Example of a typical workflow to construct a network using NeKo. by Marco Ruscone (11747264)

Identifying cognitive impairment with story recall (Wilson et al., 2024) by Sarah C. Wilson (19370966)

“…</b> Discourse measures and descriptions from the CLAN software (including duration, MLU, type-token ratio, etc.).</p><p dir="ltr"><b>Supplemental Material S3.…”

Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. by Kevin Sawade (16726527)

“…However, our algorithm was 1000 times faster than pyROSETTA (both algorithms have been parallelized on a per-structure basis using the Python package joblib [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010531#pcbi.1010531.ref069" target="_blank">69</a>]).…”

Image_1_KairoSight: Open-Source Software for the Analysis of Cardiac Optical Data Collected From Multiple Species.TIF by Blake L. Cooper (11622613)

“…Despite the refinement of software tools and algorithms, significant programming expertise is often required to analyze large optical data sets, and data analysis can be laborious and time-consuming. …”

Pyrho Validation - Check re-gridded periodic data by Jimmy Shen (12664675)

Overall framework of OptRAM. by Fangzhou Shen (5140994)

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning by Xiaoshi Cheng (14119418)

“…Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. …”

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning by Xiaoshi Cheng (14119418)

“…Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. …”

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning by Xiaoshi Cheng (14119418)

“…Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. …”

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning by Xiaoshi Cheng (14119418)

“…Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. …”

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning by Xiaoshi Cheng (14119418)

“…Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. …”

Schematic example of multiscale modelling implementation. by Robyn M. Stuart (3754000)

Application to Neuropixels dataset. by Rahul Biswas (14117086)

Image_2_Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA.tif by Changqiang Wei (11454415)

“…This profile underwent differential expression analysis, WGCNA, functional enrichment, and subsequent analysis of RPL gene expression using LASSO regression, SVM-RFE, and RandomForest algorithms for hub gene screening. …”

Image_1_Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA.tif by Changqiang Wei (11454415)

“…This profile underwent differential expression analysis, WGCNA, functional enrichment, and subsequent analysis of RPL gene expression using LASSO regression, SVM-RFE, and RandomForest algorithms for hub gene screening. …”

Image_3_Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA.tif by Changqiang Wei (11454415)

“…This profile underwent differential expression analysis, WGCNA, functional enrichment, and subsequent analysis of RPL gene expression using LASSO regression, SVM-RFE, and RandomForest algorithms for hub gene screening. …”

PyPEFAn Integrated Framework for Data-Driven Protein Engineering by Niklas E. Siedhoff (11133851)

“…Data-driven strategies are gaining increased attention in protein engineering due to recent advances in access to large experimental databanks of proteins, next-generation sequencing (NGS), high-throughput screening (HTS) methods, and the development of artificial intelligence algorithms. However, the reliable prediction of beneficial amino acid substitutions, their combination, and the effect on functional properties remain the most significant challenges in protein engineering, which is applied to develop proteins and enzymes for biocatalysis, biomedicine, and life sciences. …”

CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages by Vicente Martí-Centelles (1422415)

“…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”

CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages by Vicente Martí-Centelles (1422415)

“…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”