AIMC Topic: Machine Learning

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Uncovering age-specific subtypes of pediatric obesity and metabolic syndrome using machine learning algorithms.

Scientific reports
Identifying new subgroups among children and adolescents with obesity and metabolic syndrome requires advanced clustering techniques capable of analyzing complex multidimensional data. This study aimed to employ machine learning methods to enhance th...

Meta simulation approach for evaluating machine learning method selection in data limited settings.

Scientific reports
Selecting appropriate machine learning (ML) methods for domain-specific tasks remains a persistent challenge, particularly in medicine where datasets are often small, heterogeneous, and incomplete. Traditional benchmarking strategies rely on limited ...

A data-driven machine learning framework to predict side effects of AstraZeneca and sinopharm COVID-19 vaccines.

Scientific reports
Due to the widespread COVID-19 vaccinations, we are focusing more on side effects to immunizations that might affect people's perceptions, and ultimately vaccine hesitancy. Machine learning (ML)-based predictive models using individual-level data ser...

Prognostic value of combining nutritional inflammatory index trajectories and tumor characteristics in cervical cancer.

BMC women's health
OBJECTIVE: This investigation seeks to examine how varying longitudinal patterns in nutritional inflammatory index (NII) correlate with clinical outcomes in cervical cancer patients, while developing predictive models for prognosis.

Deriving three one dimensional NMR spectra from a single experiment through machine learning.

Nature communications
Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for analyzing complex mixtures due to its ability to manage matrix complexity, provide detailed molecular insights, and preserve sample integrity. In metabolomics, NMR enables the ident...

Improving newborn screening accuracy through genome sequencing, targeted metabolomics, and machine learning.

BMC medical genomics
BACKGROUND: Newborn screening (NBS) enables early detection of metabolic disorders, but current tandem mass spectrometry (MS/MS) methods often lead to false positives and require confirmatory testing, causing diagnostic delays. We evaluated whether i...

Using machine learning for detection of Parkinson's disease and mild cognitive impairment.

PloS one
BACKGROUND: Parkinson's disease is a movement disorder featuring motor symptoms and cognitive decline, which can manifest as mild cognitive impairment. The incidence of mild cognitive impairment increases with disease progression, and Parkinson's dis...

Advanced machine learning models for accurate water quality classification and WQI prediction: Implications for aquatic disease risk management.

The Science of the total environment
Accurate classification of water quality and precise prediction of the Water Quality Index (WQI) are essential for safeguarding aquatic ecosystems and mitigating disease risks in aquaculture. This study systematically evaluates multiple machine learn...

A Simple Framework for Collaborative Development of Predictive Models Trained on Proprietary Data.

Journal of chemical information and modeling
We present a simple methodology that allows the building and sharing of predictive models without compromising the confidentiality of the structures of the training series. Multiple shared models can be used to obtain ensemble models, providing bette...

Generative AI for the Design of Molecules: Advances and Challenges.

Journal of chemical information and modeling
The design of novel molecules underpins advances in both drug discovery and biomaterials engineering. Traditional approaches, from natural product isolation to high-throughput screening, have delivered important therapeutics but remain costly, ineffi...