function, or biochemical properties, and these differences determine the specific function and role of cells in the organism. Traditional methods for ce

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BiLETCR: An Efficient PMHC-TCR Combined Forecasting Method at predicting the binding probability between peptides and major histocompatibility complex (pMHC) with T-cell receptors (TCR), a critical aspect of cancer immunotherapy. The method specifically targets the prediction of binding specificity between neoantigens and TCR within Class I MHC complexes.