PAM graphs of the runs in Strategy 1. by Ural Akincioglu (21772538)

“…In the pre-processing stage, intrinsic mode functions of the signals were obtained using the ICEEMDAN algorithm. …”

Unsupervised Machine Learning for Differential Analysis in Proteomics by Guanyang Xu (22379531)

“…Using <i>in silico</i> simulated data sets derived from experimental data, we enabled cross-algorithm comparability through a probability based transformation. …”

Artificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants by Pengcheng Jiao (8017532)

“…An artificial intelligence inverse design model is subsequently developed by integrating an evolutionary algorithm with a neural network. This model allows for the automatic determination of the optimal microstructure for thermo-metamaterials with desired performance,i.e., target bending stiffness. …”

Artificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants by Pengcheng Jiao (8017532)

“…An artificial intelligence inverse design model is subsequently developed by integrating an evolutionary algorithm with a neural network. This model allows for the automatic determination of the optimal microstructure for thermo-metamaterials with desired performance,i.e., target bending stiffness. …”

Artificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants by Pengcheng Jiao (8017532)

“…An artificial intelligence inverse design model is subsequently developed by integrating an evolutionary algorithm with a neural network. This model allows for the automatic determination of the optimal microstructure for thermo-metamaterials with desired performance,i.e., target bending stiffness. …”

Artificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants by Pengcheng Jiao (8017532)

“…An artificial intelligence inverse design model is subsequently developed by integrating an evolutionary algorithm with a neural network. This model allows for the automatic determination of the optimal microstructure for thermo-metamaterials with desired performance,i.e., target bending stiffness. …”

Artificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants by Pengcheng Jiao (8017532)

“…An artificial intelligence inverse design model is subsequently developed by integrating an evolutionary algorithm with a neural network. This model allows for the automatic determination of the optimal microstructure for thermo-metamaterials with desired performance,i.e., target bending stiffness. …”

Artificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants by Pengcheng Jiao (8017532)

“…An artificial intelligence inverse design model is subsequently developed by integrating an evolutionary algorithm with a neural network. This model allows for the automatic determination of the optimal microstructure for thermo-metamaterials with desired performance,i.e., target bending stiffness. …”

<b>FCG-MFD: Benchmark Function Call Graph-Based Dataset for Malware Family Detection</b> by Hassan jalil hadi (18243805)

“…Our dataset has two sub-datasets (FCG & Metadata) (1,00,000 samples) from VirusSamples, Virusshare, VirusSign, theZoo, Vx-underground, and MalwareBazaar curated using FCGs and metadata to optimize the efficacy of ML algorithms. …”

Sequence analysis of gorilla CYP4B1. by Saskia Hüsken (21616072)

“…<p><b>(A)</b> The endogenous <i>Homo sapiens</i> CYP4B1 (hCYP4B1) located on human chromosome 1 (NC_000001.11 NCBI ID) encodes 12 exons. …”

Identification and enrichment analysis of the TMGs. by Ravinder Sharma (13154079)

Gene Ontology (GO) terms in the two modules. by Ravinder Sharma (13154079)

A synopsis of the research design and flowchart. by Ravinder Sharma (13154079)

“…Subsequently, using the MCODE algorithm, we identified 6 hub genes—ATN1, JPH3, TBP, VPS13A, DMD, and HTT—as core candidates. …”

S1 File - Single-cell and spatial transcriptomics reveal post-translational modifications in osteosarcoma progression and tumor microenvironment by Yue Cui (212753)

Experiment 4: Evaluating modularity-based clustering methods on tree-of-cliques networks. by Yasamin Tabatabaee (18095491)

Experiment 5: Clustering networks that are only partially clusterable. by Yasamin Tabatabaee (18095491)

Flawless analysis framework overview, with application to EEG power spectral densities as functional data observed over time. by Owen Forbes (4530652)

“…<p><i>X</i>_<i>pk</i> indicates a functional data array with <i>p</i> = individuals, <i>k</i> = time, and <i>t</i> = functional domain. …”

Presentation 1_Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated... by Rachid Tahzima (22106816)

Overview of VE-Cadherens morphology per cluster. by Rudmer J. Postma (20513473)

Characterization of CD3⁺ T-cell spatial distribution in the CRC tumor microenvironment using TIPC. by Mai Chan Lau (3135303)