DNAmethylation prognosis prediction Comparison. by Ruiyu Zhan (21602031)

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Evaluating the Efficacy of Different Machine Learning Methods to Classify Cancer Data. by Ruiyu Zhan (21602031)

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Table 7 displays the results of evaluations performed on fixed-dimension multimodal benchmark functions. by Ruiyu Zhan (21602031)

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Outcomes of unimodal benchmarking assessments. by Ruiyu Zhan (21602031)

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Pearson correlation heatmap of the miRNA. by Ruiyu Zhan (21602031)

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The rank sum test p-value. by Ruiyu Zhan (21602031)

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Gene expression feature importance ranking. by Ruiyu Zhan (21602031)

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Fixed-dimensional multimodal reference functions. by Ruiyu Zhan (21602031)

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The feature importance ranking for miRNA expression. by Ruiyu Zhan (21602031)

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BP schematic diagram. by Ruiyu Zhan (21602031)

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The results on early-stage diabetes risk prediction dataset. by Ruiyu Zhan (21602031)

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Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

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The convergence curves of the test functions. by Ruiyu Zhan (21602031)

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DNAmethylationfeature importance ranking. by Ruiyu Zhan (21602031)

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Pearson correlation heatmap of DNAmethylation. by Ruiyu Zhan (21602031)

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Single-peaked reference functions. by Ruiyu Zhan (21602031)

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Multimodal reference functions. by Ruiyu Zhan (21602031)

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Compare LGWO-BP training time, GWO-BP training time. by Ruiyu Zhan (21602031)

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Figure 3 from Extrachromosomal DNA–Driven Oncogene Spatial Heterogeneity and Evolution in Glioblastoma by Imran Noorani (9184605)

“…<b>D,</b> Spatial computational model of ecDNA-driven tumors. ABC was used to infer optimal model parameters for the initial number of ecDNA, <i>k</i>; ecDNA-conferred selection, <i>s</i>; and cell pushing strength, <i>q</i>, for a given set of patient tumor measurements. …”

Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction by Mohamad A. Raja (19640297)

“…To preform accurate forecasts on energy storage, a data-driven machine learning approach based on artificial neural networks (ANN) was optimized via a Bayesian optimization algorithm to predict the structural battery’s future capacity. …”