AIMC Topic: Machine Learning

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Identification of key diagnostic and prognostic biomarkers for aortic valve stenosis with coronary artery disease through immunological profiling integrating proteomics, single-cell sequencing, and machine learning.

Biochemical and biophysical research communications
BACKGROUND: Aortic valve stenosis with coronary artery disease (AS-CAD) represents a common yet complex cardiovascular comorbidity, characterized by multifactorial pathogenesis and a lack of specific serum biomarkers. These limitations hinder early d...

Improving outbreak forecasts through model augmentation.

Proceedings of the National Academy of Sciences of the United States of America
Accurate forecasts of disease outbreaks are critical for effective public health responses, management of healthcare surge capacity, and communication of public risk. There are a growing number of powerful forecasting methods that fall into two broad...

Use of machine learning for risk stratification of chest pain patients in the emergency department.

BMC medical informatics and decision making
OBJECTIVE: To improve the initial risk assessment capability for emergency chest pain patients without relying on laboratory test results.

Characterizing immune profiles in hepatocellular carcinoma patients benefiting from pembrolizumab and lenvatinib using machine learning.

BMC cancer
BACKGROUND: Combination immunotherapies, such as pembrolizumab plus lenvatinib (PL), are commonly used in treatment for unresectable hepatocellular carcinoma (uHCC). However, it remains challenging to predict which patients will benefit from this the...

3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.

Journal of computer-aided molecular design
To address limitations of conventional Quantitative Structure-Activity Relationship (QSAR) descriptors in capturing molecular electronic and spatial complexity, we developed a high-dimensional framework using three-dimensional electron density featur...

μOR-ligand: target-aware view-based hybrid feature selection for μ-opioid receptor ligand functional classification.

Journal of computer-aided molecular design
Understanding active functional class (agonist vs antagonist) at the human μ-opioid receptor (μOR) is critical for drug discovery and safety assessment. While recent machine learning models such as ExtraTrees (ET) and message-passing neural networks ...

Identification of hub necroptosis-related targets and discovery of potential natural inhibitors in ulcerative colitis based on bioinformatics and computer-aided drug design.

Journal of computer-aided molecular design
Ulcerative colitis (UC) is a chronic inflammatory bowel disease with a complex pathogenesis and limited treatment options. Recently, necroptosis has been found to play a significant role in UC. This study aimed to investigate necroptosis-related mech...

Artificial intelligence for predicting depression anxiety and stress using psychometric data.

Scientific reports
Mental health is a crucial aspect of overall well-being, yet it is often overlooked due to stigma and limited accessibility to care. This study investigates the ability of artificial intelligence (AI) to predict common psychological conditions, depre...

Machine learning identifies exosome related gene signatures for early prediction of non-small cell lung cancer.

Scientific reports
Non-small cell lung cancer (NSCLC) remains a major health challenge worldwide, mainly due to the lack of effective early diagnostic biomarkers. Exosome-related genes have recently emerged as potential diagnostic markers due to their roles in tumor pr...

Reducing annotation burden in physical activity research using vision language models.

Scientific reports
Data from wearable devices collected in free-living settings, and labelled with physical activity behaviours compatible with health research, are essential for both validating existing wearable-based measurement approaches and developing novel machin...