Supplemental files to the study "Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins" by Valerie de Crecy-Lagard (12823436)

“…<p dir="ltr">Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein “unknome”. …”

Expression levels of spinal cord proteins in each group of rats. by Yufeng He (5673119)

Python code for a rule-based NLP model for mapping circular economy indicators to SDGs by Zahir Barahmand (18008947)

“…The package includes:</p><ul><li>The complete Python codebase implementing the classification algorithm</li><li>A detailed manual outlining model features, requirements, and usage instructions</li><li>Sample input CSV files and corresponding processed output files to demonstrate functionality</li><li>Keyword dictionaries for all 17 SDGs, distinguishing strong and weak matches</li></ul><p dir="ltr">These materials enable full reproducibility of the study, facilitate adaptation for related research, and offer transparency in the methodological framework.…”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

Subjects: “…probabilistic scoring function…”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

Subjects: “…probabilistic scoring function…”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

Subjects: “…probabilistic scoring function…”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

Subjects: “…probabilistic scoring function…”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

Subjects: “…probabilistic scoring function…”

WCST scores and protein expression levels. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. …”

PATH⁺ explains tighter binding of carbonic anhydrase II by brinzolamide. by Yuxi Long (11024307)

Subjects:

An overview of PATH⁺. by Yuxi Long (11024307)

Subjects:

The 10 features captured by persistence fingerprint. by Yuxi Long (11024307)

Subjects:

^aTwo HIV-1 protease mutants bound to inhibitor darunavir (G48V: PDB ID 3cyw [81] & L90M: PDB ID 2f81 [82]). by Yuxi Long (11024307)

Subjects:

Scatter plots of PATH⁺ and our implementation of TNet-BP’s predictions on a held-out, test subset of PDBBind v2020 refined set for one run (90:10 train:test split ratio,... by Yuxi Long (11024307)

Subjects:

PATH⁺ correctly predicted a weaker binding affinity for HIV-1 protease with the drug-resistant G48V mutation (right, experimental kcal/mol, PDB ID: 3cyw [81]) bound to d... by Yuxi Long (11024307)

Subjects:

Performance of TNet-BP and PATH⁺ on the PDBBind v2020 refined set shows that PATH⁺ achieves similar performance with TNet-BP while having three orders fewer f... by Yuxi Long (11024307)

Subjects:

Construction of internuclear persistence contours (IPCs). by Yuxi Long (11024307)

Subjects:

Comparison of PATH⁺ with TNet-BP [43]. by Yuxi Long (11024307)

Subjects:

Heat map of the top 9 protein variables. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. …”

Quantum Computing and peptide folding by Akshay Uttarkar (19699990)

“…<p dir="ltr">The work "Peptide Folding with Quantum CVaR-VQE Algorithm" represents a significant advancement in the field of computational biology, particularly in the challenging domain of protein folding. …”