AIMC Topic: Machine Learning

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Analysis of a nonsteroidal anti inflammatory drug solubility in green solvent via developing robust models based on machine learning technique.

Scientific reports
This study develops and evaluates advanced hybrid machine learning models-ADA-ARD (AdaBoost on ARD Regression), ADA-BRR (AdaBoost on Bayesian Ridge Regression), and ADA-GPR (AdaBoost on Gaussian Process Regression)-optimized via the Black Widow Optim...

Predicting drug-target interactions using machine learning with improved data balancing and feature engineering.

Scientific reports
Drug-Target Interaction (DTI) prediction is a vital task in drug discovery, yet it faces significant challenges such as data imbalance and the complexity of biochemical representations. This study makes several contributions to address these issues, ...

Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel.

Scientific reports
The peel of pomegranate (Punica granatum) is rich in bioactive compounds, specifically phenolic compounds and tannin compounds. However, there is still a lot of difficulty dealing with the extraction of these substances due to the limitations of trad...

Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning algorithm study.

Scientific reports
Kinesiophobia is particularly common in postoperative lung cancer patients, which causes patients may be reluctant to cough and move due to misperception, internal fear or fear of pain, and avoid rehabilitation training affecting postoperative recove...

Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic rhinosinusitis with nasal polyps.

Scientific reports
Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent inflammatory disease where immunomodulation plays a pivotal role. However, immuno-transcriptomic characteristics and its clinical relevance remains largely known. We analyzed transcript...

Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study.

JMIR formative research
BACKGROUND: Depression is the top contributor to global disability. Early detection of depression and depressive symptoms enables timely intervention and reduces their physical and social consequences. Prevalence estimates of depression approach 30% ...

A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: The COVID-19 pandemic has highlighted the need for robust and adaptable diagnostic tools capable of detecting the disease from diverse and continuously evolving data sources. Machine learning models, particularly convolutional neural netw...

Predicting genetic merit in Harnali sheep using machine learning techniques.

Tropical animal health and production
Machine learning techniques offer promising avenues for enhancing animal breeding programs by leveraging genomic and phenotypic data to predict valuable traits accurately. In this study, we evaluated seven machine learning algorithms viz., K-nearest ...

Enhancing particulate matter prediction in Delhi: insights from statistical and machine learning models.

Environmental monitoring and assessment
This study advances our approach to modeling particulate matter levels-specifically, PM and PM-in Delhi's dynamic urban environment through an extensive evaluation of traditional time series models (ARIMAX, SARIMAX) and machine learning models (RF, S...

AI-Driven Biomarker Discovery and Personalized Allergy Treatment: Utilizing Machine Learning and NGS.

Current allergy and asthma reports
PURPOSEĀ OF REVIEW: This review explores the transformative potential of artificial intelligence (AI) and next-generation sequencing (NGS) in allergy diagnostics and treatment. It focuses on leveraging these technologies to enhance precision in biomar...