AIMC Topic: Support Vector Machine

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Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds.

PeerJ
BACKGROUND: To enhance the accuracy of allergen detection in cosmetic compounds, we developed a co-culture system that combines HaCaT keratinocytes (transfected with a luciferase plasmid driven by the AKR1C2 promoter) and THP-1 cells for machine lear...

Time-Frequency functional connectivity alterations in Alzheimer's disease and frontotemporal dementia: An EEG analysis using machine learning.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are prevalent neurodegenerative diseases characterized by altered brain functional connectivity (FC), affecting over 100 million people worldwide. This study aims to identify disti...

Hybrid statistical and machine-learning approach to hearing-loss identification based on an oversampling technique.

Computers in biology and medicine
BACKGROUND AND OBJECTIVES: Hearing loss is a crucial global health hazard exerting considerable social and physiological effects on spoken language and cognition. Patients affected by this condition may experience social and professional hardships th...

The significance of long chain non-coding RNA signature genes in the diagnosis and management of sepsis patients, and the development of a prediction model.

Frontiers in immunology
BACKGROUND: Sepsis is a life-threatening organ dysfunction condition produced by dysregulation of the host response to infection. It is now characterized by a high clinical morbidity and mortality rate, endangering patients' lives and health. The pur...

An exploration into the diagnostic capabilities of microRNAs for myocardial infarction using machine learning.

Biology direct
BACKGROUND: MicroRNAs (miRNAs) have shown potential as diagnostic biomarkers for myocardial infarction (MI) due to their early dysregulation and stability in circulation after MI. Moreover, they play a crucial role in regulating adaptive and maladapt...

Enhancing brain tumor classification by integrating radiomics and deep learning features: A comprehensive study utilizing ensemble methods on MRI scans.

Journal of X-ray science and technology
BACKGROUND AND OBJECTIVE: This study aims to assess the effectiveness of combining radiomics features (RFs) with deep learning features (DFs) for classifying brain tumors-specifically Glioma, Meningioma, and Pituitary Tumor-using MRI scans and advanc...

Limbic/paralimbic connection weakening in preschool autism-spectrum disorder based on diffusion basis spectrum imaging.

The European journal of neuroscience
This study aims to investigate the value of basal ganglia and limbic/paralimbic networks alteration in identifying preschool children with ASD and normal controls using diffusion basis spectrum imaging (DBSI). DBSI data from 31 patients with ASD and ...

Machine learning-based prognostic prediction and surgical guidance for intrahepatic cholangiocarcinoma.

Bioscience trends
The prognosis following radical surgery for intrahepatic cholangiocarcinoma (ICC) is poor, and optimal follow-up strategies remain unclear, with ongoing debates regarding anatomic resection (AR) versus non-anatomic resection (NAR). This study include...

Optimizing critical quality attributes of fast disintegrating tablets using artificial neural networks: a scientific benchmark study.

Drug development and industrial pharmacy
OBJECTIVE: The objective of this study is to create predictive models utilizing machine learning algorithms, including Artificial Neural Networks (ANN), k-nearest neighbor (kNN), support vector machines (SVM), and linear regression, to predict critic...

Radiomics for differential diagnosis of Bosniak II-IV renal masses via CT imaging.

BMC cancer
RATIONALE AND OBJECTIVES: The management of complex renal cysts is guided by the Bosniak classification system, which may be inadequate for risk stratification of patients to determine the appropriate intervention. Radiomics models based on CT imagin...