AIMC Topic: Machine Learning

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Proteomics and Machine Learning-Based Approach to Decipher Subcellular Proteome of Mouse Heart.

Molecular & cellular proteomics : MCP
Protein compartmentalization to distinctive subcellular niches is critical for cardiac function and homeostasis. Here, we employed a rapid and robust workflow based on differential centrifugal-based fractionation with mass spectrometry-based proteomi...

Explainable no-code OECD-compliant machine learning models to predict the mutagenic activity of polycyclic aromatic hydrocarbons and their radical cation metabolites.

The Science of the total environment
Polycyclic aromatic hydrocarbons (PAHs) are persistent pollutants with well-known genotoxic and mutagenic effects, posing risks to ecosystems and human health. Their hydrophobic nature promotes accumulation in soils and aquatic environments, increasi...

Machine Learning in Predicting Child Malnutrition: A Meta-Analysis of Demographic and Health Surveys Data.

International journal of environmental research and public health
BACKGROUND: Childhood malnutrition remains a significant global public health concern. The Demographic and Health Surveys (DHS) program provides specific data on child health across numerous countries. This meta-analysis aims to comprehensively asses...

Data-Driven Modeling and Design of Sustainable High Tg Polymers.

International journal of molecular sciences
This paper develops a machine learning methodology for the rapid and robust prediction of the glass transition temperature (Tg) for polymers for the targeted application of sustainable high-temperature polymers. The machine learning framework combine...

Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms.

European journal of medical research
OBJECTIVES: This study aimed to identify risk factors for diabetic retinopathy (DR) and develop machine learning (ML)-based predictive models using routine laboratory data in patients with type 2 diabetes mellitus (T2DM).

Interpretable machine learning models for prolonged Emergency Department wait time prediction.

BMC health services research
OBJECTIVE: Prolonged Emergency Department (ED) wait times lead to diminished healthcare quality. Utilizing machine learning (ML) to predict patient wait times could aid in ED operational management. Our aim is to perform a comprehensive analysis of M...

Utilizing machine learning algorithms for predicting Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI).

BMC psychiatry
BACKGROUND: Accurately diagnosing Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI) shows significant challenges as traditional diagnostic methods fail to meet expectations due to patient hesitance and non-psychiatric h...

Leveraging machine learning for precision medicine: a predictive model for cognitive impairment in cholestasis patients.

BMC gastroenterology
BACKGROUND: Cholestasis, characterized by impaired bile flow, impacts cognitive function through systemic mechanisms, including inflammation and metabolic dysregulation. Despite its significance, targeted predictive models for cognitive impairment in...

Life's Crucial 9 and NAFLD from association to SHAP-interpreted machine learning predictions.

Scientific reports
Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease worldwide. Cardiovascular disease (CVD) and NAFLD share multiple common risk factors. Life's Crucial 9 (LC9), a novel indicator for comprehensive assessment of card...

Construction of a machine learning-based interpretable prediction model for acute kidney injury in hospitalized patients.

Scientific reports
In this observational study, we used data from 59,936 hospitalized adults to construct a model. For the models constructed with all 53 variables, all five models achieved acceptable performance with the validation cohort, with the extreme gradient bo...