An exemplar procedure for activity flow modeling based on the direct and shortest paths at a network density of 15%. by Zhengdong Wang (35299)

Alphaviruses nsP4 protein alignment. by Marie-France Martin (12118879)

Learning alters pirouette decision and behavioral noise. by Kevin S. Chen (9201767)

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

Significantly enriched Gene Ontology (GO) terms among the 512 genes showing significant results in the all ancestry TWAS. Enrichment analyses were conducted using the multi-query i... by Jenna C. Carlson (3950024)

Schematic diagram of the G4Hunter scanning principle. by Rongxin Zhang (1618159)

dPAW captures learned olfactory navigation in worms. by Kevin S. Chen (9201767)

VEP annotation of the aSNPs listed in S1 Table. by Rongxin Zhang (1618159)

“…<div><p>G-quadruplexes (G4s) are nucleic acid secondary structures with important regulatory functions. Single-nucleotide variants (SNVs), one of the most common forms of genetic variation, can potentially impact the formation of G4 structures if they occur within G4 regions. …”

G4SNVHunter workflow for identifying variants that affect G4 formation. by Rongxin Zhang (1618159)

“…Subsequently, the impact of the variants on the formation potential of the identified G4s will be assessed based on the G4Hunter algorithm (Middle panel). Finally, candidate variants can be filtered and visualized using functions provided by G4SNVHunter to screen out those that can potentially disrupt the formation of G4 structures (Right panel). …”

Supplementary file 1_Identifying pyroptosis-hub genes and immune infiltration in neonatal hypoxic-ischemic brain injury.docx by Chi Qin (10001651)

“…Immune infiltration analysis revealed that, compared to the control group, the HIBD group exhibited higher levels of innate immune cells (e.g., macrophages, M0 cells, and dendritic cells) and adaptive immune cells (e.g., CD8 naïve T cells, CD4 follicular helper T cells, and Th1 cells). The ssGSEA algorithm results indicated differences in 25 types of immune cells and 10 immune functions. …”

S1 Dataset - by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Statistical tests of ACC on the random network. by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Parameters in the experiment. by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Statistical tests of APL on the random network. by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Statistical tests of ACC on the regular network. by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Statistical tests of APL on the regular network. by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Data information and schematic diagram of the overlapping modular architecture based on the edge-centric module detection. by Tianyuan Lei (19683861)

“…<b>(B)</b> (i) Traditional brain functional connectivity network. In this network, each node denotes a brain region of interest, and each link denotes the interregional functional connectivity. …”

Table 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.xlsx by Peng Liu (120506)

“…Subsequently, Single sample GSEA (ssGSEA) was executed to explore the relationships within immune cell infiltration, UC subtypes, and key anoikis-DEGs. …”

Image 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Subsequently, Single sample GSEA (ssGSEA) was executed to explore the relationships within immune cell infiltration, UC subtypes, and key anoikis-DEGs. …”

Image 4_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Subsequently, Single sample GSEA (ssGSEA) was executed to explore the relationships within immune cell infiltration, UC subtypes, and key anoikis-DEGs. …”