<b>Resting ECG Segmentation Dataset</b> by Yuhang Liu (21210833)

“…</li><li><b>Rhythm diversity</b> – The set spans atrial fibrillation (AFIB), atrial flutter (AF), atrial tachycardia (AT), supraventricular tachycardia (SVT) and multiple sinus irregularities, providing rich morphological variation for robust model training.<br><br>Rhythm Type Records num</li><li>AF (Atrial Flutter) 400</li><li>AFIB (Atrial Fibrillation) 400</li><li>AT (Atrial Tachycardia) 121</li><li>SB (Sinus Bradycardia) 400</li><li>SI (Sinus Irregularity) 399</li><li>SR (Sinus Rhythm) 400</li><li>ST (Sinus Tachycardia) 140</li><li>SVT (Supraventricular Tachycardia) 139</li><li>Total 2399</li></ul><p><br></p><p dir="ltr">The combination of high-quality beat-level labels and broad rhythm coverage makes RDB a strong benchmark for developing and evaluating ECG segmentation algorithms that must generalise across diverse clinical presentations.…”

Table 1_Integrated analysis of N-glycosylation and Alzheimer’s disease: identifying key biomarkers and mechanisms.xls by Hao Zhang (15339)

“…Differential expression profiling identified 6,845 DEGs, including TMEM59, MLEC, and MAX. Machine learning algorithms consistently prioritized these three genes as core N-glycosylation-related biomarkers, alongside APP as a key associated molecule. …”

Pipeline for preterm birth classifier construction. by Zhiwei Guo (335819)

“…In the training stage, we applied non-linear support vector machine (SVM), logistic regression (LR), random forest (RF), and XGBoost (XGB) models, independently augmented with backward and Lasso feature selection algorithms to develop a set of predictive classifiers. …”

Table 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”

Image 1_Integrated analysis of N-glycosylation and Alzheimer’s disease: identifying key biomarkers and mechanisms.tif by Hao Zhang (15339)

“…Differential expression profiling identified 6,845 DEGs, including TMEM59, MLEC, and MAX. Machine learning algorithms consistently prioritized these three genes as core N-glycosylation-related biomarkers, alongside APP as a key associated molecule. …”

Supplementary file 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”

Table 2_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”

EKC virus-specific HMGB1 secretion. by Amrita Saha (1771837)

“…There were a total of 38,913 positions in the final data set. The evolutionary history was inferred using the Maximum Likelihood method and Tamura-Nei model [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1013184#ppat.1013184.ref105" target="_blank">105</a>]. …”

Annotated dataset of simulated voiding sound for urine flow estimation by Marcos Lazaro Alvarez (20107426)

“…Filename format: [device]_f_0_30s.wav ### Example: - `um_f_0s.wav` - `phone_f_0s.wav` - `oppo_f_0s.wav` ## Purpose The goal of this dataset is to provide a standardized audio repository for the development, training and validation of machine learning algorithms for voiding flow prediction. This enables researchers to: - Benchmark different approaches on a common dataset - Develop flow estimation models using synthetic audio before transferring them to real-world applications - Explore the spectral and temporal structure of urination-related audio signals ## Flow Generation - Pump Used: L600-1F precision peristaltic pump - Flow Range: 1–50 ml/s (based on ICS-reported ranges for male uroflowmetry) - Calibration: Pump flows were validated using a graduated cylinder - Noise Isolation: The pump was placed in a separate room (via 15m silicone tubing) to eliminate pump noise from recordings ## Recording Devices | Device | Sampling Rate | Frequency Range | Description | |---------|----------------|------------------|--------------------------------------| | UM | 192 kHz | 0–96 kHz | High-quality ultrasonic microphone | | Phone | 48 kHz | 0–24 kHz | Android smartphone (Mi A1) | | Watch | 44.1 kHz | 0–22.05 kHz | Oppo Smartwatch with built-in mic | Each recording was carried out using a custom mobile or desktop app with preset parameters. ## Recording Environment - Recordings were made in a bathroom with a standard ceramic toilet containing water at the bottom. - The nozzle height varied between 73–86 cm depending on flow rate to ensure consistent water impact. - Microphone heights: - UM: 84 cm - Phone: 95 cm - Watch: 86 cm (simulating wrist height) ## Data Collection Protocol 1. …”

Data Sheet 1_ARGContextProfiler: extracting and scoring the genomic contexts of antibiotic resistance genes using assembly graphs.pdf by Nazifa Ahmed Moumi (7434359)

“…Several tools, databases, and algorithms are now available to facilitate the identification of ARGs in metagenomic sequencing data; however, direct annotation of short-read data provides limited contextual information. …”

Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-bas... by Mingchen Xie (4325692)

“…This study aims to evaluate the relationship between the TyG index and HCE incidence/clinical outcomes in critically ill patients with DM and to construct a risk prediction model using machine-learning algorithms.</p>Methods<p>This multi-center retrospective investigation leveraged clinical repositories from Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD). …”