Giovanni Montana
Giovanni Montana
Professor of Data Science, University of Warwick
Verified email at - Homepage
Cited by
Cited by
Genome expansion and gene loss in powdery mildew fungi reveal tradeoffs in extreme parasitism
PD Spanu, JC Abbott, J Amselem, TA Burgis, DM Soanes, K Stüber, ...
Science 330 (6010), 1543-1546, 2010
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
JH Cole, RPK Poudel, D Tsagkrasoulis, MWA Caan, C Steves, ...
NeuroImage 163, 115-124, 2017
Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks
A Payan, G Montana
arXiv preprint arXiv:1502.02506, 2015
Deep neural networks for anatomical brain segmentation
A de Brebisson, G Montana
Proceedings of the IEEE conference on computer vision and pattern …, 2015
Brown and white adipose tissues: intrinsic differences in gene expression and response to cold exposure in mice
M Rosell, M Kaforou, A Frontini, A Okolo, YW Chan, E Nikolopoulou, ...
American Journal of Physiology-Endocrinology and Metabolism 306 (8), E945-E964, 2014
Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation
RPK Poudel, P Lamata, G Montana
Reconstruction, Segmentation, and Analysis of Medical Images: First …, 2017
Automated triaging of adult chest radiographs with deep artificial neural networks
M Annarumma, SJ Withey, RJ Bakewell, E Pesce, V Goh, G Montana
Radiology 291 (1), 196-202, 2019
Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach
M Vounou, TE Nichols, G Montana, ...
Neuroimage 53 (3), 1147-1159, 2010
Smchd1-dependent and-independent pathways determine developmental dynamics of CpG island methylation on the inactive X chromosome
AV Gendrel, A Apedaile, H Coker, A Termanis, I Zvetkova, J Godwin, ...
Developmental cell 23 (2), 265-279, 2012
Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks
PP Ypsilantis, M Siddique, HM Sohn, A Davies, G Cook, V Goh, ...
PloS one 10 (9), e0137036, 2015
Estimating time-varying brain connectivity networks from functional MRI time series
RP Monti, P Hellyer, D Sharp, R Leech, C Anagnostopoulos, G Montana
NeuroImage 103, 427-443, 2014
Statistical tests for admixture mapping with case-control and cases-only data
G Montana, JK Pritchard
The American Journal of Human Genetics 75 (5), 771-789, 2004
Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease
M Vounou, E Janoušová, R Wolz, JL Stein, PM Thompson, D Rueckert, ...
Elsevier, The Nederlands. ISSN, 2012
False positives in neuroimaging genetics using cluster-size inference
M Silver, G Montana, TE Nichols
Neuroimage 54 (2), 992-1000, 2010
Learning to detect chest radiographs containing pulmonary lesions using visual attention networks
E Pesce, SJ Withey, PP Ypsilantis, R Bakewell, V Goh, G Montana
Medical image analysis 53, 26-38, 2019
Whole-brain mapping of structural connectivity in infants reveals altered connection strength associated with growth and preterm birth
AS Pandit, E Robinson, P Aljabar, G Ball, IS Gousias, Z Wang, JV Hajnal, ...
Cerebral cortex 24 (9), 2324-2333, 2014
Community detection in multiplex networks using locally adaptive random walks
Z Kuncheva, G Montana
Proceedings of the 2015 IEEE/ACM International Conference on Advances in …, 2015
Modelling radiological language with bidirectional long short-term memory networks
S Cornegruta, R Bakewell, S Withey, G Montana
arXiv preprint arXiv:1609.08409, 2016
Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression
M Silver, E Janousova, X Hua, PM Thompson, G Montana, ...
NeuroImage 63 (3), 1681-1694, 2012
The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI
R Lorenz, RP Monti, IR Violante, C Anagnostopoulos, AA Faisal, ...
NeuroImage 129, 320-334, 2016
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