Performance assessment of Cell2Spatial on real and simulated mouse brain spatial transcriptomics (ST) data. by Huamei Li (8815955)

Data_Sheet_1_Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals.docx by Nadia Youssef (11586382)

“…The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). …”

Data_Sheet_1_Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals.docx by Nadia Youssef (11586382)

“…The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). …”

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”

Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF by Minhua Guo (18392646)

Image_2_Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels.TIFF by Hyeokjin Kwon (9314486)

“…We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. …”

Image_3_Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels.TIFF by Hyeokjin Kwon (9314486)

“…We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. …”

Image_1_Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels.TIFF by Hyeokjin Kwon (9314486)

“…We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. …”

Improved A* algorithm flowchart. by Peiying Li (797714)

“…Specifically, A-star is optimized by evaluation function, sub node selection mode and path smoothness, and fuzzy control is introduced to optimize the sliding window algorithm. …”

Codes of the flow distance algorithm "D∞-TLI" and the width function algorithm "MEB" by Pengfei Wu (11627371)

“…<p>The JAVA codes of the flow distance algorithm "D∞-TLI" and the width function algorithm "MEB" are provided. …”

Diagram illustrating the main processes of the functional-structural rice model and interaction with the MPSO algorithm. by Lifeng Xu (778021)

Data_Sheet_1_Nrf2 functions as a pyroptosis-related mediator in traumatic brain injury and is correlated with cytokines and disease severity: a bioinformatics analysis and retrospe... by Gengshui Zhao (14097285)

Performance as a function of the number of algorithm executions for the full-sized matrix design. by Jörg Felder (5848541)

“…<p>Performance as a function of the number of algorithm executions for the full-sized matrix design.…”

Data_Sheet_1_Developmental Changes in Dynamic Functional Connectivity From Childhood Into Adolescence.pdf by Mónica López-Vicente (3980462)

“…Our objective was to characterize the longitudinal developmental changes in dynamic functional connectivity in a population-based pediatric sample. …”

Convergence curves of the voltage profile improvement by using (a) the HFPSO algorithm and (b) the PSO algorithm. by Abdullah Khan (377292)

Convergence curves of voltage stability enhancement based on (a) the HFPSO algorithm and (b) the PSO algorithm. by Abdullah Khan (377292)

Image_2_Distinct Brain Dynamic Functional Connectivity Patterns in Schizophrenia Patients With and Without Auditory Verbal Hallucinations.JPEG by Yao Zhang (134381)

“…In this study, 25 Schizophrenia patients with AVHs (AVHs group, 23.2 ± 5.35 years), 52 Schizophrenia patients without AVHs (non-AVHs group, 25.79 ± 5.63 years) and 28 healthy subjects (NC group, 26.14 ± 5.45 years) were enrolled. Dynamic functional connectivity was studied with a sliding-window method and functional connectivity states were then obtained with the k-means clustering algorithm in the three groups. …”

Image_3_Distinct Brain Dynamic Functional Connectivity Patterns in Schizophrenia Patients With and Without Auditory Verbal Hallucinations.JPEG by Yao Zhang (134381)

“…In this study, 25 Schizophrenia patients with AVHs (AVHs group, 23.2 ± 5.35 years), 52 Schizophrenia patients without AVHs (non-AVHs group, 25.79 ± 5.63 years) and 28 healthy subjects (NC group, 26.14 ± 5.45 years) were enrolled. Dynamic functional connectivity was studied with a sliding-window method and functional connectivity states were then obtained with the k-means clustering algorithm in the three groups. …”

Image_1_Distinct Brain Dynamic Functional Connectivity Patterns in Schizophrenia Patients With and Without Auditory Verbal Hallucinations.JPEG by Yao Zhang (134381)

“…In this study, 25 Schizophrenia patients with AVHs (AVHs group, 23.2 ± 5.35 years), 52 Schizophrenia patients without AVHs (non-AVHs group, 25.79 ± 5.63 years) and 28 healthy subjects (NC group, 26.14 ± 5.45 years) were enrolled. Dynamic functional connectivity was studied with a sliding-window method and functional connectivity states were then obtained with the k-means clustering algorithm in the three groups. …”

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. …”