The drug sensitivity predicted by oncopredict in the TCGA-KIRC cohort. by Yu Zhang (12946)

Subjects:

Network pharmacology analysis predicted the targets of NS on atherosclerosis. by Yihua Wang (491766)

Fig 6 - by Teagan McMahon (20108036)

The proportional change in maximum height for the five study groups. by Teagan McMahon (20108036)

Prediction formulas for fitting different indicators. by Yi Wang (32470)

Subjects:

ROC Results Predicted for Out of the Sample. by Fangyi Yang (18867853)

“…The model estimation results indicate that passenger patience significantly influences drop-off decisions. All models—static, dynamic, and Cox proportional hazards—achieved prediction accuracies exceeding 70%, with the dynamic model outperforming others when ample sample data is available. …”

SARIMA predicts season components. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Table 1_Predicted habitat and areas of ecological significance shifts of top predators in the South Shetland Islands under climate changes.docx by Yufei Dai (621612)

“…Key findings include: 1) The spatial distribution of top predators in the South Shetland Islands is predominantly influenced by bathymetry, mixed layer thickness (Mlotst), and sea ice concentration (SIC). 2) The highly suitable habitats for the Gentoo Penguin (Pygoscelis papua), Humpback Whale (Megaptera novaeangliae), and Light-mantled Albatross (Phoebetria palpebrata) are expected to decrease under various future scenarios. 3) The AES in the South Shetland Islands are predominantly concentrated along the southern coastal areas. 4) The AES on the western side of the islands are projected to undergo significant fluctuations, while those on the eastern side are likely to exhibit minor changes, with the central area remaining relatively stable.…”

Gene profiles of leaf and root endophytic bacterial communities of <i>Synsepalum dulcificum</i> predicted using Tax4Fun 2. by Rabiath F. R. Adigoun (14189090)

Functional feature prediction of intestinal microbiota from healthy (blue) and diseased (orange) shrimp. by Lalitphan Kitsanayanyong (19353505)

Subjects:

Threshold effect analysis of systemic inflammatory biomarkers on hepatitis C using two-piece wise linear regression. by Lu Han (151620)

Negative ion mode significant differential metabolites (Fold Change greater than 2 or less than 0.5). by Peng-fei Han (694841)

Subjects:

Positive ion mode significant differential metabolites (Fold Change greater than 2 or less than 0.5). by Peng-fei Han (694841)

Subjects:

Comparison between actual and predicted values. by Xueyong Pan (20390363)

Analysis of raw data prediction results. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Analysis of STL-PCA prediction results. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Scatter plots of the linear regression for the relationship between the duration of the vegetative phase and the average mass of single inflorescences. by Torsten Schober (20485754)

Comparison of HRV and STAI between intervention and control at Pre-CRI:SIS and Post-CRI-SIS shifts from linear mixed model for repeated measures. by Leigh V. Evans (19753987)

A slight negative trend in the relationship between U-Cd levels and BMD in both 2019 and 2022, as evident from the slightly downward-sloping regression lines in both years. However... by Aroon La-up (14095691)

Subjects:

Distance-based redundancy analysis (dbRDA) plot performed from the distance-based linear model permutation test (DistLM) that describes the aggregation of sampling stations at ever... by Francisco Flores-de-Santiago (20391063)