Assessing the risk of acute kidney injury associated with a four-drug regimen for heart failure: a ten-year real-world pharmacovigilance analysis based on FAERS events by Sen Lin (182597)

“…Disproportionality analysis and subgroup analysis were performed using four algorithms. Time-to-onset (TTO) analysis was used to assess the temporal risk patterns of ADE occurrence. …”

<b>Leveraging protected areas for dual goals of biodiversity conservation and zoonotic</b> <b>risk reduction</b> by Li Yang (13558573)

“…Each approach was run using both the Additive Benefit Function (ABF) and Core-Area Zonation (CAZ) algorithms.…”

LDD microtubule-based nucleation naturally results in a local microtubule density-dependent fraction of microtubule-based nucleation. by Marco Saltini (22209094)

“…<p>Fraction of microtubule-based nucleation as a function of (A) local and (B) global microtubule density (<i>ρ</i>) during a single simulation run. …”

An Extension of the Unified Skew-Normal Family of Distributions and its Application to Bayesian Binary Regression by Paolo Onorati (20461248)

“…For more general prior distributions, the proposed methodology is based on a simple Gibbs sampler algorithm. We also claim that, in the <math><mrow><mi>p</mi><mo>></mo><mi>n</mi></mrow></math> case, our proposal presents better performances—both in terms of mixing and accuracy—compared to the existing methods.…”

PEG neurons encoded more complex features than A1 neurons. by Shoutik Mukherjee (18626028)

“…The magnitudes of STRFs were computed (second column) and approximated by a probability distribution function for a Gaussian mixture model (GMM) fit with a boosting algorithm with large- and small-covariance Gaussian weak learners (third and fourth columns, respectively) and by <i>k</i> components of its singular value decomposition (fifth column). …”

Semiparametric Estimation for Error-Prone Partially Linear Single-Index Models by Li-Pang Chen (9747423)

“…To implement the proposed method efficiently, we develop a boosting algorithm that enables us to select variables and estimate the parameters without handling non-differentiable penalty functions. …”

Mean squared Error on all unseen data. by Edward Antonian (21453161)

“…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”

The notational conventions used in this paper. by Edward Antonian (21453161)

“…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”

Table 11_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Image 1_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.tif by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 7_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 4_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 6_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 10_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 1_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 8_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 9_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xls by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 2_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 5_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 3_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”