AIMC Topic: Machine Learning

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AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.

Clinical and experimental medicine
Cancer's staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic...

Speech-based respiratory diagnostics: A study on COVID-19 detection with machine learning.

PloS one
Respiratory sound analysis has emerged as a promising approach for detecting and diagnosing respiratory diseases, including COVID-19. This study investigates using OpenSMILE features for COVID-19 detection using vowel speech sounds /a/, /e/, and /o/ ...

Predicting the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures using machine learning algorithms.

PloS one
OBJECTIVE: To construct and validate a predictive model for the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures based on machine learning algorithms, so as to provide decision-making support for clinic...

BLDC motor's speed and torque modelling through hybrid machine learning based approach of nonlinear autoregressive neural network with exogenous inputs (NARX-NN).

PloS one
Modeling the complex nonlinear dynamics of Brushless DC motors has been a prominent research focus over the past two decades, driven by their superior advantages and widespread industrial applications. Despite extensive efforts, achieving high-effici...

Interpretable machine learning framework for predicting pesticide phytotoxicity in wastewater reuse: Integrating molecular, quantum, and experimental descriptors.

Environmental research
Pesticides are essential for crop protection, but their potential toxicity poses significant environmental and health risks. Although numerous toxicological studies have been conducted, accurately predicting pesticide phytotoxicity remains challengin...

Machine learning in ecotoxicology: Pollutant exposure levels and detection, biotoxicity and environmental behavior prediction.

The Science of the total environment
In recent years, the worsening problem of environmental pollution and the limitations of traditional toxicological assays have accelerated the adoption of machine learning (ML) in ecotoxicology. ML enables rapid and accurate prediction of pollutant e...

Multimodal Feature Fusion for Bone Toxicity Prediction and Local Platform.

Journal of chemical information and modeling
Drug-induced osteotoxicity refers to the detrimental effects of certain drugs on bone metabolism, density, and structure, posing serious safety concerns in clinical practice, drug development, and environmental health. Although previous studies have ...

Data-driven cluster analysis identifies three clinical phenotypes in hemodialysis patients.

Renal failure
Clinical heterogeneity among hemodialysis patients necessitates precision medicine approaches transcending conventional single-parameter management. Through machine learning analysis of 1,207 maintenance hemodialysis patients, we developed a novel tw...

Microbial and seminal traces of sexual intercourse and forensic implications.

Microbiome
BACKGROUND: The increasing numbers of sexual violence and unresolved rape cases require alternative approaches with higher evidential value to complement existing forensic tools. Predicting recent intercourse is crucial in forensic casework on sexual...

Machine learning-powered discovery of a novel berberine derivative inducing SCD-dependent ferroptosis in osteosarcoma.

Journal of translational medicine
BACKGROUND: Despite decades of therapeutic development, osteosarcoma survival remains poor. Although berberine (BBR) shows anti-tumor activity, its efficacy is limited. We addressed this through structural modification and machine learning-guided dis...