Performance of the greedy approximation algorithm. by Virgile Andreani (17087764)

Experiment on the two OrthoMaM amino acid alignments datasets. For each dataset, we show the total number of sites with gaps (across 15,868 alignments), and the percentages of thes... by Jordan Moutet (21800145)

Assessing binding site representation across dense arrays. by Virgile Andreani (17087764)

An optimal solution <i>A</i>⁺ for the example in Fig 1, found following the steps detailed in Fig 4. by Jordan Moutet (21800145)

A sequence of intermediate solutions produced by the top-down phase on the input of Fig 1, leading to an optimal solution (step 5). by Jordan Moutet (21800145)

Comprehensive, Open-Source, and Automated Workflow for Multisite λ‑Dynamics in Lead Optimization by Renling Hu (17888900)

“…To address this challenge, we developed a comprehensive, open-source, and automated workflow for MSLD calculations based on the BLaDE dynamics engine. This workflow incorporates the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm to accurately generate MSLD multiple topologies with ideal perturbation patterns. …”

Performance of the SPP. by Virgile Andreani (17087764)

Distribution of SNPs and their frequencies based on mutation classes. by Sadia Afrin Runa (22504971)

Single-Shot Capillary Zone Electrophoresis–Tandem Mass Spectrometry Produces over 4400 Phosphopeptide Identifications from a 220 ng Sample by Zhenbin Zhang (1444888)

“…The dependence of capillary zone electrophoresis (CZE) separations on the charge state of the analyte is useful for the analysis of many post-translational modifications in proteins. In this work, we coupled CZE to an Orbitrap Fusion Lumos Tribrid platform with an advanced peak determination algorithm for phosphoproteomics analysis. …”

Medium-throughput filtering by structural modeling and microarray screening. by Jérôme Tubiana (14559981)

Overview of the ligand data preprocessing. by Ubair Aziz (21485738)

Table3_Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.xlsx by Roberto Martin-Hernandez (3661549)

Table1_Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.xlsx by Roberto Martin-Hernandez (3661549)

Table2_Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.XLSX by Roberto Martin-Hernandez (3661549)

Image1_Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.JPEG by Roberto Martin-Hernandez (3661549)

Discovery of Vascular Endothelial Growth Factor Receptor 2 Inhibitors Employing Junction Tree Variational Autoencoder with Bayesian Optimization and Gradient Ascent by Gia-Bao Truong (20173121)

“…Newly generated molecules possessing acceptable results corresponding to both assessments were shortlisted and checked for interactions with the protein at the binding site on important residues, including Cys919, Asp1046, and Glu885.…”

DataSheet2_Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.PDF by Roberto Martin-Hernandez (3661549)

DataSheet1_Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.PDF by Roberto Martin-Hernandez (3661549)

DataSheet3_Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.PDF by Roberto Martin-Hernandez (3661549)

Determination of Multidirectional Pathways for Ligand Release from the Receptor: A New Approach Based on Differential Evolution by Hoang Linh Nguyen (7245437)

“…Here, we have proposed to determine the direction of pulling with a simple scoring function that minimizes the receptor–ligand interaction, and an optimization algorithm called differential evolution is used for energy minimization. …”