Table 4_Biomarkers in glioblastoma and degenerative CNS diseases: defining new advances in clinical usefulness and therapeutic molecular target.xlsx by Fan Bu (1563562)

Table 3_Biomarkers in glioblastoma and degenerative CNS diseases: defining new advances in clinical usefulness and therapeutic molecular target.xlsx by Fan Bu (1563562)

Table 1_Biomarkers in glioblastoma and degenerative CNS diseases: defining new advances in clinical usefulness and therapeutic molecular target.docx by Fan Bu (1563562)

Table 2_Biomarkers in glioblastoma and degenerative CNS diseases: defining new advances in clinical usefulness and therapeutic molecular target.xlsx by Fan Bu (1563562)

Table 1_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.xlsx by Yuezhou Huang (9998177)

“…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …”

Table 2_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.xlsx by Yuezhou Huang (9998177)

“…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …”

Table 3_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.docx by Yuezhou Huang (9998177)

“…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …”

Data Sheet 1_Identification of novel lipid metabolism-related biomarkers of aortic dissection by integrating single-cell RNA sequencing analysis and machine learning algorithms.zip by Zhechen Li (15456929)

“…Single-cell RNA sequencing data from aortic dissection and control samples were processed to analyze lipid metabolism activity and identify differentially expressed genes. Machine learning algorithms and protein-protein interaction networks were then used to prioritize biomarkers, which were further validated through bulk RNA-seq analysis and immune infiltration studies and experiments using an Ang II-induced aortic dissection mouse model.. …”

Convergence of the numerical solution of the non-linear equation system through the iterations <i>p</i> using the Newton-Raphson algorithm and the Levenberg–Marquardt algorithm. by Martin Dodek (20623832)

Subjects:

Table 1_Integrating GWAS and machine learning for disease risk prediction in the Taiwanese Hakka population.xlsx by Jing-Hong Xiao (22780781)

“…After standard quality control, 295,589 SNPs were retained. Fourteen machine-learning algorithms were evaluated using SNPs selected through traditional GWAS filtering and refined via wrapper-based feature selection with a best-first search algorithm. …”

Predictions from a Universal Value Function Approximator (UVFA) algorithm. by Sam Hall-McMaster (10343795)

Subjects:

Cost analysis for prototype implementation for crop and fertilizer recommendation system. by Gourab Saha (8987405)

Subjects:

Mean parameter values for the selected crops. by Gourab Saha (8987405)

Subjects:

Performance comparison of ML models. by Gourab Saha (8987405)

Subjects:

Comparative data of different soil samples. by Gourab Saha (8987405)

Subjects:

Flow chart of Fuzzy Logic based control system. by Gourab Saha (8987405)

Subjects:

Comparative analysis of crop yield prediction from satellite image. by Gourab Saha (8987405)

Subjects:

Crop factor of different crops according to height. by Gourab Saha (8987405)

Subjects:

Confusion matrix of random forest model. by Gourab Saha (8987405)

Subjects:

Cost comparison between diesel pump and solar pump. by Gourab Saha (8987405)

Subjects: