Data Sheet 1_Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods.docx by Ankita Lawarde (16544943)

“…Ensemble ML algorithms were trained and validated with stratified five-fold cross-validation for robust performance assessment across class distributions.…”

Seismic-Acoustic Dataset of Coastal Bryde’s Whales in the Beibu Gulf by Yue Wang (22435645)

Quantitative results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on DRPD dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on RFRB dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Main module structure. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on DRPD dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Architecture of MAR-YOLOv9. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Example images from four plant datasets. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on RFRB dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Controlling the Taxonomic Composition of Biological Information Storage in 16S rRNA by Kiara Reyes Gamas (22215078)

“…To achieve control over the organisms barcoded by cat-RNA, we created a program called Ribodesigner that uses input sets of rRNA sequences to create designs with varying specificities. We show how this algorithm can be used to identify designs that enable kingdom-wide barcoding, or selective barcoding of specific taxonomic groups within a kingdom. …”

Haplotype phylogenies and iSNV frequency trajectories across sampling time points (patient A and J). by Lele Zhao (532080)

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 .…”

Supplementary file 1_A real-world disproportionality analysis of FDA adverse event reporting system (FAERS) events for lecanemab.docx by Linlin Yan (4480570)

“…Using the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) algorithms, we conducted a comprehensive analysis of lecanemab-related AEs, restricting the analysis to AEs with the role code of primary suspect (PS).…”

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

Aluminum alloy industrial materials defect by Ying Han (20349093)

“…Finally, the organized defect dataset is detected and classified.</p><h2>Description of the data and file structure</h2><p dir="ltr">This is a project based on the YOLOv8 enhanced algorithm for aluminum defect classification and detection tasks.…”