Data Sheet 2_Antibodies and cryptographic hash functions: quantifying the specificity paradox.pdf by Robert J. Petrella (5146220)

“…The results illustrate and quantify how the humoral immune system achieves both high specificity and high degeneracy simultaneously by effectively decoupling the two properties, similarly to programs in cryptography called secure hash algorithms (SHAs), which display the same paradoxical features. …”

Quantum Chemistry-Guided Machine Learning for Accelerated Design of CO₂‑Solubilizing Deep Eutectic Solvents by Dingkai Hu (16277547)

“…We compiled 2287 experimental measurements from 119 DESs over wide temperature (293.15–353.15 K) and pressure (26.3–7620 kPa) ranges. …”

Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling by Shan He (121220)

“…To address this, we created a new data set of NP systems from public sources. Herein, 11 different AI/ML algorithms were used to develop the predictive AI/ML models. …”

SPSS Data File for Sample 1. by Mariusz Zięba (21369920)

“…The three cross-sectional studies involved a total of 1,219 participants from Poland. Confirmatory factor analysis conducted on two separate samples indicates that bifactor models provide the best fit for both the Compassion for Others scale and the Compassion from Others scale. …”

Indices of fit for two three-factor models. by Mariusz Zięba (21369920)

“…The three cross-sectional studies involved a total of 1,219 participants from Poland. Confirmatory factor analysis conducted on two separate samples indicates that bifactor models provide the best fit for both the Compassion for Others scale and the Compassion from Others scale. …”

SPSS Data File for Sample 3. by Mariusz Zięba (21369920)

“…The three cross-sectional studies involved a total of 1,219 participants from Poland. Confirmatory factor analysis conducted on two separate samples indicates that bifactor models provide the best fit for both the Compassion for Others scale and the Compassion from Others scale. …”

SPSS Data File for Sample 2. by Mariusz Zięba (21369920)

“…The three cross-sectional studies involved a total of 1,219 participants from Poland. Confirmatory factor analysis conducted on two separate samples indicates that bifactor models provide the best fit for both the Compassion for Others scale and the Compassion from Others scale. …”

Background heterogeneity and sequencing depth improve WheresWalker SNPindex. by McKenna Feltes (11242085)

Mapping and confirmation of the <i>arches</i> locus. by McKenna Feltes (11242085)

Forward genetic screen identifies 28 dark yolk mutants. by McKenna Feltes (11242085)

Summary of WGS datasets. by McKenna Feltes (11242085)

“…<div><p>Forward genetic screening is a powerful approach to assign functions to genes and can be used to elucidate the many genes whose functions remain unknown. …”

Oligo sequences. by McKenna Feltes (11242085)

“…<div><p>Forward genetic screening is a powerful approach to assign functions to genes and can be used to elucidate the many genes whose functions remain unknown. …”

<i>zion</i> WGS summary. by McKenna Feltes (11242085)

“…<div><p>Forward genetic screening is a powerful approach to assign functions to genes and can be used to elucidate the many genes whose functions remain unknown. …”

Confirmation of the <i>zion</i> locus. by McKenna Feltes (11242085)

<i>slc3a2a</i>^<i>zion</i> is a novel regulator of B-lp metabolism. by McKenna Feltes (11242085)

Reinforcement Learning-Based Hybrid Force/Position Control of Redundant Manipulators under Time Delays by Mojtaba Radan Kashani (20150034)

“…This paper introduces a semi-model-free framework, the Force/Position Reinforcement Learning Super-Twisting Algorithm (F/P-RL-STA), which avoids explicit space separation and reduces dependency on accurate models. …”

S1 File - by Xihao Shen (20347942)

“…After integrating Senescence-Related Genes (SRGs) from multiple established databases, differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were applied to identify predictive SRGs (pSRGs). …”

6 data sets included in this study. by Xihao Shen (20347942)

“…After integrating Senescence-Related Genes (SRGs) from multiple established databases, differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were applied to identify predictive SRGs (pSRGs). …”

Identification of hub DE-SRGs. by Xihao Shen (20347942)

Supplementary file 1_Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.zip by Nina de Lacy (6559520)

“…Deep learning was guided by an evolutionary algorithm that jointly performed optimization across hyperparameters and automated feature selection, allowing more candidate predictors and a wider variety of predictor types to be analyzed than the largest previous comparable machine learning studies.…”