DataSheet1_Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm.docx حسب Ying Sun (23237)

Details of 23 basic benchmark functions. حسب Ying Li (38224)

QA vs GA for 10 full-length proteins. حسب Dillion M. Fox (4043159)

Eigenvalues measured by QA vs GA scores. حسب Dillion M. Fox (4043159)

Table_6_Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.XLSX حسب Shiqi Zhang (709298)

Table_8_Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.XLSX حسب Shiqi Zhang (709298)

Table_4_Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.XLSX حسب Shiqi Zhang (709298)

Table_2_Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.XLSX حسب Shiqi Zhang (709298)

Table_3_Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.XLSX حسب Shiqi Zhang (709298)

Table_5_Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.XLSX حسب Shiqi Zhang (709298)

Table_10_Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.XLSX حسب Shiqi Zhang (709298)

Table_7_Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.XLSX حسب Shiqi Zhang (709298)

Table_9_Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.XLSX حسب Shiqi Zhang (709298)

Table_1_Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.XLSX حسب Shiqi Zhang (709298)

Table 1_Leveraging a disulfidptosis-based signature to characterize heterogeneity and optimize treatment in multiple myeloma.docx حسب Bingxin Zhang (13011397)

RfaH sequence entropy distributions. حسب Lu Hong (399882)

Optimal stability ranking of candidate reference genes using geNorm, NormFinder, BestKeeper, and rank aggregation method. حسب Marina Linardić (10720448)

Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential حسب Fengyang Han (14613218)

"…In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). …"

Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential حسب Fengyang Han (14613218)

"…In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). …"

Performance of pairing prediction versus training set size and number of pairs per species. حسب Guillaume Marmier (7495823)