AIMC Topic: Machine Learning

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Integrated multiomics analysis and machine learning refine neutrophil extracellular trap-related molecular subtypes and prognostic models for acute myeloid leukemia.

Frontiers in immunology
BACKGROUND: Neutrophil extracellular traps (NETs) play pivotal roles in various pathological processes. The formation of NETs is impaired in acute myeloid leukemia (AML), which can result in immunodeficiency and increased susceptibility to infection.

Comparative analysis prediction of prostate and testicular cancer mortality using machine learning: accuracy study.

Sao Paulo medical journal = Revista paulista de medicina
BACKGROUND: The mortality rates of prostate and testicular cancer are higher mortality in the northeast region.

Optimizing Bi-LSTM networks for improved lung cancer detection accuracy.

PloS one
Lung cancer remains a leading cause of cancer-related deaths worldwide, with low survival rates often attributed to late-stage diagnosis. To address this critical health challenge, researchers have developed computer-aided diagnosis (CAD) systems tha...

Development and validation of predictive models for diabetic retinopathy using machine learning.

PloS one
OBJECTIVE: This study aimed to develop and compare machine learning models for predicting diabetic retinopathy (DR) using clinical and biochemical data, specifically logistic regression, random forest, XGBoost, and neural networks.

Integrative analysis of mitochondrial and immune pathways in diabetic kidney disease: identification of AASS and CASP3 as key predictors and therapeutic targets.

Renal failure
OBJECTIVES: Diabetic kidney disease (DKD) is driven by mitochondrial dysfunction and immune dysregulation, yet the mechanistic interplay remains poorly defined. This study aimed to identify key molecular networks linking mitochondrial and immune path...

Hypothesis: Net benefit as an objective function during development of machine learning algorithms for medical applications.

International journal of medical informatics
Net benefit is the most widely used metric for evaluating the clinical utility of medical prediction models. The approach applies decision analytic theory to weight true and false positives depending on the relative consequences of different decision...

Resveratrol in animal models of pancreatitis and pancreatic cancer: A systematic review with machine learning.

Phytomedicine : international journal of phytotherapy and phytopharmacology
BACKGROUND: Resveratrol (RES), a common type of plant polyphenols, has demonstrated promising therapeutic efficacy and safety in animal models of pancreatitis and pancreatic cancer. However, a comprehensive analysis of these data is currently unavail...

A new era of psoriasis treatment: Drug repurposing through the lens of nanotechnology and machine learning.

International journal of pharmaceutics
Psoriasis is a persistent inflammatory skin disorder characterized by hyper-proliferation and abnormal epidermal differentiation. Conventional treatments such as; topical therapies, phototherapy, systemic immune modulators, and biologics aim to relie...

Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning.

Journal of chemical information and modeling
While the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therapeutics has also slowed down. In the era of advanced computational methods, approaches like machine le...

Machine learning-based analysis of microplastic-induced changes in anaerobic digestion parameters influencing methane yield.

Journal of environmental management
Microplastics (MPs) present significant challenges for anaerobic digestion (AD) processes used in energy recovery from contaminated organic waste. Given that optimal AD conditions vary widely across studies when MPs are present, a robust predictive m...