AI Medical Compendium Journal:
Interdisciplinary sciences, computational life sciences

Showing 1 to 10 of 118 articles

Identify Modules Associated with Immunotherapy Response from Mouse Tumor Profiles for Stratifying Cancer Patients.

Interdisciplinary sciences, computational life sciences
Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical benefits in cancer treatment, but only a minority of patients exhibit favorable response, highlighting the importance of determining patients who will benefit from immunothera...

Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.

Interdisciplinary sciences, computational life sciences
Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (...

CR-deal: Explainable Neural Network for circRNA-RBP Binding Site Recognition and Interpretation.

Interdisciplinary sciences, computational life sciences
circRNAs are a type of single-stranded non-coding RNA molecules, and their unique feature is their closed circular structure. The interaction between circRNAs and RNA-binding proteins (RBPs) plays a key role in biological functions and is crucial for...

CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation.

Interdisciplinary sciences, computational life sciences
Continual learning is the ability of a model to learn over time without forgetting previous knowledge. Therefore, adapting new data in dynamic fields like disease outbreak prediction is paramount. Deep neural networks are prone to error due to catast...

MVGNCDA: Identifying Potential circRNA-Disease Associations Based on Multi-view Graph Convolutional Networks and Network Embeddings.

Interdisciplinary sciences, computational life sciences
Increasing evidences have indicated that circular RNAs play a crucial role in the onset and progression of various diseases. However, exploring potential disease-associated circRNAs using conventional experimental techniques remains both time-intensi...

Prediction of Multimorbidity Network Evolution in Middle-Aged and Elderly Population Based on CE-GCN.

Interdisciplinary sciences, computational life sciences
PURPOSE: With the evolving disease spectrum, chronic diseases have emerged as a primary burden and a leading cause of mortality. Due to the aging population and the nature of chronic illnesses, patients often suffer from multimorbidity. Predicting th...

A Multi-View Feature-Based Interpretable Deep Learning Framework for Drug-Drug Interaction Prediction.

Interdisciplinary sciences, computational life sciences
Drug-drug interactions (DDIs) can result in deleterious consequences when patients take multiple medications simultaneously, emphasizing the critical need for accurate DDI prediction. Computational methods for DDI prediction have garnered recent atte...

Adaptive Multi-Kernel Graph Neural Network for Drug-Drug Interaction Prediction.

Interdisciplinary sciences, computational life sciences
 Combination therapy, which synergistically enhances treatment efficacy and inhibits disease progression through the combined effects of multiple drugs, has emerged as a mainstream approach for treating complex diseases and alleviating symptoms. Howe...

Reinforced Collaborative-Competitive Representation for Biomedical Image Recognition.

Interdisciplinary sciences, computational life sciences
Artificial intelligence technology has demonstrated remarkable diagnostic efficacy in modern biomedical image analysis. However, the practical application of artificial intelligence is significantly limited by the presence of similar pathologies amon...

NRGCNMDA: Microbe-Drug Association Prediction Based on Residual Graph Convolutional Networks and Conditional Random Fields.

Interdisciplinary sciences, computational life sciences
The process of discovering new drugs related to microbes through traditional biological methods is lengthy and costly. In response to these issues, a new computational model (NRGCNMDA) is proposed to predict microbe-drug associations. First, Node2vec...