MEDOC: A Fast, Scalable, and Mathematically Exact Algorithm for the Site-Specific Prediction of the Protonation Degree in Large Disordered Proteins by Martin J. Fossat (3714079)

Subjects: “…mathematically exact algorithm…”

Effect of <i>|Δd</i>_<i>n</i><i>|</i> perturbation on 3D modeling. by Giacomo Janson (8138517)

Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

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Development and Evaluation of GlycanDock: A Protein–Glycoligand Docking Refinement Algorithm in Rosetta by Morgan L. Nance (10981871)

“…In this work, we introduce <i>GlycanDock</i>, a residue-centric protein–glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. …”

Development and Evaluation of GlycanDock: A Protein–Glycoligand Docking Refinement Algorithm in Rosetta by Morgan L. Nance (10981871)

“…In this work, we introduce <i>GlycanDock</i>, a residue-centric protein–glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. …”

Development and Evaluation of GlycanDock: A Protein–Glycoligand Docking Refinement Algorithm in Rosetta by Morgan L. Nance (10981871)

“…In this work, we introduce <i>GlycanDock</i>, a residue-centric protein–glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. …”

Development and Evaluation of GlycanDock: A Protein–Glycoligand Docking Refinement Algorithm in Rosetta by Morgan L. Nance (10981871)

“…In this work, we introduce <i>GlycanDock</i>, a residue-centric protein–glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. …”

Development and Evaluation of GlycanDock: A Protein–Glycoligand Docking Refinement Algorithm in Rosetta by Morgan L. Nance (10981871)

“…In this work, we introduce <i>GlycanDock</i>, a residue-centric protein–glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. …”

(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. by Devi Soorya Narayana Sasikumar (22331743)

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Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

“…<b>(B)</b> The priority of the top 10 genes was evaluated through MNC, which identifies clusters of protein nodes that are more functionally connected to each other and selects the central proteins within the cluster. …”

Functional annotation enrichment for undruggable human disease associated proteins selected by PortalCG. by Tian Cai (4227052)

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Final result UMAP-Gaussian mixture model-monophyletic cluster maximization: Full algorithm UMAP-GMMEM-MCIM results for the OMP phylogeny are shown by color and number. by Adrian N. Ortiz-Velez (17775800)

Using ESM-1v as an objective function biases sampling towards RfaHβ-like sequences with lower native sequence recovery. by Lu Hong (399882)

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A Schematic flowchart of the ANN Models. by Saksham Phul (12437059)

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Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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Pair Potentials as Machine Learning Features by Jun Pei (1765720)

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Pair Potentials as Machine Learning Features by Jun Pei (1765720)

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Pair Potentials as Machine Learning Features by Jun Pei (1765720)

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Protein-Protein Interaction (PPI) and ceRNA Regulatory Network Analysis. by Jie Chu (293795)

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MCC values reported for three different ANN models. by Saksham Phul (12437059)

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