AIMC Topic: Machine Learning

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Predictive, integrative, and regulatory aspects of AI-driven computational toxicology - Highlights of the German Pharm-Tox Summit (GPTS) 2024.

Toxicology
The 9th German Pharm-Tox Summit (GPTS) and the 90th Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) took place in Munich from March 13-15, 2024. The event brought together over 700 participants fr...

PLRTE: Progressive learning for biomedical relation triplet extraction using large language models.

Journal of biomedical informatics
Document-level relation triplet extraction is crucial in biomedical text mining, aiding in drug discovery and the construction of biomedical knowledge graphs. Current language models face challenges in generalizing to unseen datasets and relation typ...

A systematic review on machine learning approaches in cerebral palsy research.

PeerJ
BACKGROUND: This review aims to explore advances in the field of cerebral palsy (CP) focusing on machine learning (ML) models. The objectives of this study is to analyze the advances in the application of ML models in the field of CP and to compare t...

Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis.

Biochemical genetics
Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the ute...

Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1.

Molecular diversity
Sphingosine kinases (SphKs) are a group of important enzymes that circulate at low micromolar concentrations in mammals and have received considerable attention due to the roles they play in a broad array of biological processes including apoptosis, ...

Harnessing machine learning technique to authenticate differentially expressed genes in oral squamous cell carcinoma.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: Advancements in early detection of the disease, prognosis and the development of therapeutic strategies necessitate tumor-specific biomarkers. Despite continuous efforts, no molecular marker has been proven to be an effective therapeutic t...

Clinically Guided Adaptive Machine Learning Update Strategies for Predicting Severe COVID-19 Outcomes.

The American journal of medicine
BACKGROUND: Machine learning algorithms are essential for predicting severe outcomes during public health crises like COVID-19. However, the dynamic nature of diseases requires continual evaluation and updating of these algorithms. This study aims to...

Student Pharmacists' Perceptions of Artificial Intelligence and Machine Learning in Pharmacy Practice and Pharmacy Education.

American journal of pharmaceutical education
OBJECTIVE: This study explored student pharmacists' perceptions and attitudes regarding artificial intelligence (AI) and machine learning (ML) in pharmacy practice. Due to AI/ML's promising prospects, understanding students' current awareness, compre...

Artificial intelligence-driven predictive framework for early detection of still birth.

SLAS technology
Predictive modeling is becoming increasingly popular in the context of early disease detection. The use of machine learning approaches for predictive modeling can help early detection of diseases thereby enabling medical experts to appropriate medica...

Leveraging Broad-Spectrum Fluorescence Data and Machine Learning for High-Accuracy Bacterial Species Identification.

Journal of biophotonics
Rapid and accurate identification of bacterial species is essential for the effective treatment of infectious diseases and suppression of antibiotic-resistant strains. The unique autofluorescence properties of bacterial cells are exploited for rapid ...