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Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.

M. Rahaman, J. Chen, Z. Fu, N. Lewis, A. Iraji, and V. Calhoun. EMBC, page 3267-3272. IEEE, (2021)

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Md Abdur Rahim

Abdur Rahman

Hasibur Rahaman

Ayjamal Abdurahman

Makarius Victor Mdemu

Other publications of authors with the same name

Shared sets of correlated polygenic risk scores and voxel-wise grey matter across multiple traits identified via bi-clustering.M. Rahaman, A. Rodrigue, D. Glahn, J. Turner, and V. Calhoun. EMBC, page 2201-2206. IEEE, (2021)A deep generative multimodal imaging genomics framework for Alzheimer's disease prediction.G. Dolci, M. Rahaman, J. Chen, K. Duan, Z. Fu, A. Abrol, G. Menegaz, and V. Calhoun. BIBE, page 41-44. IEEE, (2022)N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia.M. Rahaman, D. Mathalon, H. Lee, W. Jiang, B. Mueller, O. Andreassen, I. Agartz, S. Sponheim, A. Mayer, J. Stephen and 12 other author(s). IEEE Trans. Biomed. Eng., 67 (1): 110-121 (2020)Longitudinal Whole-Brain Functional Network Change Patterns Over A Two-Year Period In The ABCD Data.R. Saha, D. Saha, M. Rahaman, Z. Fu, and V. Calhoun. ISBI, page 1-4. IEEE, (2022)Statelets: A Novel Multi-Dimensional State-Shape Representation Of Brain Functional Connectivity Dynamics.M. Rahaman, E. Damaraju, D. Saha, V. Calhoun, and S. Plis. ISBI, page 1822-1826. IEEE, (2021)Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals.M. Rahaman, E. Damaraju, J. Turner, T. van Erp, D. Mathalon, J. Vaidya, B. Mueller, G. Pearlson, and V. Calhoun. Brain Connect., 12 (1): 61-73 (2022)Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.M. Rahaman, J. Chen, Z. Fu, N. Lewis, A. Iraji, and V. Calhoun. EMBC, page 3267-3272. IEEE, (2021)Two-Dimensional Attentive Fusion for Multi-Modal Learning of Neuroimaging and Genomics Data.M. Rahaman, Y. Garg, A. Iraji, Z. Fu, J. Chen, and V. Calhoun. MLSP, page 1-6. IEEE, (2022)Deep Generative Transfer Learning Predicts Conversion To Alzheimer'S Disease From Neuroimaging Genomics Data.G. Dolci, M. Rahaman, I. Galazzo, F. Cruciani, A. Abrol, J. Chen, Z. Fu, K. Duan, G. Menegaz, and V. Calhoun. ICASSP Workshops, page 1-5. IEEE, (2023)A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age.R. Saha, D. Saha, M. Rahaman, Z. Fu, J. Liu, and V. Calhoun. Brain Connect., 14 (2): 130-140 (2024)

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Disambiguation of "Rahaman, Md Abdur"

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Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.

Please choose a person to relate this publication to

Md Abdur Rahim

Abdur Rahman

Hasibur Rahaman

Ayjamal Abdurahman

Makarius Victor Mdemu

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Rahaman, Md Abdur"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.

Please choose a person to relate this publication to

Md Abdur Rahim

Abdur Rahman

Hasibur Rahaman

Ayjamal Abdurahman

Makarius Victor Mdemu

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.