Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review
G Orru, W Pettersson-Yeo, AF Marquand… - Neuroscience & …, 2012 - Elsevier
Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical
and functional differences between healthy individuals and patients suffering a wide range …
and functional differences between healthy individuals and patients suffering a wide range …
The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception
MW Weiner, DP Veitch, PS Aisen, LA Beckett… - Alzheimer's & …, 2013 - Elsevier
Abstract The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing,
longitudinal, multicenter study designed to develop clinical, imaging, genetic, and …
longitudinal, multicenter study designed to develop clinical, imaging, genetic, and …
MRMD2. 0: a python tool for machine learning with feature ranking and reduction
S He, F Guo, Q Zou - Current Bioinformatics, 2020 - ingentaconnect.com
Aims: The study aims to find a way to reduce the dimensionality of the dataset. Background:
Dimensionality reduction is the key issue of the machine learning process. It does not only …
Dimensionality reduction is the key issue of the machine learning process. It does not only …
Latent representation learning for Alzheimer's disease diagnosis with incomplete multi-modality neuroimaging and genetic data
The fusion of complementary information contained in multi-modality data [eg, magnetic
resonance imaging (MRI), positron emission tomography (PET), and genetic data] has …
resonance imaging (MRI), positron emission tomography (PET), and genetic data] has …
Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy
Background Supervised machine learning has been proposed as a revolutionary approach
for identifying sensitive medical image biomarkers (or combination of them) allowing for …
for identifying sensitive medical image biomarkers (or combination of them) allowing for …
Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique
Motivation The prediction of protein–protein interaction (PPI) sites is a key to mutation
design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task …
design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task …
Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification
The high feature-dimension and low sample-size problem is one of the major challenges in
the study of computer-aided Alzheimer's disease (AD) diagnosis. To circumvent this …
the study of computer-aided Alzheimer's disease (AD) diagnosis. To circumvent this …
2014 Update of the Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception
MW Weiner, DP Veitch, PS Aisen, LA Beckett… - Alzheimer's & …, 2015 - Elsevier
Abstract The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing,
longitudinal, multicenter study designed to develop clinical, imaging, genetic, and …
longitudinal, multicenter study designed to develop clinical, imaging, genetic, and …
Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects
Dementia, a chronic and progressive cognitive declination of brain function caused by
disease or impairment, is becoming more prevalent due to the aging population. A major …
disease or impairment, is becoming more prevalent due to the aging population. A major …
A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis
Recent studies on AD/MCI diagnosis have shown that the tasks of identifying brain disease
and predicting clinical scores are highly related to each other. Furthermore, it has been …
and predicting clinical scores are highly related to each other. Furthermore, it has been …