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PLoS ONE, ISSN 1932-6203, 02/2016, Volume 11, Issue 2, p. e0148195
Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets.... 
REGRESSION | BILIRUBIN | DIAGNOSIS | CELL DISTRIBUTION WIDTH | ANTIOXIDANT | INFLAMMATION | MULTIDISCIPLINARY SCIENCES | DISEASE | CLASSIFICATION | RISK | MULTIPLE IMPUTATION | Erythrocyte Indices - physiology | Blood Glucose - physiology | Multivariate Analysis | Nutrition Surveys | Depressive Disorder - pathology | Humans | Middle Aged | Biomarkers - analysis | Logistic Models | Male | Biomarkers - blood | Machine Learning | Young Adult | Bilirubin - blood | Algorithms | Adolescent | Aged, 80 and over | Adult | Female | Aged | Depressive Disorder - psychology | Depressive Disorder - blood | Data Mining - methods | Usage | Depression, Mental | Machine learning | Genetic aspects | Research | Diagnosis | Data mining | Biological markers | Physical activity | Methodology | Control methods | Identification methods | Mental health | Alcohol | Glucose | Bilirubin | Mental depression | Moderators | Epidemiology | Antioxidants | Learning | Body mass index | Regression models | Bioindicators | Mathematical models | Statistical modelling | Medical research | Poverty | Statistical analysis | Nutrition | Mortality | Amalgamation | Data processing | Inflammation | Regression analysis | Preventive medicine | Studies | Missing data | Learning algorithms | Hospitals | Body mass | Food security | Biomarkers | Diabetes | Alzheimers disease | Psychiatry | Artificial intelligence | Open access | Smoking | Cancer
Journal Article
Journal Article
Journal Article
NeuroImage, ISSN 1053-8119, 02/2017, Volume 147, pp. 736 - 745
Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However,... 
Autism spectrum disorders | Resting-state fMRI | Biomarkers | Data heterogeneity | Connectome | Data pipelines | NETWORK | FUNCTIONAL CONNECTIVITY | SPECTRUM DISORDERS | NEUROSCIENCES | NEUROIMAGING | PREDICTION | HUMAN CEREBRAL-CORTEX | BRAIN CONNECTIVITY | CONNECTOMES | SUBJECT | SAMPLE-SIZE | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | FMRI | Reproducibility of Results | Image Processing, Computer-Assisted - standards | Humans | Datasets as Topic - standards | Magnetic Resonance Imaging - methods | Connectome - standards | Image Processing, Computer-Assisted - methods | Autism Spectrum Disorder - diagnostic imaging | Cerebral Cortex - diagnostic imaging | Cerebral Cortex - physiopathology | Multicenter Studies as Topic - methods | Magnetic Resonance Imaging - standards | Adolescent | Connectome - methods | Adult | Multicenter Studies as Topic - standards | Child | Biological markers | Autism | Nervous system diseases | Data exchange | Attention deficit disorder | Neuroimaging | Brain | Mental disorders | Pipelines | Disorders | Schizophrenia | Complexity | Datasets | Heterogeneity | Classification | Functional magnetic resonance imaging | Bioindicators | Atlases | Medical imaging | Feasibility studies | Brain mapping | Data processing | Studies | Brain research | Magnetic resonance imaging | Neural networks | Data sets | Diagnostic software | Predictions | Connectivity | Diagnostic systems | Neuroscience | Medical Imaging | Cognitive science | Statistics | Machine Learning | Computer Science
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