Table 6_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

The gene primer sequences. by Meili Liu (327309)

“…</p><p>Methods</p><p>Public gene expression datasets were analyzed to identify differentially expressed genes (DEGs) common to both RA and UC. Functional enrichment and immune infiltration analyses revealed dysregulated pathways. …”

All models comparison table. by Meili Liu (327309)

“…</p><p>Methods</p><p>Public gene expression datasets were analyzed to identify differentially expressed genes (DEGs) common to both RA and UC. Functional enrichment and immune infiltration analyses revealed dysregulated pathways. …”

Table 2_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.xlsx by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Image 3_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.tif by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Table 4_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.xlsx by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Table 1_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.xlsx by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Image 4_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.tif by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Table 3_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.xlsx by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Image 6_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.tif by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Image 1_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.tif by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Image 2_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.tif by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Image 5_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.tif by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Supplementary file 1_Hamiltonian formulations of centroid-based clustering.pdf by Myeonghwan Seong (21159605)

“…However, defining similarity is often ambiguous, making it challenging to determine the most appropriate objective function for a given dataset. Traditional clustering methods, such as the k-means algorithm and weighted maximum k-cut, focus on specific objectives—typically relying on average or pairwise characteristics of the data—leading to performance that is highly data-dependent. …”

Bayesian Clustering via Fusing of Localized Densities by Alexander Dombowsky (20289372)

“…The data are then clustered by minimizing the expectation of a clustering loss function that favors similarity to the component labels. …”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”

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. …”

Table 5_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 2_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. …”