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

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

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

RfaH sequence entropy distributions. by Lu Hong (399882)

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

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

Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential by 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 by 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). …”

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

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

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

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

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

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

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

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

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

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

Network propagation identifies ageing-associated genes as well as genes distinguishing prostate tumors of different grades. by Konstantina Charmpi (9901053)

Data_Sheet_1_A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.PDF by Seema Khadirnaikar (9227318)

“…A notable result observed was high infiltration of T cells and a higher level of neopeptides in uLUAD patients, making these patients an optimal candidate for immunotherapy. Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs–based classification, and identified a group of patients with high stem cell–like characteristic in the 10 most common cancer types and CCLE cell lines. …”