AIMC Topic: Support Vector Machine

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Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets.

Journal of biomedical optics
We developed machine learning methods to identify fibrolipidic and fibrocalcific A-lines in intravascular optical coherence tomography (IVOCT) images using a comprehensive set of handcrafted features. We incorporated features developed in previous st...

Machine-Learning Classification Suggests That Many Alphaproteobacterial Prophages May Instead Be Gene Transfer Agents.

Genome biology and evolution
Many of the sequenced bacterial and archaeal genomes encode regions of viral provenance. Yet, not all of these regions encode bona fide viruses. Gene transfer agents (GTAs) are thought to be former viruses that are now maintained in genomes of some b...

Learning to detect and understand drug discontinuation events from clinical narratives.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Identifying drug discontinuation (DDC) events and understanding their reasons are important for medication management and drug safety surveillance. Structured data resources are often incomplete and lack reason information. In this article...

Sign-Consistency Based Variable Importance for Machine Learning in Brain Imaging.

Neuroinformatics
An important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables per single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable i...

Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites.

Neuroinformatics
Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the...

MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry.

Brain imaging and behavior
Neuroanatomical pattern classification using support vector machines (SVMs) has shown promising results in classifying Multiple Sclerosis (MS) patients based on individual structural magnetic resonance images (MRI). To determine whether pattern class...

Statistical algorithms for emotion classification via functional connectivity.

Journal of integrative neuroscience
Pattern recognition algorithms decode emotional brain states by using functional connectivity measures which are extracted from EEG signals as input to the statistical classifiers. An open-access EEG dataset for emotional state analysis is used to cl...

Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes.

JCO clinical cancer informatics
PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools that help to monitor and prioritize the literature to understand the clinical implications of pathogenic genetic variants. We developed and ...

Machine-learning techniques for the prediction of protein-protein interactions.

Journal of biosciences
Protein-protein interactions (PPIs) are important for the study of protein functions and pathways involved in different biological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental tec...

Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine.

Journal of gastrointestinal cancer
PURPOSE: The utility of chromoendoscopy for early gastric cancer (GC) was determined by machine learning using data of color differences.