ABIDE dataset subject demographics. by Alexandria M. Jensen (6494957)

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Linear kernel: Power and Type I error for cluster evaluation metric simulations. by Alexandria M. Jensen (6494957)

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Examples of between- and within-community Beta distributions. by Alexandria M. Jensen (6494957)

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P-values from analysis of ABIDE dataset for binary outcome of diagnostic group. by Alexandria M. Jensen (6494957)

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Examples of four simulated networks and adjacency matrices. by Alexandria M. Jensen (6494957)

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P-values from analysis of ABIDE dataset for continuous outcome of VIQ. by Alexandria M. Jensen (6494957)

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Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network by Rajesh Kumar Thangavel (20591164)

“…<p>Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. …”

The Blood–Brain Barrier (BBB) Score by Mayuri Gupta (1886839)

“…An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …”

The Blood–Brain Barrier (BBB) Score by Mayuri Gupta (1886839)

“…An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …”

The Blood–Brain Barrier (BBB) Score by Mayuri Gupta (1886839)

“…An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …”

The PS-VAE enables a detailed brain region-to-behavior mapping in the two-view mouse dataset. by Matthew R. Whiteway (11467528)

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Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

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Python implementation from Symplectic decomposition from submatrix determinants by Jason L. Pereira (11598632)

“…Python implementation of the algorithm and demonstration of how to use the functions.…”

Data_Sheet_1_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX by Raymond Salvador (813880)

“…On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. …”

Data_Sheet_2_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX by Raymond Salvador (813880)

“…On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. …”

Data_Sheet_3_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX by Raymond Salvador (813880)

“…On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. …”

Consistency of model-derived predictive patterns as a function of sample diversity. by Oualid Benkarim (9707986)

Classification accuracy and information transfer rate (ITR) as a function of the measurement set-up and the number of combined trials. by Dovilė Kurmanavičiūtė (21072549)

Confusion matrices of machine learning models trained on data oversampled by SMOTE and data trained with class weights given to the loss function. by Lucas Teoh (12878206)

Spatial characterization and cell-type-specific genes in mouse brain tissue. by Huamei Li (8815955)