Data Sheet 4_CD44 knockdown alters miRNA expression and their target genes in colon cancer.pdf by Diana Maltseva (11678641)

“…Introduction<p>Metastasis formation poses a significant challenge to oncologists, as it severely limits the survival of colorectal cancer (CRC) patients. …”

Data Sheet 5_CD44 knockdown alters miRNA expression and their target genes in colon cancer.zip by Diana Maltseva (11678641)

“…Introduction<p>Metastasis formation poses a significant challenge to oncologists, as it severely limits the survival of colorectal cancer (CRC) patients. …”

Data Sheet 1_CD44 knockdown alters miRNA expression and their target genes in colon cancer.pdf by Diana Maltseva (11678641)

“…Introduction<p>Metastasis formation poses a significant challenge to oncologists, as it severely limits the survival of colorectal cancer (CRC) patients. …”

Data Sheet 3_CD44 knockdown alters miRNA expression and their target genes in colon cancer.pdf by Diana Maltseva (11678641)

“…Introduction<p>Metastasis formation poses a significant challenge to oncologists, as it severely limits the survival of colorectal cancer (CRC) patients. …”

Image 1_Ketogenic diet–induced changes in methylation status and neuropeptide signaling: relationships between S-adenosylmethionine (AdoMet), orexin-A, and metabolic health.pdf by Laura Mosca (704904)

“…Circulating AdoMet (−75.7%) and Orexin-A (−7.2%) levels also decreased significantly. …”

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

The model in which the PoFbx23 is required in activating PoAce1 to control the expression of target genes. by Zhongjiao Liu (11877464)

Subjects:

Data that underlies the TAP-MS of three Ace1-TAP samples. by Zhongjiao Liu (11877464)

Subjects:

Data that underlies the ChIP-qPCR in this paper. by Zhongjiao Liu (11877464)

Subjects:

The results of TAP-MS using transcription factor PoAce1 as bait. by Zhongjiao Liu (11877464)

Subjects:

The top proteins interacting with PoAce1 identified through TAP-MS. by Zhongjiao Liu (11877464)

Subjects:

Ubiquitination of PoAce1 and effect of proteasome inhibitor MG132 on PoAce1. by Zhongjiao Liu (11877464)

Subjects:

The result of TAP-MS using PoAce1 as the bait. by Zhongjiao Liu (11877464)

Subjects:

The phenotype of the Δ<i>ace1</i> and (hemi)cellulase production of various mutants. by Zhongjiao Liu (11877464)

Subjects:

PoAce1 enrichment in the specific regions of (hemi)cellulase genes assayed by ChIP-qPCR. by Zhongjiao Liu (11877464)

Subjects:

Saturation analysis of transcriptome data for the WT and Δ<i>fbx23</i>. by Zhongjiao Liu (11877464)

Subjects: