AIMC Topic: Machine Learning

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Application of Machine Learning for Classification of Brain Tumors: A Systematic Review and Meta-Analysis.

World neurosurgery
BACKGROUND: Classifying brain tumors accurately is crucial for treatment and prognosis. Machine learning (ML) shows great promise in improving tumor classification accuracy. This study evaluates ML algorithms for differentiating various brain tumor t...

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients.

Seminars in neurology
The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex ...

Integrating transformer-based machine learning with SERS technology for the analysis of hazardous pesticides in spinach.

Journal of hazardous materials
This study introduces an innovative strategy for the rapid and accurate identification of pesticide residues in agricultural products by combining surface-enhanced Raman spectroscopy (SERS) with a state-of-the-art transformer model, termed SERSFormer...

One Descriptor to Fold Them All: Harnessing Intuition and Machine Learning to Identify Transferable Lasso Peptide Reaction Coordinates.

The journal of physical chemistry. B
Identifying optimal reaction coordinates for complex conformational changes and protein folding remains an outstanding challenge. This study combines collective variable (CV) discovery based on chemical intuition and machine learning with enhanced sa...

Boosting predictive models and augmenting patient data with relevant genomic and pathway information.

Computers in biology and medicine
The recurrence of low-stage lung cancer poses a challenge due to its unpredictable nature and diverse patient responses to treatments. Personalized care and patient outcomes heavily rely on early relapse identification, yet current predictive models,...

Novel clinical phenotypes, drug categorization, and outcome prediction in drug-induced cholestasis: Analysis of a database of 432 patients developed by literature review and machine learning support.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
BACKGROUND: Serum transaminases, alkaline phosphatase and bilirubin are common parameters used for DILI diagnosis, classification, and prognosis. However, the relevance of clinical examination, histopathology and drug chemical properties have not bee...

DNA Family: Boosting Weight-Sharing NAS With Block-Wise Supervisions.

IEEE transactions on pattern analysis and machine intelligence
Neural Architecture Search (NAS), aiming at automatically designing neural architectures by machines, has been considered a key step toward automatic machine learning. One notable NAS branch is the weight-sharing NAS, which significantly improves sea...

Machine learning identifies novel coagulation genes as diagnostic and immunological biomarkers in ischemic stroke.

Aging
BACKGROUND: Coagulation system is currently known associated with the development of ischemic stroke (IS). Thus, the current study is designed to identify diagnostic value of coagulation genes (CGs) in IS and to explore their role in the immune micro...