Unknown

Dataset Information

0

Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster.

ABSTRACT: Asperger syndrome (AS) is subtype of autism spectrum disorder (ASD). Diagnosis and pathological analysis of AS through resting-state fMRI data is one of the hot topics in brain science. We employed a new model called the genetic-evolutionary random Support Vector Machine cluster (GE-RSVMC) to classify AS and normal people, and search for lesions. The model innovatively integrates the methods of the cluster and genetic evolution to improve the performance of the model. We randomly selected samples and sample features to construct GE-RSVMC, and then used the cluster to classify and extract lesions according to classification results. The model was validated by data of 157 participants (86 AS and 71 health controls) in ABIDE database. The classification accuracy of the model reached to 97.5% and we discovered the brain regions with significant differences, such as the Angular gyrus (ANG.R), Precuneus (PCUN.R), Caudate nucleus (CAU.R), Cuneus (CUN.R) and so on. Our method provides a new perspective for the diagnosis and treatment of AS, and a universal framework for other brain science research as the model has excellent generalization performance.

SUBMITTER: Bi XA 

PROVIDER: S-EPMC6262410 | biostudies-other | 2018

REPOSITORIES: biostudies-other

Json Xml
altmetric image

Publications

Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster.

Bi Xia-An XA   Chen Jie J   Sun Qi Q   Liu Yingchao Y   Wang Yang Y   Luo Xianhao X  

Frontiers in physiology 20181121

Asperger syndrome (AS) is subtype of autism spectrum disorder (ASD). Diagnosis and pathological analysis of AS through resting-state fMRI data is one of the hot topics in brain science. We employed a new model called the genetic-evolutionary random Support Vector Machine cluster (GE-RSVMC) to classify AS and normal people, and search for lesions. The model innovatively integrates the methods of the cluster and genetic evolution to improve the performance of the model. We randomly selected sample  ...[more]

Similar Datasets

Unknown Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease.
| S-EPMC5865739 | biostudies-literature
Unknown Research on Voxel-Based Features Detection and Analysis of Alzheimer's Disease Using Random Survey Support Vector Machine.
| S-EPMC8995748 | biostudies-literature
Unknown Using support vector machine and evolutionary profiles to predict antifreeze protein sequences.
| S-EPMC3292016 | biostudies-literature
Unknown Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.
| S-EPMC5784944 | biostudies-literature
Unknown Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine.
| S-EPMC9056223 | biostudies-literature
Unknown Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs.
| S-EPMC2147037 | biostudies-literature
Unknown DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.
| S-EPMC5788611 | biostudies-literature
Unknown Comparative analyses between retained introns and constitutively spliced introns in Arabidopsis thaliana using random forest and support vector machine.
| S-EPMC4128822 | biostudies-literature
Unknown Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers.
| S-EPMC5600879 | biostudies-literature
Unknown Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images.
| S-EPMC5265810 | biostudies-other