Graphical user interface for workflow management. by Yukako Yamane (9034133)

Example of downstream analysis. by Yukako Yamane (9034133)

Example steps for checking the input movie data. by Yukako Yamane (9034133)

“…OptiNiSt includes the following features. 1) Researchers can easily create analysis pipelines by selecting multiple processing modules, tuning their parameters, and visualizing the results at each step through a graphic user interface in a web browser. 2) In addition to pre-installed tools, new analysis algorithms can be easily added. 3) Once a processing pipeline is designed, the entire workflow with its modules and parameters are stored in a YAML file, which makes the pipeline reproducible and deployable on high-performance computing clusters. 4) OptiNiSt can read image data in a variety of file formats and store the analysis results in NWB (Neurodata Without Borders), a standard data format for data sharing. …”

Normalized convergence time. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

VGR structure. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Comparison of normalized throughput and load. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Principle of transfer learning. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Body-connected routing scenario. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025 by Andrew Rogers (17623239)

“…</p><h2>3. VRE Siting Algorithm and Optimization</h2><p><br></p><p dir="ltr">The VRE siting model uses a cost-minimization optimization approach to select the most cost-efficient project sites to meet a projected energy mix target.…”

Workflow summary: GRN motif, cell signaling model, and inference framework. by Michael Alexander Ramirez Sierra (20209354)

“…Multiple posterior estimates are produced with the same training set, selecting the best learned distribution conditional on the target observation by analyzing a “meta score” distribution (row 4 col 3). …”

Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

“…Set enrichment analysis was used for the pathway by selecting non-significant differentially expressed genes specified as the “background universe” and accounting for multiple testing using a false discovery rate of q < 0.1. …”

<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)

“…The described extracted features were used to predict leaf betalain content (µg per FW) using multiple machine learning regression algorithms (Linear regression, Ridge regression, Gradient boosting, Decision tree, Random forest and Support vector machine) using the <i>Scikit-learn</i> 1.2.1 library in Python (v.3.10.1) (list of hyperparameters used is given in <a href="#sup1" target="_blank">Supplementary Data S5</a>). …”