VEP annotation of the aSNPs listed in S1 Table. by Rongxin Zhang (1618159)

“…<div><p>G-quadruplexes (G4s) are nucleic acid secondary structures with important regulatory functions. Single-nucleotide variants (SNVs), one of the most common forms of genetic variation, can potentially impact the formation of G4 structures if they occur within G4 regions. …”

G4SNVHunter workflow for identifying variants that affect G4 formation. by Rongxin Zhang (1618159)

“…Subsequently, the impact of the variants on the formation potential of the identified G4s will be assessed based on the G4Hunter algorithm (Middle panel). Finally, candidate variants can be filtered and visualized using functions provided by G4SNVHunter to screen out those that can potentially disrupt the formation of G4 structures (Right panel). …”

A hypothetical example illustrating the differences between the two assessment modes in G4SNVHunter. by Rongxin Zhang (1618159)

Overview of archaic SNPs (aSNPs) potentially affecting G4 formation, identified by G4SNVHunter. by Rongxin Zhang (1618159)

Reactive Molecular Simulation and Microscopic Origins in the Reaction Kinetics of Binary Polymerization by Xinwei Chen (255125)

“…A microscopic expression of the hybrid function is derived based on the general collision reaction model and reactive algorithm in molecular dynamics (MD) simulation, in which both the reaction energy barrier and intermolecular interaction play pivotal roles. …”

Overnight technician routing and scheduling problem with time windows and balanced workloads: a bi-objective zebra optimization algorithm by Abolfazl Gharaei (21803416)

“…</p> <p><b>Highlights</b></p><p>An ML-based bi-objective zebra optimisation algorithm to treat large-scale TRSPs</p><p>Centroid-based clustering on the population of zebras to avoid bias towards a specific search space</p><p>Making a trade-off between exploration and exploitation of the feasible region in the developed algorithm</p><p>A new MINLP model of a weighted bi-objective TRSP with limited capacity depots</p><p>Workload function, penalty function for lateness, subcontracts, time windows for tasks and breaks</p><p>Experiments using real data to show the performance of the model and solution method</p><p></p> <p>An ML-based bi-objective zebra optimisation algorithm to treat large-scale TRSPs</p> <p>Centroid-based clustering on the population of zebras to avoid bias towards a specific search space</p> <p>Making a trade-off between exploration and exploitation of the feasible region in the developed algorithm</p> <p>A new MINLP model of a weighted bi-objective TRSP with limited capacity depots</p> <p>Workload function, penalty function for lateness, subcontracts, time windows for tasks and breaks</p> <p>Experiments using real data to show the performance of the model and solution method</p>…”

S1 Dataset - by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Statistical tests of ACC on the random network. by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Parameters in the experiment. by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Statistical tests of APL on the random network. by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Statistical tests of ACC on the regular network. by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Statistical tests of APL on the regular network. by Ruochen Zhang (3434996)

“…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …”

Optimal Subsampling for Functional Quasi-Mode Regression with Big Data by Tao Wang (12008)

“…<p>We propose investigating optimal subsampling for functional regression with massive datasets based on the mode value, which is referred to as functional quasi-mode regression, to reduce data volume and alleviate computational burden. …”

Deep Neural Network for Functional Graphical Models Structure Learning by Shuoyang Wang (22127443)

“…We discover a novel critical sampling frequency that governs the convergence rates of the deep neural network estimator for both densely and sparsely observed functional data. …”

Data for "A multi-objective platform for autonomous property targeting and optimization of colloidal lead halide perovskite quantum dots" by Neal Munyebvu (19690954)

Longitudinal trajectories of functional network development across the birth transition. by Lanxin Ji (20290942)

“…<p><b> </b> (A) One-sample <i>t</i> test on RSFC across all subjects. Stronger RSFC within networks affirms validity of the network clustering algorithm. …”

Protein-Protein Interaction (PPI) and ceRNA Regulatory Network Analysis. by Jie Chu (293795)

GSEA Analysis of Differentially Expressed Genes in High and Low-Risk Groups. by Jie Chu (293795)

Effect of Mitophagy Score on Immunotherapy for Different Datasets. by Jie Chu (293795)

GO/KEGG Enrichment Analysis of Differentially Expressed Genes in High and Low-Risk Groups. by Jie Chu (293795)