Example display of the only tab, <i>Text-mining</i>, in the <i>Examine</i> section of the application for extraction and display of information from free-text wildlife necropsy dat... by Stefan Saverimuttu (20882325)

Study workflow. by Sayantan Kumar (20208872)

Table 1_Analysis and validation of characteristic genes in RNA sequencing datasets from heart failure patients based on multiple algorithms.docx by Yuxuan Li (1674307)

Image 1_Analysis and validation of characteristic genes in RNA sequencing datasets from heart failure patients based on multiple algorithms.tif by Yuxuan Li (1674307)

Identifying the groups becomes harder when the degradation chain is long, especially for groups catalyzing upstream reactions. by Yuanchen Zhao (12905580)

“…<p>(A) The panel shows the ability of the Metropolis algorithm to recover the true functional groups within a linear degradation chain with <i>N</i> = 4 metabolites. …”

Data Sheet 2_Machine learning integrates region-specific microbial signatures to distinguish geographically adjacent populations within a province.xlsx by Li Luo (149019)

“…To obtain the optimal model that can distinguish geographically close populations, three machine learning (ML) algorithms based on microbiota or functions were employed.…”

Data Sheet 1_Machine learning integrates region-specific microbial signatures to distinguish geographically adjacent populations within a province.docx by Li Luo (149019)

“…To obtain the optimal model that can distinguish geographically close populations, three machine learning (ML) algorithms based on microbiota or functions were employed.…”

Comparison with baseline clustering techniques. by Sayantan Kumar (20208872)

A Multi-Pathology Ballistocardiogram Dataset for Cardiac Function Monitoring and Arrhythmia Assessment by Jing Zhan (20716673)

“…There are 153 subfolders within a primary directory named "data," derived from 85 participants. …”

Screening of candidate hub genes, functional enrichment analysis, and construction of the PPI network. by Changqi Du (21696596)

Supporting data for “The role of forest composition heterogeneity on temperate ecosystem carbon dynamic under climate change" by Ziyu Lin (9151064)

“…The process includes (1) harmonizing Landsat 5, 7, 8, and Sentinel-2 data using the HLS algorithm, and (2) filling temporal gaps with an optimized object-based STARFM fusion algorithm. …”

Raw results. by Mahmood A. Jumaah (22272371)

Schematic representation of the coarse structure of serial interval data. by Oswaldo Gressani (13933254)

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”

Table 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…</p>Methods<p>We integrated placental transcriptomic data from two datasets (GSE75010, GSE10588) to systematically investigate ribosome biogenesis dysregulation in preeclampsia. Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. …”

Table 4_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…</p>Methods<p>We integrated placental transcriptomic data from two datasets (GSE75010, GSE10588) to systematically investigate ribosome biogenesis dysregulation in preeclampsia. Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. …”