Table 4_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx حسب 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 حسب 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 حسب 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 3_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx حسب 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. …"

Data Sheet 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.docx حسب 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. …"

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. حسب Gursimran Singh (575288)

الموضوعات:

Confirmation of hub MitoDEGs expression and association with cardiac function in MI mice. حسب Wenlong Kuang (20485167)

الموضوعات:

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms حسب Zahra Bastami (21474755)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms حسب Zahra Bastami (21474755)

Comparison the results with and without Fourier Loss Function for Rain100H. حسب Chaobing Zheng (20896579)

Functional enrichment of DEGs. حسب Man Wang (110466)

"…</p><p>Methods</p><p>The study employed a combination of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and various machine learning algorithms to screen for characteristic genes. …"

Transmission modelling functional block diagram. حسب Alyssa N. Sbarra (18460545)

Logistic kernel: Power and Type I error for cluster evaluation metric simulations. حسب Alexandria M. Jensen (6494957)

الموضوعات:

ABIDE dataset subject demographics. حسب Alexandria M. Jensen (6494957)

الموضوعات:

Linear kernel: Power and Type I error for cluster evaluation metric simulations. حسب Alexandria M. Jensen (6494957)

الموضوعات:

P-values from analysis of ABIDE dataset for binary outcome of diagnostic group. حسب Alexandria M. Jensen (6494957)

الموضوعات:

Examples of four simulated networks and adjacency matrices. حسب Alexandria M. Jensen (6494957)

الموضوعات:

P-values from analysis of ABIDE dataset for continuous outcome of VIQ. حسب Alexandria M. Jensen (6494957)

الموضوعات:

Explained variance ration of the PCA algorithm. حسب Abeer Aljohani (18497914)

"…<div><p>Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. …"

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... حسب Ziyan Jin (21021976)