AIMC Topic: Support Vector Machine

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3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI.

Neuroinformatics Jan 1, 2020
We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state function...
Humans Neural Networks, Computer Brain Middle Aged Female Deep Learning Support Vector Machine Imaging, Three-Dimensional Magnetic Resonance Imaging Alzheimer Disease Aged Mental Status and Dementia Tests Aged, 80 and over Male
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Prediction of S-nitrosylation sites by integrating support vector machines and random forest.

Molecular omics Dec 2, 2019
Cysteine S-nitrosylation is a type of reversible post-translational modification of proteins, which controls diverse biological processes. It is associated with redox-based cellular signaling to protect against oxidative stress. The identification of...
Software Protein Processing, Post-Translational Algorithms Cysteine Computational Biology Support Vector Machine
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Machine Learning Approach for Predicting Past Environmental Exposures From Molecular Profiling of Post-Exposure Human Serum Samples.

Journal of occupational and environmental medicine Dec 1, 2019
OBJECTIVE: To develop an approach for a retrospective analysis of post-exposure serum samples using diverse molecular profiles.
Tobacco Smoke Pollution Environmental Exposure Sex Factors Smoking Adolescent Support Vector Machine Age Factors Disease Models, Animal Mice, Inbred C57BL Cotinine Adult Animals Male Humans Biomarkers Machine Learning Female Young Adult
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Using convolutional neural networks to identify patient safety incident reports by type and severity.

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2019
OBJECTIVE: To evaluate the feasibility of a convolutional neural network (CNN) with word embedding to identify the type and severity of patient safety incident reports.
Humans Neural Networks, Computer Feasibility Studies Classification Medical Errors Patient Safety Support Vector Machine Risk Management
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iRNAD: a computational tool for identifying D modification sites in RNA sequence.

Bioinformatics (Oxford, England) Dec 1, 2019
MOTIVATION: Dihydrouridine (D) is a common RNA post-transcriptional modification found in eukaryotes, bacteria and a few archaea. The modification can promote the conformational flexibility of individual nucleotide bases. And its levels are increased...
Computational Biology Support Vector Machine Nucleotides RNA Base Sequence Reproducibility of Results
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Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy.

Journal of radiation research Nov 22, 2019
The purpose of this study was to predict the survival time of patients with malignant glioma after radiotherapy with high accuracy by considering additional clinical factors and optimize the prescription dose and treatment duration for individual pat...
Middle Aged Adolescent Time Factors Child Models, Theoretical Glioma Young Adult Adult Male Humans Female Machine Learning Aged Support Vector Machine Aged, 80 and over Survival Analysis Dose-Response Relationship, Radiation
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Medical knowledge infused convolutional neural networks for cohort selection in clinical trials.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: In this era of digitized health records, there has been a marked interest in using de-identified patient records for conducting various health related surveys. To assist in this research effort, we developed a novel clinical data represent...
Support Vector Machine Humans Natural Language Processing Data Mining Patient Selection Neural Networks, Computer Clinical Trials as Topic Deep Learning
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Simulation-assisted machine learning.

Bioinformatics (Oxford, England) Oct 15, 2019
MOTIVATION: In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, wh...
Support Vector Machine Machine Learning Software
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SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties.

Bioinformatics (Oxford, England) Oct 15, 2019
MOTIVATION: Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation and host-pathogen interactions. However, due to the poor ionization...
Humans Protein Processing, Post-Translational Animals Mice Glycoproteins Glycosylation Support Vector Machine
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Min3: Predict microRNA target gene using an improved binding-site representation method and support vector machine.

Journal of bioinformatics and computational biology Oct 1, 2019
MicroRNAs are single-stranded noncoding RNAs known to down-regulate target genes at the protein or mRNA level. Computational prediction of targets is essential for elucidating the detailed functions of microRNA. However, prediction specificity and se...
3' Untranslated Regions Databases, Genetic RNA, Messenger Gene Knockdown Techniques Humans MicroRNAs Binding Sites Models, Genetic HeLa Cells Software Gene Knockout Techniques Support Vector Machine Computational Biology
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