AIMC Topic: Support Vector Machine

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Machine Learning and Deep Learning Approaches in Breast Cancer Survival Prediction Using Clinical Data.

Folia biologica
Breast cancer survival prediction can have an extreme effect on selection of best treatment protocols. Many approaches such as statistical or machine learning models have been employed to predict the survival prospects of patients, but newer algorith...

Alzheimer's Disease Classification Based on Multi-feature Fusion.

Current medical imaging reviews
BACKGROUND: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer's Disease (AD).

New Computational Tool Based on Machine-learning Algorithms for the Identification of Rhinovirus Infection-Related Genes.

Combinatorial chemistry & high throughput screening
BACKGROUND: Human rhinovirus has different identified serotypes and is the most common cause of cold in humans. To date, many genes have been discovered to be related to rhinovirus infection. However, the pathogenic mechanism of rhinovirus is difficu...

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches.

Current pharmaceutical design
BACKGROUND: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess...

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components.

Current topics in medicinal chemistry
In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The i...

Volumetric Histogram-Based Alzheimer's Disease Detection Using Support Vector Machine.

Journal of Alzheimer's disease : JAD
In this research work, machine learning techniques are used to classify magnetic resonance imaging brain scans of people with Alzheimer's disease. This work deals with binary classification between Alzheimer's disease and cognitively normal. Supervis...

Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites.

Combinatorial chemistry & high throughput screening
AIM AND OBJECTIVE: The accurate identification of protein-ligand binding sites helps elucidate protein function and facilitate the design of new drugs. Machine-learning-based methods have been widely used for the prediction of protein-ligand binding ...

Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer's Disease.

Journal of Alzheimer's disease : JAD
BACKGROUND: Predicting clinical course of cognitive decline can boost clinical trials' power and improve our clinical decision-making. Machine learning (ML) algorithms are specifically designed for the purpose of prediction; however. identifying opti...

Implementation of Bagged SVM Ensemble Model for Classification of Epileptic States Using EEG.

Current pharmaceutical biotechnology
BACKGROUND: To decipher EEG (Electroencephalography), intending to locate inter-ictal and ictal discharges for supporting the diagnoses of epilepsy and locating the seizure focus, is a critical task. The aim of this work was to find how the ensemble ...

Comparison of support vector machines based on particle swarm optimization and genetic algorithm in sleep staging.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Heart rate variability (HRV) can reflect the relationship between heart rhythm and sleep structure.