Machine learning
Researchers from Sichuan University in Chengdu, People's Republic of China, have made significant advancements in using deep learning and artificial intelligence to diagnose migraine, a neuropsychiatric disorder. By combining multiple functional magnetic resonance imaging (fMRI) indicators with explainable artificial intelligence (XAI) techniques, the researchers aimed to improve the
Artificial intelligence
Researchers in Palo Alto, California, have made significant progress in the field of pharmacogenomics, a discipline that involves tailoring medical treatments to an individual's unique genetic profile. According to a recent study published in Mayo Clinic Proceedings Digital Health, the integration of high-throughput "omics" techniques with
Artificial intelligence
A recent systematic review and meta-analysis published in the Journal of Clinical Medicine has assessed the effectiveness of artificial intelligence (AI) in predicting complications and treatment outcomes for patients with spinal metastases. The study, conducted by researchers from the University College of Medical Sciences, found that AI models showed reasonable
Machine learning
Researchers at the University of Nigeria have published a comprehensive review on the integration of artificial intelligence (AI) with reverse docking methodologies in drug discovery, highlighting its transformative potential in personalized medicine. The study showcases the power of AI-driven reverse docking in streamlining the identification of drug targets and therapeutic
Machine learning
Researchers from Fondazione Policlinico Universitario Agostino Gemelli IRCCS have developed a hierarchical machine learning model to predict 12-month SLE activity in patients with systemic lupus erythematosus (SLE). This innovative approach considers patient demographics, laboratory, clinical features, treatments, and pathways, resulting in a reliable tool for predicting SLE activity. The model
Machine learning
Researchers at the Shanghai University of Traditional Chinese Medicine have made significant progress in developing a hybrid model that integrates clinical characteristics with radiomics features to predict adverse prognostic features in prostate cancer. The study aimed to develop MRI-based radiomics machine learning models for predicting adverse pathological prognostic features in
Artificial intelligence
Researchers from Jilin University have made significant findings in the application of deep learning (DL) in cancer research, particularly in analyzing complex data patterns. This study, published in Briefings in Bioinformatics, highlights the potential of DL in enhancing cancer diagnostics and therapeutics. The researchers employed DL models to integrate high-dimensional
Machine learning
Researchers at the University of Massachusetts Amherst are working on a pioneering project to develop a machine learning-based music intervention to prevent Alzheimer's disease among rural-residing older adults. The study aims to create an intelligent recommendation system to identify the optimal therapeutic music components to elicit engagement and
Machine learning
Researchers in Algeria have made significant strides in using machine learning and deep learning algorithms to improve the diagnosis and prognosis of glioblastoma, a highly malignant primary brain cancer. Traditional diagnosis and prognosis are often subjective and time-consuming, whereas advances in machine learning and deep learning have accelerated research by
Machine learning
A recent study from Wuhan, People's Republic of China, has developed and validated a novel prognostic model for breast cancer based on hypoxia-related genes and lactate metabolism-related genes. The aim of the study was to identify molecular subtypes capable of predicting patient prognosis and treatment response, thereby facilitating
Machine learning
A team of researchers at Xi'an Jiaotong-Liverpool University has developed a novel machine learning framework, called OncoTrace-TOO, to accurately classify the tissue-of-origin in cancer patients, facilitating clinical diagnosis and personalized treatment. The framework utilizes gene expression profiles and identifies pan-cancer discriminative molecular features to achieve high predictive accuracy.
Machine learning
Researchers from Rutgers University - The State University of New Jersey have conducted a study to explore the fairness of machine learning (ML) models in classifying colorectal and lung cancer patients. The study revealed that despite their potential to inform healthcare decisions, ML models are vulnerable to biases in data
