Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

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. …”

Flow chart diagram of quantum hash function. by Sultan H. Almotiri (14029251)

“…Our study addresses five major components of the quantum method to overcome these challenges: lattice-based cryptography, fully homomorphic algorithms, quantum key distribution, quantum hash functions, and blind quantum algorithms. …”

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

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

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

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

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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)

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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)

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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)

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

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

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Siou loss function calculation diagram. by Weiyu Han (3448742)

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. …”

Strong negative correlation between the precision of algorithms and the evolutionary distance from the training data. by Sven B. Gould (12237287)

Identification and functional analysis of hub genes. by Wei Ya Lan (22403712)

“…<p>(A) Protein–protein interaction (PPI) network constructed using the STRING database with a confidence score > 0.04. …”

The “Bubblepole” (BUPO) Method for Linear-Scaling Coulomb Matrix Construction with or without Density Fitting by Frank Neese (736441)

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DataSheet1_The evolution of flexibility and function in the Fc domains of IgM, IgY, and IgE.pdf by Rosaleen A. Calvert (10039787)

“…</p>Results and discussion<p>We analysed the scattering curves in terms of contributions from a pool of variously bent models chosen by a non-negative linear least-squares algorithm and found that the four proteins form a series in which the proportion of acutely bent material increases: IgM-Fc < IgY-Fc < plIgE-Fc < huIgE-Fc. …”

ClaritySpectra: Raman spectra analysis tool by Aaron Celestian (9395696)