AI Medical Compendium Topic

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VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Immunogenetics
Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/ ) is an integrated pipeline designed to enhance epitope prediction and prioritization. This study a...

MDMNI-DGD: A novel graph neural network approach for druggable gene discovery based on the integration of multi-omics data and the multi-view network.

Computers in biology and medicine
Accurately predicting druggable genes is of paramount importance for enhancing the efficacy of targeted therapies, reducing drug-related toxicities and improving patients' survival rates. Nevertheless, accurately predicting candidate cancer-druggable...

AEmiGAP: AutoEncoder-Based miRNA-Gene Association Prediction Using Deep Learning Method.

International journal of molecular sciences
MicroRNAs (miRNAs) play a crucial role in gene regulation and are strongly linked to various diseases, including cancer. This study presents AEmiGAP, an advanced deep learning model that integrates autoencoders with long short-term memory (LSTM) netw...

Predicting miRNA-Disease Associations Based on Spectral Graph Transformer With Dynamic Attention and Regularization.

IEEE journal of biomedical and health informatics
Extensive research indicates that microRNAs (miRNAs) play a crucial role in the analysis of complex human diseases. Recently, numerous methods utilizing graph neural networks have been developed to investigate the complex relationships between miRNAs...

Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response.

Apoptosis : an international journal on programmed cell death
To demonstrate the efficacy of machine learning models in predicting mortality in melanoma cancer, we developed an interpretability model for better understanding the survival prediction of cancer. To this end, the optimal features were identified, t...

Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.

Journal of pediatric hematology/oncology
OBJECTIVES: This study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in pediatric patients with cancer who presenting febrile neutropenia (FN) at emergency department (ED) v...

Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review.

JCO clinical cancer informatics
PURPOSE: The adoption of artificial intelligence (AI) in health care may afford new avenues for personalized and patient-centered care. This systematic review explored the role of AI in symptom monitoring for adult cancer survivors.

Extending visual range of bacteria with upconversion nanoparticles and constructing NIR-responsive bio-microrobots.

Journal of colloid and interface science
The motility of bacteria is crucial for navigating competitive environments and is closely linked to physiological activities essential for their survival, such as biofilm development. Precise regulation of bacterial motility enhances our understandi...

Data - Knowledge driven machine learning model for cancer pain medication decisions.

International journal of medical informatics
BACKGROUND: Cancer pain is one of the most common symptoms in cancer patients, and drug decision-making in cancer pain management remains challenges. This study aims to develop machine learning models using real-world clinical data and prior knowledg...