AIMC Topic: Machine Learning

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Multimodal MRI analysis selecting key brain features for machine learning based classification of diabetic neuropathic pain and phenotypes.

Journal of the neurological sciences
Cerebral alterations are associated with diabetic peripheral neuropathy (DPN) and neuropathic pain, including reductions in brain volumes, cortical thickness, sulcus depth, and alterations in metabolites and functional connectivity. This study combin...

Machine learning-based evaluation of risk factors for carbapenem-resistant dissemination in neonatal units.

mSystems
Healthcare-associated infections (HAIs), particularly in neonatal intensive care units (NICUs), pose significant challenges due to neonates' vulnerability and the rapid infection spread. However, risk factors facilitating pathogen persistence and dis...

Effect of immune-related intratumoral microbiota and host gene expression on cancer prognosis.

mSystems
UNLABELLED: The intratumoral microbiota has been identified as an indispensable part of the tumor microenvironment (TME). However, the relationship between the intratumoral microbiota and host gene expression, as well as its impact on prognosis and T...

Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data.

Journal of chemical information and modeling
Today, machine learning models are employed extensively to predict the physicochemical and biological properties of molecules. Their performance is typically evaluated on in-distribution (ID) data, i.e., data originating from the same distribution as...

Clinical parameters-based machine learning models for predicting intraoperative hemodynamic instability in hypertensive pheochromocytomas and paragangliomas patients.

World journal of urology
PURPOSE: To create machine learning (ML) models based on inflammatory markers and coagulation parameters for predicting intraoperative hemodynamic Instability (HI) in sustained hypertensive patients with pheochromocytomas and paragangliomas (PPGLs).

Integrating Machine Learning with Flow-Imaging Microscopy for Automated Monitoring of Algal Blooms.

Environmental science & technology
Real-time monitoring of phytoplankton in freshwater systems is critical for early detection of harmful algal blooms (HABs) to enable efficient response by water management agencies. This manuscript presents an image processing pipeline developed to a...

Efficacious paper-based colorimetric detection of bacterial contamination in vegetables utilizing indicator dyes and machine learning.

Food chemistry
Food contamination from bacteria and resulting spoilage has been a persistent problem in the supply chain, leading to substantial waste and financial loss. Likewise, vegetables are prone to microbial contamination due to poor/unhygienic agricultural ...

Comparative machine learning strategies for improving antioxidant properties and aroma quality in fermented mung bean milkby Lactobacillus plantarum PC4.

International journal of food microbiology
This study compares least squares support vector machine (LSSVM) and artificial neural network (ANN) models, integrated with the NSGA-II algorithm, to optimize the fermentation of mung bean milk by Lactobacillus plantarum PC4. Given its superior pred...

Genetic algorithm-optimized neural network outperforms TNM staging in predicting rapidly progressive nasopharyngeal carcinoma: Reassessing adjuvant chemotherapy benefit via propensity score matching.

European journal of cancer (Oxford, England : 1990)
PURPOSE: To establish machine learning-based predictive models for rapidly progressive nasopharyngeal carcinoma (RP-NPC), defined as disease progression within 24 months post-initial treatment, and to assess differential survival benefits of adjuvant...

Immune-enhanced machine learning approach for early detection of precancerous colorectal neoplasia: Insights from biomarkers in routine health checkups.

European journal of cancer (Oxford, England : 1990)
BACKGROUND AND AIMS: Current screening strategies for colorectal cancer (CRC) rely on colonoscopy, an invasive procedure with limited capacity to address individual risk. There is growing interest in integrating noninvasive immune biomarkers to impro...