Flow chart of the algorithm. by Mukesh Karunanethy (18422857)

Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity by George S. Watts (7962206)

“…We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. Binary mixtures of bacteria showed all three reliably identified organisms down to 1% relative abundance, while only the relative abundance estimates of Centrifuge and CLARK were accurate. …”

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

“…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”

Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke by Chulho Kim (622686)

“…Using “quanteda” NLP package, all text data were parsed into tokens to create the data frequency matrix. …”

Data_Sheet_1_Evaluating the Risk of Paroxysmal Atrial Fibrillation in Noncardioembolic Ischemic Stroke Using Artificial Intelligence-Enabled ECG Algorithm.DOCX by Changho Han (1309563)

Data_Sheet_2_Evaluating the Risk of Paroxysmal Atrial Fibrillation in Noncardioembolic Ischemic Stroke Using Artificial Intelligence-Enabled ECG Algorithm.xlsx by Changho Han (1309563)

Input gene scores. by Sergio Picart-Armada (4666903)

Statistics in Proteomics: A Meta-analysis of 100 Proteomics Papers Published in 2019 by David C. L. Handler (8791451)

“…This included questions such as whether a pilot study was conducted and whether false discovery rate calculation was employed at either the quantitation or identification stage. These data were then transformed to binary inputs, analyzed via machine learning algorithms, and classified accordingly, with the aim of determining if clusters of data existed for specific journals or if certain statistical measures correlated with each other. …”

Table4_Identification of mitophagy-related genes with potential clinical utility in myocardial infarction at transcriptional level.docx by Zhikai Yang (9501094)

Table_1_Identification of sources of DIF using covariates in patient-reported outcome measures: a simulation study comparing two approaches based on Rasch family models.DOCX by Yseulys Dubuy (16809567)

UMAHand: Hand Activity Dataset (Universidad de Málaga) by Eduardo Casilari (3333906)

“…<p dir="ltr">The objective of the UMAHand dataset is to provide a systematic, Internet-accessible benchmarking database for evaluating algorithms for the automatic identification of manual activities. …”

Data_Sheet_1_A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.docx by Çaǧlar Çaǧlayan (12253934)

“…</p>Materials and Methods<p>Leveraging data from electronic healthcare records and a unique MDRO universal screening program, we developed a data-driven modeling framework to predict MRSA, VRE, and CRE colonization upon intensive care unit (ICU) admission, and identified the associated socio-demographic and clinical factors using logistic regression (LR), random forest (RF), and XGBoost algorithms. …”

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

“…<p dir="ltr"><b>LC-MS/MS raw data</b></p><p dir="ltr">Spectrum matching and protein identification and validation were performed with MSFragger, and quantification of protein intensities with matching between runs was performed with IonQuant as components of the FragPipe analysis pipeline using the default settings of each module. …”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p><br></p><p dir="ltr">In the fifth measurement technique, the numbers of sharp <b>surface projections/protrusions</b> were calculated by initially applying Canny's edge detection algorithm to generate an edge map of the cell mask image. …”

Supplementary materials for PhD thesis 'Characterisation Of The Blazhko Effect In RR Lyrae Stars Using SuperWASP Data' by Paul Greer (22619328)

“…This phase domain analysis also led to the identification of 17 candidates for rare binary objects, due to their sinusoidal O-C curves. 73 objects had quadratic O-C curves, suggesting binary candidates with periods longer than the duration of SuperWASP observations.…”