AIMC Topic: Support Vector Machine

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Hookworm Detection in Wireless Capsule Endoscopy Images With Deep Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
As one of the most common human helminths, hookworm is a leading cause of maternal and child morbidity, which seriously threatens human health. Recently, wireless capsule endoscopy (WCE) has been applied to automatic hookworm detection. Unfortunately...

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.

Bioinformatics (Oxford, England)
MOTIVATION: While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a tim...

Using machine learning for sequence-level automated MRI protocol selection in neuroradiology.

Journal of the American Medical Informatics Association : JAMIA
Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indica...

Post hoc support vector machine learning for impedimetric biosensors based on weak protein-ligand interactions.

The Analyst
Impedimetric biosensors for measuring small molecules based on weak/transient interactions between bioreceptors and target analytes are a challenge for detection electronics, particularly in field studies or in the analysis of complex matrices. Prote...

[Application of support vector machine in predicting in-hospital mortality risk of patients with acute kidney injury in ICU].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
OBJECTIVE: To construct an in-hospital mortality prediction model for patients with acute kidney injury (AKI) in intensive care unit (ICU) by using support vector machine (SVM), and compare it with the simplified acute physiology score II (SAPS-II) w...

Fast, Accurate, and Stable Feature Selection Using Neural Networks.

Neuroinformatics
Multi-voxel pattern analysis often necessitates feature selection due to the high dimensional nature of neuroimaging data. In this context, feature selection techniques serve the dual purpose of potentially increasing classification accuracy and reve...

Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation.

Neuroinformatics
The advances in neuroimaging methods reveal that resting-state functional fMRI (rs-fMRI) connectivity measures can be potential diagnostic biomarkers for autism spectrum disorder (ASD). Recent data sharing projects help us replicating the robustness ...

microRPM: a microRNA prediction model based only on plant small RNA sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine ...

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems.

Journal of neural engineering
OBJECTIVE: Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-bas...

Identification of recurrent risk-related genes and establishment of support vector machine prediction model for gastric cancer.

Neoplasma
This study sought to investigate genes related to recurrent risk and establish a support vector machine (SVM) classifier for prediction of recurrent risk in gastric cancer (GC).Based on the gene expression profiling dataset GSE26253, feature genes th...