Unsupervised clustering of TCGA-KIRC subjects based on glucocorticoid signaling-related genes. by Yu Zhang (12946)

Features with significant variance discrepancies affecting classification accuracy. by Yifan He (3986123)

Correlation heatmap with significance. by Bo Dong (218172)

“…</p><p>Results</p><p>The health status of China’s migrant population is generally positive, though it is influenced by multiple factors, with varying degrees of significance. Among six distinct machine learning models, the Random Forest model demonstrated the best predictive performance. …”

Proportion of the increase (green) or decrease (red) in forest cover in areas with different fire recurrences in Africa, according to the Mann-Kendall test and statistically signif... by Oswaldo Maillard (20660661)

The mevalonate pathway (cholesterol biosynthesis): the place of action of statins, and their main effects. by Ewa Paszkowska (21246702)

Subjects:

Random Forest based prediction process. by Mohammed Majeed Hameed (14053535)

“…To address this, the study focuses on the Lotschental catchment in Switzerland, conducting a comprehensive comparison between deep learning and ensemble-based models. Given the significant autocorrelation in runoff time series data, which may hinder the evaluation of prediction models, a novel statistical method is employed to assess the effectiveness of forecasting models in detecting turning points in the runoff data. …”

Prediction models based on neural networks. by Jorge L. B. Araújo (20324246)

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The pro-inflammatory cytokines were mainly inhibited but anti-inflammatory genes were significantly activated in zebrafish embryos under PGRP5-KD conditions. by Xi Li (19208)

Geometric significance. by Liangliang Yang (3814120)

Best performance based on linear and quadratic model. by Alina Bazarova (17138191)

Mutation L35F in <i>murA</i> results in a significant decrease of PG amount in the <i>ΔdacA1</i> background. by Manuela Alvarado Obando (18340526)

Display of significance test results. by Qingchun Jiao (18388097)

“…In this study, the trend prediction model for the experimental process of fabric tear performance testing (BLTT-FT) based on the “bidirectional long- and short-term attention mechanism” is adopted. …”

Main effects of range analysis. by Zhengguo Zhu (2676715)

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Main characteristics of the FORCCHN model. by Junfang Zhao (299684)

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Main instruments and equipment for experiment. by Xin Kuang (17390340)

Metacognitive and predictive bias results. by Sarah M. Tashjian (5615795)

“…</b> Model parameters for metacognitive and predictive bias coefficients regressed against model-based control weighting (<i>ω</i>) and learning rate (<i>α</i>) parameters for each task variant. …”

Observed and predicted changes for the five metabolites significantly associated with the rs603424 SNP. by Juliette Cooke (11402543)

Table1_Identification of significant biomarkers for predicting the risk of bipolar disorder with arteriosclerosis based on integrative bioinformatics and machine learning.xlsx by Xiabing Zheng (19527181)

Image 1_Construction of a clinically significant prostate cancer risk prediction model based on traditional diagnostic methods.tif by Wen-Tong Ji (20457248)

“…Objectives<p>to construct a prediction model for clinically significant prostate cancer (csPCa) based on prostate-specific antigen (PSA) levels, digital rectal examination (DRE), and transrectal ultrasonography (TRUS).…”

The top five significant biological processes for each trait. by Hyo Jun Lee (9721560)