AIMC Topic: Machine Learning

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Artificial intelligence model for predicting early biochemical recurrence of prostate cancer after robotic-assisted radical prostatectomy.

Scientific reports
Prostate cancer remains a significant public health concern, with a substantial proportion of patients experiencing biochemical recurrence (BCR) after radical prostatectomy (RP). Traditional risk models, such as CAPRA-S, have demonstrated moderate pr...

Image-based honey bee larval viral and bacterial diagnosis using machine learning.

Scientific reports
Honey bees are essential pollinators of ecosystems and agriculture worldwide. With an estimated 50-80% of crops pollinated by honey bees, they generate approximately $20 billion annually in market value in the U.S. alone. However, commercial beekeepe...

CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph node metastasis in peripheral lung cancer.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Accurate preoperative assessment of occult lymph node metastasis (OLNM) plays a crucial role in informing therapeutic decision-making for lung cancer patients. Computed tomography (CT) is the most widely used imaging modality for preopera...

GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data.

BMC genomics
BACKGROUND: Single-cell RNA sequencing analysis faces critical challenges including high dimensionality, sparsity, and complex topological relationships between cells. Current methods struggle to simultaneously preserve global structure, model cellul...

Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis.

Scientific reports
The accurate classification of obesity is essential for public health and clinical decision-making. Traditional anthropometric measures such as body mass index (BMI) have limitations in differentiating between fat and lean mass. This study aimed to e...

Systematic selection of best performing mathematical models for in vitro gas production using machine learning across diverse feeds.

Scientific reports
In vitro gas production (GP) is commonly used to evaluate ruminant feed, yet its accurate interpretation requires robust mathematical modeling. This study systematically explores a wide array of nonlinear models to explain GP dynamics across various ...

Balancing ethics and statistics: machine learning facilitates highly accurate classification of mice according to their trait anxiety with reduced sample sizes.

Translational psychiatry
Understanding how individual differences influence vulnerability to disease and responses to pharmacological treatments represents one of the main challenges in behavioral neuroscience. Nevertheless, inter-individual variability and sex-specific patt...

Immune-related adverse events of neoadjuvant immunotherapy in patients with perioperative cancer: a machine-learning-driven, decade-long informatics investigation.

Journal for immunotherapy of cancer
Research on neoadjuvant immunotherapy (NAI) is increasingly focusing on immunotherapy-related adverse events (AEs). However, many unknowns remain in this field. Hence, through the machine learning (ML)-driven informatics analysis, this study aimed to...

Application of Raman spectroscopy and machine learning for determination of pro-toxicant activation in CYP2E1-expressing cells.

Analytical methods : advancing methods and applications
Developing a convenient, accurate, and cost-effective analytical method for the detection of Cytochrome P450 (CYP) mediated drug bioactivation remains a challenge. The present study proposes a method using Raman spectroscopy (RS) combined with machin...

Machine learning-assisted MALDI-MSI to characterize hippocampal subregion lipid and purine metabolic alterations in depression-related dry eye disease.

Analytical methods : advancing methods and applications
Dry eye disease (DED) and depression exhibit high comorbidity, yet lipid and purine diagnostic biomarkers for depression-related DED remain unidentified. In this study, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI)...