Variations of loss functions versus epoch number. by Xueliang Guo (4797057)

IEM reconstruction of EEG recorded while subjects performed the 2-back task (<i>N</i> = 42, combining data from the pilot study and the preregistered experiment from Wan et al. [13... by Quan Wan (1329666)

Model-based decoding of learning. by Kevin S. Chen (9201767)

Data_Sheet_1_Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms.pdf by Alejandro O. Blenkmann (13914651)

“…This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. …”

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. …”

Table_1_An ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment.DOCX by Wenwen Xu (175846)

“…</p><p>Methods: The PubMed, Embase, and Web of Science databases were thoroughly searched to obtain neuroimaging articles on the amplitude of low-frequency fluctuation, regional homogeneity, and functional connectivity in sVCI patients. According to the activation likelihood estimation (ALE) algorithm, a meta-analysis based on coordinate and functional connectivity modeling was conducted.…”

Data_Sheet_1_Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large-Scale Neuronal Networks.PDF by Alessio Boschi (11276457)

“…<p>The identification of the organization principles on the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. …”

Data_Sheet_1_Potential of brain age in identifying early cognitive impairment in subcortical small-vessel disease patients.docx by Yachen Shi (6835871)

“…The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers.…”

Data_Sheet_1_Day-to-Day Test-Retest Reliability of EEG Profiles in Children With Autism Spectrum Disorder and Typical Development.DOCX by April R. Levin (4890703)

“…Here, using two resting EEGs collected a median of 6 days apart from 22 children with ASD and 25 typically developing (TD) controls during the Feasibility Visit of the Autism Biomarkers Consortium for Clinical Trials, we estimate test-retest reliability based on the characterization of the PSD shape in two ways: (1) Using the FOOOF algorithm we estimate six parameters (offset, slope, number of peaks, and amplitude, center frequency and bandwidth of the largest alpha peak) that characterize the shape of the EEG PSD; and (2) using nonparametric functional data analyses, we decompose the shape of the EEG PSD into a reduced set of basis functions that characterize individual power spectrum shapes. …”

Table_1_Distinguishing Glioblastoma Subtypes by Methylation Signatures.XLSX by Yu-Hang Zhang (190330)

“…Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as CXCR4, TBX18, SP5, and TMEM22, and enriched in relevant biological functions related to GBM classification (e.g., subtype-specific functions). …”

Table_2_Distinguishing Glioblastoma Subtypes by Methylation Signatures.XLSX by Yu-Hang Zhang (190330)

“…Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as CXCR4, TBX18, SP5, and TMEM22, and enriched in relevant biological functions related to GBM classification (e.g., subtype-specific functions). …”

Table_3_Distinguishing Glioblastoma Subtypes by Methylation Signatures.XLSX by Yu-Hang Zhang (190330)

“…Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as CXCR4, TBX18, SP5, and TMEM22, and enriched in relevant biological functions related to GBM classification (e.g., subtype-specific functions). …”

Table_4_Distinguishing Glioblastoma Subtypes by Methylation Signatures.XLSX by Yu-Hang Zhang (190330)

“…Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as CXCR4, TBX18, SP5, and TMEM22, and enriched in relevant biological functions related to GBM classification (e.g., subtype-specific functions). …”

Data_Sheet_8_Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease.xlsx by Yi-Jie He (14162616)

“…Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed.…”

Data_Sheet_3_Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease.xlsx by Yi-Jie He (14162616)

“…Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed.…”

Table_1_Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease.XLSX by Yi-Jie He (14162616)

“…Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed.…”

Image_1_Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease.JPEG by Yi-Jie He (14162616)

“…Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed.…”

Image_2_Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease.JPEG by Yi-Jie He (14162616)

“…Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed.…”