AIMC Topic: Support Vector Machine

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Multi-center machine learning in imaging psychiatry: A meta-model approach.

NeuroImage Apr 17, 2017
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizoph...
Pattern Recognition, Automated Neuroimaging Schizophrenia Datasets as Topic Support Vector Machine Magnetic Resonance Imaging Humans Multicenter Studies as Topic Female Male
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Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.

BMC medical informatics and decision making Apr 13, 2017
BACKGROUND: Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (...
Surveys and Questionnaires Support Vector Machine Emotions Middle Aged Diagnosis, Computer-Assisted Pattern Recognition, Automated Neuropsychological Tests Diffusion Tensor Imaging Migraine Disorders Headache Advisory Committees Machine Learning Algorithms Female Adult Male Humans
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Automated detection of pathologic white matter alterations in Alzheimer's disease using combined diffusivity and kurtosis method.

Psychiatry research. Neuroimaging Apr 12, 2017
Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) are important diffusion MRI techniques for detecting microstructure abnormities in diseases such as Alzheimer's. The advantages of DKI over DTI have been reported generally; however,...
White Matter Diffusion Tensor Imaging Leukoaraiosis Support Vector Machine Alzheimer Disease Female Reproducibility of Results Humans Aged Male Middle Aged
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S-SulfPred: A sensitive predictor to capture S-sulfenylation sites based on a resampling one-sided selection undersampling-synthetic minority oversampling technique.

Journal of theoretical biology Apr 12, 2017
Protein S-sulfenylation is a reversible post-translational modification involving covalent attachment of hydroxide to the thiol group of cysteine residues, which is involved in various biological processes including cell signaling, response to stress...
Support Vector Machine Protein Processing, Post-Translational Databases, Protein Cysteine Software Sequence Analysis, Protein
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Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators.

Waste management (New York, N.Y.) Apr 10, 2017
The heating values, particularly lower heating values of burning municipal solid waste are critically important parameters in operating circulating fluidized bed incineration systems. However, the heating values change widely and frequently, while th...
Solid Waste Incineration Support Vector Machine Heating Neural Networks, Computer
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Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds.

Journal of chemical information and modeling Apr 10, 2017
Support vector machine (SVM) modeling is one of the most popular machine learning approaches in chemoinformatics and drug design. The influence of training set composition and size on predictions currently is an underinvestigated issue in SVM modelin...
Drug Design Support Vector Machine
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Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.

BioMed research international Apr 9, 2017
The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redunda...
Ion Channels Support Vector Machine Models, Molecular Conotoxins
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Classification of High-Grade Glioma into Tumor and Nontumor Components Using Support Vector Machine.

AJNR. American journal of neuroradiology Apr 6, 2017
BACKGROUND AND PURPOSE: Current imaging assessment of high-grade brain tumors relies on the Response Assessment in Neuro-Oncology criteria, which measure gross volume of enhancing and nonenhancing lesions from conventional MRI sequences. These assess...
Male Middle Aged Adult Glioma Magnetic Resonance Spectroscopy Support Vector Machine Sensitivity and Specificity Magnetic Resonance Imaging Brain Neoplasms Neoplasm Grading Humans Female
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An improved method for identification of small non-coding RNAs in bacteria using support vector machine.

Scientific reports Apr 6, 2017
Bacterial small non-coding RNAs (sRNAs) are not translated into proteins, but act as functional RNAs. They are involved in diverse biological processes like virulence, stress response and quorum sensing. Several high-throughput techniques have enable...
Reproducibility of Results Computational Biology Support Vector Machine Machine Learning RNA, Small Untranslated Databases, Genetic Genome, Bacterial RNA, Bacterial
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New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Apr 5, 2017
In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300nm have been used for determination of antihistamine decongestant con...
Algorithms Reproducibility of Results Sensitivity and Specificity Support Vector Machine Least-Squares Analysis Neural Networks, Computer Histamine Antagonists Nasal Decongestants Linear Models Spectrophotometry
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