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BMC Proceedings
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December 17, 2016
Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data
Elizabeth Held, Joshua Cape, Nathan Tintle
Schizophrenia Research
|
March 25, 2023
Global network disorganization underlying psychosis high risk states
Konasale Prasad, Jonathan Rubin, Satish Iyengar, et al.
Brain Connectivity
|
May 11, 2023
Threshold Selection for Brain Connectomes
Nicholas Theis, Jonathan Rubin, Joshua Cape, et al.
Neural Networks : the Official Journal of the International Neural Network Society
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October 9, 2024
Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes
Madison Lewis, Wenlong Jiang, Nicholas D Theis, et al.
Journal of Machine Learning Research : JMLR
|
October 15, 2021
Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace
Jesús Arroyo, Avanti Athreya, Joshua Cape, et al.
Biorxiv : the Preprint Server for Biology
|
February 8, 2024
Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study
Nicholas Theis, Jyotika Bahuguna, Jonathan E Rubin, et al.
Schizophrenia Research
|
December 13, 2021
Structural covariance networks in schizophrenia: A systematic review Part II
Konasale Prasad, Jonathan Rubin, Anirban Mitra, et al.
Schizophrenia Research
|
December 15, 2021
Structural covariance networks in schizophrenia: A systematic review Part I
Konasale Prasad, Jonathan Rubin, Anirban Mitra, et al.
Biorxiv : the Preprint Server for Biology
|
February 6, 2026
Maximum entropy model reveals frequent brain state switching in a multiversal brain function analysis in early psychoses
Nicholas Theis, Jonathan Rubin, Ella O'Rourke, et al.
Proceedings of the National Academy of Sciences of the United States of America
|
March 10, 2019
On a two-truths phenomenon in spectral graph clustering
Carey E Priebe, Youngser Park, Joshua T Vogelstein, et al.
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Search research articles
Search
Showing results (1-10 of 11) with videos related to
Sort By:
Page
of 2
BMC Proceedings
|
December 17, 2016
Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data
Elizabeth Held, Joshua Cape, Nathan Tintle
Schizophrenia Research
|
March 25, 2023
Global network disorganization underlying psychosis high risk states
Konasale Prasad, Jonathan Rubin, Satish Iyengar, et al.
Brain Connectivity
|
May 11, 2023
Threshold Selection for Brain Connectomes
Nicholas Theis, Jonathan Rubin, Joshua Cape, et al.
Neural Networks : the Official Journal of the International Neural Network Society
|
October 9, 2024
Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes
Madison Lewis, Wenlong Jiang, Nicholas D Theis, et al.
Journal of Machine Learning Research : JMLR
|
October 15, 2021
Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace
Jesús Arroyo, Avanti Athreya, Joshua Cape, et al.
Biorxiv : the Preprint Server for Biology
|
February 8, 2024
Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study
Nicholas Theis, Jyotika Bahuguna, Jonathan E Rubin, et al.
Schizophrenia Research
|
December 13, 2021
Structural covariance networks in schizophrenia: A systematic review Part II
Konasale Prasad, Jonathan Rubin, Anirban Mitra, et al.
Schizophrenia Research
|
December 15, 2021
Structural covariance networks in schizophrenia: A systematic review Part I
Konasale Prasad, Jonathan Rubin, Anirban Mitra, et al.
Biorxiv : the Preprint Server for Biology
|
February 6, 2026
Maximum entropy model reveals frequent brain state switching in a multiversal brain function analysis in early psychoses
Nicholas Theis, Jonathan Rubin, Ella O'Rourke, et al.
Proceedings of the National Academy of Sciences of the United States of America
|
March 10, 2019
On a two-truths phenomenon in spectral graph clustering
Carey E Priebe, Youngser Park, Joshua T Vogelstein, et al.
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of 2