AIMC Topic: Machine Learning

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TabNet and TabTransformer: Novel Deep Learning Models for Chemical Toxicity Prediction in Comparison With Machine Learning.

Journal of applied toxicology : JAT
The prediction of chemical toxicity is crucial for applications in drug discovery, environmental safety, and regulatory assessments. This study aims to evaluate the performance of advanced deep learning architectures, TabNet and TabTransformer, in co...

Machine learning-driven optical microfiltration device for improved nanoplastic sampling and detection in water systems.

Journal of hazardous materials
The rising presence of nanoplastics in water poses toxicity risks and long-term ecological and health impacts. Detecting nanoplastics remains challenging due to their small size, complex chemistry, and environmental interference. Traditional filtrati...

Data alignment based adversarial defense benchmark for EEG-based BCIs.

Neural networks : the official journal of the International Neural Network Society
Machine learning has been extensively applied to signal decoding in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). While most studies have focused on enhancing the accuracy of EEG-based BCIs, more attention should be given to thei...

Dual-Site Targeting by Peptide Inhibitors of the N-Terminal Domain of Hsp90: Mechanism and Design.

Journal of chemical information and modeling
Heat shock protein 90 (Hsp90) is a pivotal molecular chaperone crucial in the maturation of client proteins, positioning it as a significant target for cancer therapy. However, the design of effective Hsp90 inhibitors presents substantial challenges ...

Machine Learning for Predicting Critical Events Among Hospitalized Children.

JAMA network open
IMPORTANCE: Unrecognized deterioration among hospitalized children is associated with a high risk of mortality and morbidity. The current approach to pediatric risk stratification is fragmented, as each hospital unit (emergency, ward, or intensive ca...

Identification of Factors Affecting Prostate Cancer Using Machine Learning Methods: A Systematic Review.

Asian Pacific journal of cancer prevention : APJCP
BACKGROUND: Prostate cancer is identified as the second cause of malignancy worldwide and the fifth cause of death among men. Considering the upward trend in cancer incidence and mortality rate due to this disease, the identification of risk factors ...

Integration of Bioinformatics and Machine Learning Strategies Identifies Ferroptosis and Immune Infiltration Signatures in Peri-Implantitis.

International journal of molecular sciences
Peri-implantitis (PI) is a chronic inflammatory disease that ultimately leads to the dysfunction and loss of implants with established osseointegration. Ferroptosis has been implicated in the progression of PI, but its precise mechanisms remain uncle...

Machine Learning Multimodal Model for Delirium Risk Stratification.

JAMA network open
IMPORTANCE: Automating the identification of risk for developing hospital delirium with models that use machine learning (ML) could facilitate more rapid prevention, identification, and treatment of delirium. However, there are very few reports on th...

Global burden of non-melanoma skin cancers among older adults: a comprehensive analysis using machine learning approaches.

Scientific reports
Non-melanoma skin cancers (NMSCs), including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), have shown significant global increases in burden, particularly among older adults, with wide regional, gender, and socio-demographic dispariti...

Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy.

Scientific reports
Anoikis and immune cell infiltration are pivotal factors in the pathophysiological mechanism of diabetic nephropathy (DN), yet a comprehensive understanding of the mechanism is lacking. This work aimed to pinpoint distinctive anoikis-related genes (A...