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)

Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures by Hongpeng He (348094)

“…The subset of optimal descriptors was screened by combining the genetic algorithm with the multiple linear regression method. …”

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…The algorithm was applied to aqueous, binary mixture systems composed of 37 common biochemical substances such as amino acids, organic acids, and sugars. …”

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

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)

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

RfaH sequence entropy distributions. by Lu Hong (399882)

Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity by George S. Watts (7962206)

“…We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. Binary mixtures of bacteria showed all three reliably identified organisms down to 1% relative abundance, while only the relative abundance estimates of Centrifuge and CLARK were accurate. …”

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). …”