AIMC Topic: Machine Learning

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From disinfectant to neurodegeneration: Integrating machine learning and mendelian randomization reveals triclosan as a novel environmental risk factor for Alzheimer's disease.

Environmental pollution (Barking, Essex : 1987)
This study systematically investigated the association between triclosan (TCS) exposure and Alzheimer's disease (AD) risk via integrated bioinformatics approaches. TCS-AD-related genes were identified using bioinformatics tools and public databases, ...

Cognitive prediction using regional connectivities and network biomarkers in Alzheimer's disease.

Neuroscience
Achieving a deep understanding of brain mechanisms requires multi-scale perspectives to capture the architecture of complex networks. In this study, we focused on patients with cognitive impairment and constructed individual brain networks from neuro...

All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models.

Journal of chemical information and modeling
Proteochemometric models (PCMs) are used in computational drug discovery to employ both protein and ligand representations jointly for bioactivity prediction. While machine learning (ML) and deep learning (DL) have come to dominate PCMs, often servin...

UCP2 is identified as a therapeutic target for abdominal aortic aneurysm by comprehensive bioinformatic analysis and experimental validation.

Biochemical and biophysical research communications
Abdominal aortic aneurysm (AAA) is a potentially life-threatening vascular condition that currently lacks effective pharmacological treatment. The disease is strongly associated with chronic inflammation, where immune cells like macrophages play a cr...

MGRL-DDI: Multiview Graph Representation Learning for Accurate Drug-Drug Interaction Prediction.

Journal of chemical information and modeling
Drug-drug interactions (DDIs) present a significant challenge in clinical practice, as they may lead to adverse reactions, diminished therapeutic efficacy, and serious risks to patient safety. However, most existing methods depend on single-view repr...

Machine learning prediction of clinical pregnancy in endometriosis patients following fresh IVF/ICSI-ET.

European journal of medical research
BACKGROUND: Fresh embryo transfer reduces waiting time and minimizes embryo cryodamage for endometriosis (EM) patients. The current prediction models for fresh embryo transfer outcomes in EM primarily rely on logistic regression, with limited applica...

Recognition of molecular clusters and a novel prognostic signature based on natural killer cell-related genes in skin cutaneous melanoma.

World journal of surgical oncology
BACKGROUND: Skin cutaneous melanoma (SKCM) is the third most common type of cutaneous malignant tumor with a poor prognosis. This research aimed to recognize molecular clusters and develop a novel prognostic signature based on natural killer (NK) cel...

Detecting suicide risk in bipolar disorder patients from lymphoblastoid cell lines genetic signatures.

Translational psychiatry
This research aimed to develop a machine learning algorithm to predict suicide risk in bipolar disorder (BD) patients using RNA sequencing analysis of lymphoblastoid cell lines (LCLs). By identifying differentially expressed genes (DEGs) between high...

Development and validation of a machine learning-based prediction model for frailty in older adults with diabetes: a study protocol for a retrospective cohort study.

BMJ open
INTRODUCTION: Frailty is a common condition in older adults with diabetes, which significantly increases the risk of adverse health outcomes. Early identification of frailty in this population is crucial for implementing timely interventions. However...