Binary leaf image rotated anti-clock wise from the origin degree to 40^<i>o</i>, 80^<i>o</i>, 120^<i>o</i>, and 160^<i>o</i> from left to ri... by Ali Ahmed (2567584)

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

RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte Carlo Metropolis Algorithms by Yidan Tang (6623693)

“…By leveraging combinatorial ultralarge libraries, RosettaAMRLD ensures synthetic accessibility, optimizing protein–ligand interactions while efficiently sampling accessible chemical space. …”

LITNET-2020 data splitting approach. by Asmaa Ahmed Awad (16726315)

Process of generating MS/MS spectra from a protein mixture using mass-spectrometry analysis. by Muhammad Usman Tariq (11022141)

Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model by Getachew S. Molla (6416744)

“…Because of the semipredictive nature of the symmetric eNRTL-SAC model, the segment parameter regression is a critical step for solubility prediction accuracy. A particle swarm optimization algorithm is incorporated to preregress conceptual segment parameters of solutes. …”

Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF by Haidi Wang (9694004)

Performance of the SPP. by Virgile Andreani (17087764)

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

A generic proteomics flow. by Muhammad Usman Tariq (11022141)

Optimal 8-mer and 9-mer SARS-CoV-2 epitope identification. by Mariah Hassert (5746874)

“…Peptide sequences are named based on the protein they are contained within, followed by the number of the first amino acid residue of the peptide in the context of the full protein, to the last amino acid residue. potential optimal 8-mer or 9-mer CD8+ T cell epitopes were predicted. …”

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…In this direction, the option of estimating the statistics of the model directly from the training image (performing a refined pattern search instead of simulating data) is a very promising.<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”

DataSheet1_Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study.docx by Sergei Evteev (18294157)

“…</p><p>Methods: We developed Quantum-assisted Fragment-based Automated Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. …”

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

“…This information is useful to guide the construction of libraries for shape-based virtual screening and to improve the docking algorithm to efficiently sample bound conformations.…”

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

“…This information is useful to guide the construction of libraries for shape-based virtual screening and to improve the docking algorithm to efficiently sample bound conformations.…”

GSE96058 information. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”

The performance of classifiers. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”

Image_13_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

Image_2_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

Image_1_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)