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

P-values from analysis of ABIDE dataset for continuous outcome of VIQ. by Alexandria M. Jensen (6494957)

<b>Gene Catalog and Associated Functional Data</b> for BioProject PRJNA1311452 by Satheeswaran Thangaraj (22187269)

Observed distributions of ARI ×100 from the FRECL partition after consensus clustering; against the number of runs. by Susana Conde (5495675)

Table 1_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.xlsx by Yuezhou Huang (9998177)

“…</p>Results<p>Our study identified 183 AG-related targets, 5,193 differentially expressed genes, and 6,173 co-expression module genes associated with TNBC. Machine learning algorithms pinpointed 4 hub genes from 28 intersecting targets. …”

Table 2_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.xlsx by Yuezhou Huang (9998177)

“…</p>Results<p>Our study identified 183 AG-related targets, 5,193 differentially expressed genes, and 6,173 co-expression module genes associated with TNBC. Machine learning algorithms pinpointed 4 hub genes from 28 intersecting targets. …”

Table 3_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.docx by Yuezhou Huang (9998177)

“…</p>Results<p>Our study identified 183 AG-related targets, 5,193 differentially expressed genes, and 6,173 co-expression module genes associated with TNBC. Machine learning algorithms pinpointed 4 hub genes from 28 intersecting targets. …”

Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation. by Changze Ou (22806374)

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.…”

Quantum Simulation of Molecular Dynamics ProcessesA Benchmark Study Using a Classical Simulator and Present-Day Quantum Hardware by Tamila Kuanysheva (21546962)

“…Although Qiskit provides a general method for initializing wave functions, in most cases it generates deep quantum circuits. …”

Multimodal reference functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

P-values from analysis of ABIDE dataset for binary outcome of diagnostic group. by Alexandria M. Jensen (6494957)

Heatmap results from the global sensitivity analysis for the generation of chorionic vessels. by Diana C. de Oliveira (9220729)

Speech spectrogram extracted from speech signal processed by wavelet transform method (Morlet mother wavelet with center frequency parameter of 50 and bandwidth parameter of 3 was... by Li Jiashen (20552690)

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

Rosenbrock function losses for . by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

Rosenbrock function losses for . by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

Levy function losses for . by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

Rastrigin function losses for . by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

Levy function losses for . by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”