AIMC Topic: Machine Learning

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A tumor-infiltrating B lymphocytes -related index based on machine-learning predicts prognosis and immunotherapy response in lung adenocarcinoma.

Frontiers in immunology
INTRODUCTION: Tumor-infiltrating B lymphocytes (TILBs) play a pivotal role in shaping the immune microenvironment of tumors (TIME) and in the progression of lung adenocarcinoma (LUAD). However, there remains a scarcity of research that has thoroughly...

Optimising window size of semantic of classification model for identification of in-text citations based on context and intent.

PloS one
Citations in scientific literature act as channels for the sharing, transfer, and development of scientific knowledge. However, not all citations hold the same significance. Numerous taxonomies and machine learning models have been developed to analy...

Integrating data mining with transcranial focused ultrasound to refine neuralgia treatment strategies.

Journal of neuroscience methods
BACKGROUND: Neuralgia and other neuropathic pain are difficult to treat owing to their complicated etiology and a wide variety of responses to treatment. The novel neuromodulation technology transcranial focused ultrasound (tFUS) has intriguing impli...

Evaluation and comparison of machine learning algorithms for predicting discharge against medical advice in injured inpatients.

Surgery
BACKGROUND: Whether the application of machine learning algorithms offers an advantage over logistic regression in forecasting discharge against medical advice occurrences needs to be evaluated.

Which approach better predicts diabetes: Traditional econometric methods or machine learning? Evidence from a cross-sectional study in South Korea.

Computers in biology and medicine
To prevent chronic disease from getting worse, it is important to detect and predict it at an early stage. Therefore, the accuracy of the prediction is particularly important. To investigate the accuracy of different methods, this study compares the ...

aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.

Genome biology
A primary goal of microbial genome-wide association studies is identifying genomic variants associated with a particular habitat. Existing tools fail to identify known causal variants if the analyzed trait shaped the phylogeny. Furthermore, due to in...

Machine learning methods for determining skin age: A systematic review.

Journal of tissue viability
AIM: This systematic review explores how machine learning is used in determining skin aging, aiming to evaluate accuracy, limitations, and gaps in the current literature.

The spatiotemporal evolution of dissolved-phase NAPL plumes revealed by the integrated groundwater quality and machine learning models.

Water research
Rapid prediction of dissolved-phase contamination plume distributions is crucial for emergency remediation of aquifers contaminated with non-aqueous phase liquids (NAPLs). However, collecting and analyzing contaminated groundwater samples is expensiv...

Class balancing diversity multimodal ensemble for Alzheimer's disease diagnosis and early detection.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical for delaying progression and improving patient outcomes. Traditional ...

Pan-cancer analysis of CDC7 in human tumors: Integrative multi-omics insights and discovery of novel marine-based inhibitors through machine learning and computational approaches.

Computers in biology and medicine
Cancer remains a significant global health challenge, with the Cell Division Cycle 7 (CDC7) protein emerging as a potential therapeutic target due to its critical role in tumor proliferation, survival, and resistance. However, a comprehensive analysi...