Frequency trajectories of iSNVs that fluctuated (Top) and their positions on the genome (Bottom) (patients A-I). by Lele Zhao (532080)

Supplementary Table S7: All Results of Structural Alignment between Selected Rice Gene Group and Human using the Foldseek by Sora Yonezawa (14618045)

“…</p><p dir="ltr">【Column Name Description】<br>"From" column: rice (<i>Oryza sativa subsp. japonica</i>) gene ID</p><p dir="ltr">"HN5": HN-score (gene expression pattern metrics)<br>"UniProt Accession": rice structure prediction accession (UniProt accession)<br>"foldseek hit": human structure prediction accession (UniProt accession)<br></p><p><br></p><p dir="ltr">Table S7-1: <b>foldseek_output_uniprot_rice_up_9606_modified</b>: Results of structural alignment of rice upregulated gene group and human using Foldseek (3Di + AA Goto-Smith-waterman algorithm)</p><p dir="ltr">Table S7-2: <b>foldseek_output_uniprot_rice_up_9606_tmalign</b><b>_modified</b>: Results of structural alignment of rice upregulated gene group and human using Foldseek (Foldseek-TM)</p><p dir="ltr">Table S7-3: <b>foldseek_output_uniprot_rice_down_9606</b><b>_modified</b>: Results of structural alignment of rice downregulated gene group and human using Foldseek (3Di + AA Goto-Smith-waterman algorithm)</p><p dir="ltr">Table S7-4: <b>foldseek_output_uniprot_rice_down_9606_tmalign</b><b>_modified</b>: Results of structural alignment of rice downregulated gene group and human using Foldseek (Foldseek-TM)</p><p dir="ltr"><b>List of execution commands (using Common Workflow Language (CWL), the workflow language):</b></p><p dir="ltr">Note: You can use files from the following repositories: <a href="https://github.com/yonesora56/HS_rice_analysis" rel="noreferrer" target="_blank">https://github.com/yonesora56/HS_rice_analysis</a></p><p dir="ltr"><b>(1) Index creation using the </b><code><strong>foldseek databases</strong></code><b> command (network access required)</b></p><h4><code>cwltool --debug .…”

Enhancing the Robustness of Vehicle Re-Identification in Intelligent Transportation Systems by Mei Qiu (21081170)

Exploring the spatial architecture of the human dorsolateral prefrontal cortex (DPLFC). by Huamei Li (8815955)

Exploring the spatial architecture of the human thymus. by Huamei Li (8815955)

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

Cell2Spatial enables versatile applications across various analytical domains. by Huamei Li (8815955)

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

Comparative analysis of spatial architectures and cellular proportions in Lung, Breast, and Intestinal tissues recovered by different mapping tools. by Huamei Li (8815955)

Investigating the spatial architecture of the mouse kidney. by Huamei Li (8815955)

Spatial transcriptomics (ST) and single-cell atlases of multiple human tissues. by Huamei Li (8815955)

Performance assessment of Cell2Spatial in real ST data from various tissue types. by Huamei Li (8815955)

Evaluation of spatial transcriptomics (ST) tools across various metrics and conditions. by Huamei Li (8815955)

Cut points prediction. by Loïc Omnes (21181968)

Contains the edge lists and vertex attributes for the nine networks used in this study as excel files. by Helal El-Zaatari (20442243)

Field photo diagnostic imaging by Hirokuni Miyamoto (16318251)

“…</p><p dir="ltr">Data_file_fish_new.xlsx: Whole raw data</p><p dir="ltr">utils.py (causion: same to original command name "utils.py" for "<code>machine_learning.py"</code>in the GitHub website "<a href="https://github.com/hmiyamoto2000/program_tai1_Texture_main/tree/v1.0.6" target="_blank">program_tai1_Texture_main</a><a href="https://github.com/hmiyamoto2000/program_tai1_Texture_main/tree/v2.0.1" target="_blank">(https://github.com/hmiyamoto2000/program_tai1_Texture_main/tree/v2.0.1</a>)": optional command for feature selection based on machine learning algorithms (The new command "utils.py" fix the random seed to control the randomness and reduce uncertainty in the training process to improve reproducibility and stability of the results.)…”

Kleptographic Attacks Against Implicit Rejection by Antoine Joux (17288464)

LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery by M RN (9866504)

DataSheet1_DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological network.PDF by Minwoo Pak (6842618)

“…The source code for DGDRP can be found at: https://github.com/minwoopak/heteronet.…”

LitiumLIC BscScan Verified Source (2025-11-13) by Investlitiumlic Oy (22614180)