<b>Protein-Protein interaction (PPI) network of differentially acetylated proteins</b><b> by Aspirin during differentiation of THP-1 cell towards macrophage</b> by Li Xing (21105170)

“…The protein-protein interaction (PPI) networks were generated using STRING (H. sapiens; confidence score > 0.7) and visualized in Cytoscape 3.2.1. to elucidate how Aspirin-driven acetylated proteins functionally coordinate within cellular systems. The PPI network was further analyzed to identify densely interconnected functional clusters/modules using topological clustering algorithms. …”

Table 1_Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis.docx by Chengyuan Xu (4174936)

“…Clinical tissue analyses and functional assays confirmed that PYGM is upregulated in rectal cancer and promotes tumor cell proliferation, migration, and M2 macrophage polarization.…”

Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Data Sheet 1_Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine lear... by Qianqian Liang (1521904)

“…</p>Results<p>Cross-analysis identified 186 shared DEGs between diabetes and COPD patients. Functional enrichment results demonstrate that metabolic and immune-related pathways are common features altered in both diabetes and COPD patients. …”

Data Sheet 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.csv by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Data Sheet 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.csv by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 2_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Image 3_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Image 1_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

<b>NanoNeuroBot: Beyond Healing, Toward Human Connection</b> by ahmed hossam (21420446)

“…The research also explores development timelines, projected budgets, ethical concerns, and translational potential in both clinical and commercial applications.</p>…”

Image 4_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation by Ryo Tamura (1957942)

“…We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. …”

Image 2_Spatial transcriptomic analysis of 4NQO-induced tongue cancer revealed cellular lineage diversity and evolutionary trajectory.tif by Feng Liu (72874)

“…</p>Methods<p>We leveraged artificial intelligence (AI) algorithms and spatial transcriptomic sequencing to meticulously characterize the spatial and temporal evolution of 4-nitroquinoline-1-oxide (4NQO)-induced tongue carcinogenesis and intratumor heterogeneity.…”