AIMC Topic: Machine Learning

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Applications of machine learning in potentially toxic elemental contamination in soils: A review.

Ecotoxicology and environmental safety
Soil contamination by potentially toxic elements (PTEs) poses substantial risks to the environment and human health. Traditional investigational methods are often inadequate for large-scale assessments because they are time-consuming, costly, and hav...

Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer patients.

Scientific reports
Muscle-Invasive Bladder Cancer (MIBC) is a more aggressive disease than non-muscle-invasive bladder cancer (NMIBC), with greater chances of metastasis. We sought to develop machine learning (ML) models to predict metastasis and prognosis in MIBC pati...

Recent Advances in Molecular Targeted Therapy for Inflammatory Bowel Disease.

Internal medicine (Tokyo, Japan)
Progress in the development of molecular-targeted therapies in the field of inflammatory bowel disease has been remarkable. With the advent of molecular-targeted agents, treatment strategies have changed, and higher-level treatment goals have been se...

A comprehensive review and evaluation of machine learning-based approaches for identifying tumor T cell antigens.

Computational biology and chemistry
The precise identification of tumor T-cell antigens (TTCAs) is crucial for advancements in cancer immunotherapy and other clinical uses. In contrast to the labor-intensive and time-consuming process of experimentally identifying TTCAs, computational ...

Exploration and exploitation in continual learning.

Neural networks : the official journal of the International Neural Network Society
Continual learning (CL) has received a surge of interest, particularly in parameter isolation approaches, aiming to prevent catastrophic forgetting by assigning a disjoint parameter set to each task. Despite their effectiveness, existing approaches o...

Optimizing agarase production from sp. using response surface methodology and machine learning models.

Environmental technology
Agarase enzymes are critical in industries like food, cosmetics, and medicine where they play a critical role in DNA recovery, food gelling, cosmetic formulations, and waste treatment. However, current agarase sources often face limitations related t...

Evaluating degradation efficiency of pesticides by persulfate, Fenton, and ozonation oxidation processes with machine learning.

Environmental research
Quantifying organic properties is pivotal for enhancing the precision and interpretability of degradation predictive machine learning (ML) models. This study used Binary Morgan Fingerprints (B-MF) and Count-Based Morgan Fingerprints (C-MF) to quantif...

Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction.

Journal of chemical theory and computation
Directionality in molecular and biomolecular networks plays an important role in the accurate representation of the complex, dynamic, and asymmetrical nature of interactions present in protein-ligand binding, signal transduction, and biological pathw...

Large-scale bulk and single-cell RNA sequencing combined with machine learning reveals glioblastoma-associated neutrophil heterogeneity and establishes a VEGFA neutrophil prognostic model.

Biology direct
BACKGROUND: Neutrophils play a key role in the tumor microenvironment (TME); however, their functions in glioblastoma (GBM) are overlooked and insufficiently studied. A detailed analysis of GBM-associated neutrophil (GBMAN) subpopulations may offer n...

Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning.

Scientific reports
The burden of diabetic foot ulcers (DFU) is exacerbated in diabetic patients with concomitant arteriosclerotic occlusion disease (ASO) in the lower extremities, who experience more severe symptoms and poorer prognoses. The study aims to develop a pre...