Table 1_Uncovering differential gene expression between mtRNA-positive and -negative osteosarcoma cells: implications beyond mitochondrial function.xlsx by Xiaoyuan Meng (22261234)

“…</p>Methods<p>Explore the function of mtRNA in the occurrence and development of osteosarcoma utilizing bioinformatics analysis of the Gene Expression Omnibus (GEO) microarray dataset. …”

Table 3_Uncovering differential gene expression between mtRNA-positive and -negative osteosarcoma cells: implications beyond mitochondrial function.xlsx by Xiaoyuan Meng (22261234)

“…</p>Methods<p>Explore the function of mtRNA in the occurrence and development of osteosarcoma utilizing bioinformatics analysis of the Gene Expression Omnibus (GEO) microarray dataset. …”

Comparison curve of objective function (). by Wanjun Zhang (496858)

Functional analysis of anoikis-related genes. by Yufeng He (5673119)

Loss function curve. by Xiangqian Xu (17310895)

“…Aiming at the small defect size in the weld image, which is easy to cause missed detection and false detection, a lightweight target detection algorithm based on improved YOLOv7 is proposed. …”

Computational time for each algorithm as functions of (1) number of genes, with a fixed number of 1200 cells per time point (left); or (2) number of cells per time point, with a fixed number of 100 genes. by Wenjun Zhao (644283)

“…<p>Computational time for each algorithm as functions of (1) number of genes, with a fixed number of 1200 cells per time point (left); or (2) number of cells per time point, with a fixed number of 100 genes.…”

Objective functions and solving methods for PVRP problems with time windows, multiple depots, and a heterogeneous fleet. by Alejandro Arenas-Vasco (19797133)

Table 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 4_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 5_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 2_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 3_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Data Sheet 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.docx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It implements a probabilistic scoring function to match spectra to sequences, a novel combinatorial search strategy for finding post-translational modifications, and a Bayesian approach to locate modification sites. …”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It implements a probabilistic scoring function to match spectra to sequences, a novel combinatorial search strategy for finding post-translational modifications, and a Bayesian approach to locate modification sites. …”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It implements a probabilistic scoring function to match spectra to sequences, a novel combinatorial search strategy for finding post-translational modifications, and a Bayesian approach to locate modification sites. …”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It implements a probabilistic scoring function to match spectra to sequences, a novel combinatorial search strategy for finding post-translational modifications, and a Bayesian approach to locate modification sites. …”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It implements a probabilistic scoring function to match spectra to sequences, a novel combinatorial search strategy for finding post-translational modifications, and a Bayesian approach to locate modification sites. …”

The process of constructing a functional network. by Wen-Wei Shao (21162477)

Dependency of the average execution time of a single iteration corresponding to the proposed correlation method and the recursive pseudolinear regression as a function of the numbe... by Martin Dodek (20623832)