Table 2_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 6_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Predicted F1 score across temporal aggregation levels and BirdNET parameters. by Andrew J. Fairbairn (22231970)

Data-Driven Design of Random Heteropolypeptides as Synthetic Polyclonal Antibodies by Haisen Zhou (13017532)

“…By combining high-throughput synthesis of selenopolypeptide derivatives with algorithm-assisted optimization, we rapidly identified SpAbs targeting human interferon-α (IFN) and tumor necrosis factor-α (TNF-α) within 2 weeks. …”

Data-Driven Design of Random Heteropolypeptides as Synthetic Polyclonal Antibodies by Haisen Zhou (13017532)

“…By combining high-throughput synthesis of selenopolypeptide derivatives with algorithm-assisted optimization, we rapidly identified SpAbs targeting human interferon-α (IFN) and tumor necrosis factor-α (TNF-α) within 2 weeks. …”

Data-Driven Design of Random Heteropolypeptides as Synthetic Polyclonal Antibodies by Haisen Zhou (13017532)

“…By combining high-throughput synthesis of selenopolypeptide derivatives with algorithm-assisted optimization, we rapidly identified SpAbs targeting human interferon-α (IFN) and tumor necrosis factor-α (TNF-α) within 2 weeks. …”

Predicted structure of VPS13C PD variants. by Anna K. Waldmann (22250729)

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”

Bayesian Clustering via Fusing of Localized Densities by Alexander Dombowsky (20289372)

“…The data are then clustered by minimizing the expectation of a clustering loss function that favors similarity to the component labels. …”

IEEE Big Data 2024 Presentation: Norma: A Framework for Finding Threshold Associations Between Continuous Variables Using Point-wise Function by Md Mahin (21434966)

“…Norma introduces the unique Continuous Variable Threshold (CVT) pattern, aiming to identify a pair of thresholds within the value domain of two continuous variables, revealing strong associations within a specified geographic area. …”

Quantile Regression and Homogeneity Identification of a Semiparametric Panel Data Model by Rui Li (4631)

“…Observing that subjects within a group may share identical trend functions, we are motivated to further explore potential homogeneity in these trends. …”

Overview diagram of the method and contributions of this work. by Gowri Nayar (14234462)

“…Panel B shows three tasks that can be performed with the set of HA sites. (1) The HA sites can be used to confirm the protein family alignment and validate a protein’s membership within a family based on the similarity of the progression of attention values. (2) The HA sites can define vectors that can be used to determine pairwise similarities between proteins. (3) The HA sites are also predictive of functionally important regions of the protein, as they are spatially close to active sites and residues crucial to the specificity of the protein.…”

This figure highlights the limitations of manual scoring. by Saeed Mohammadzadeh (13492285)

Test data on the ability to escape local optima. by Kejia Liu (5699651)

“…<div><p>The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. …”

Summary of the notations. by Kejia Liu (5699651)

“…<div><p>The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. …”

Comparison of population diversity. by Kejia Liu (5699651)

“…<div><p>The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. …”

Test data on mining capacity. by Kejia Liu (5699651)

“…<div><p>The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. …”

Comparison of standard GEP and DGEP. by Kejia Liu (5699651)

“…<div><p>The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. …”

Test data on population diversity. by Kejia Liu (5699651)

“…<div><p>The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. …”

High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization by Stephanos Karafiludis (12902591)

“…This work highlights the potential of integrating automated synthesis platforms with data-driven algorithms to accelerate the discovery of high-entropy materials, offering an efficient design pathway to advanced functional materials.…”