AIMC Topic: Support Vector Machine

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A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO.

Journal of integrative bioinformatics
Breast cancer is the leading diseases of death in women. It induces by a genetic mutation in breast cancer cells. Genetic testing has become popular to detect the mutation in genes but test cost is relatively expensive for several patients in develop...

Integrated analysis of machine learning and deep learning in chili pest and disease identification.

Journal of the science of food and agriculture
BACKGROUND: Chili is one of the most important and high-value vegetable crops worldwide. However, pest and disease infections are among the main limiting factors in chili cultivation. These diseases cannot be eradicated but can be handled and monitor...

Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery.

Neurorehabilitation and neural repair
BACKGROUND: Considerable evidence indicates that the functional connectome of the healthy human brain is highly stable, analogous to a fingerprint.

Fused Sparse Network Learning for Longitudinal Analysis of Mild Cognitive Impairment.

IEEE transactions on cybernetics
Alzheimer's disease (AD) is a neurodegenerative disease with an irreversible and progressive process. To understand the brain functions and identify the biomarkers of AD and early stages of the disease [also known as, mild cognitive impairment (MCI)]...

RAM-PGK: Prediction of Lysine Phosphoglycerylation Based on Residue Adjacency Matrix.

Genes
BACKGROUND: Post-translational modification (PTM) is a biological process that is associated with the modification of proteome, which results in the alteration of normal cell biology and pathogenesis. There have been numerous PTM reports in recent ye...

Machine Learning Feature Selection for Predicting High Concentration Therapeutic Antibody Aggregation.

Journal of pharmaceutical sciences
Protein aggregation can hinder the development, safety and efficacy of therapeutic antibody-based drugs. Developing a predictive model that evaluates aggregation behaviors during early stage development is therefore desirable. Machine learning is a w...

Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences.

Sensors (Basel, Switzerland)
Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and...

InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection.

Sensors (Basel, Switzerland)
Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fa...

Inter-protein residue covariation information unravels physically interacting protein dimers.

BMC bioinformatics
BACKGROUND: Predicting physical interaction between proteins is one of the greatest challenges in computational biology. There are considerable various protein interactions and a huge number of protein sequences and synthetic peptides with unknown in...