Summary of four signal detection algorithms. by Wangyu Ye (17616256)

The set of sleep measures computed by ActiLife, including definitions for each measure and the aggregation methods utilized when multiple sleep periods were detected within the sam... by Louis Faust (6807728)

Control Parameters of IRSA Algorithm. by Wan-Hua Zhang (21601139)

“…This improvement aims to improve the network performance and accuracy in diabetes detection. In the study, the IRSA-BP hybrid algorithm and many other machine learning algorithms were used for diabetes prediction, and the algorithm performance was comprehensively evaluated using multiple classification metrics. …”

UMAFall: Fall Detection Dataset (Universidad de Malaga) by Eduardo Casilari (3333906)

“…The files contain the mobility traces generated by a group of 19 experimental subjects that emulated a set of predetermined ADL (Activities of Daily Life) and falls. The traces are aimed at evaluating fall detection algorithms.…”

Data Sheet 1_The curvilinear associations between Life’s Crucial 9 and frailty: cross-sectional study of NHANES 2003 - 2023.docx by Bo Wang (86769)

“…</p>Methods<p>We used a weighted multiple logistic regression model to evaluate the relationship between Life’s Essential 8 (LE8) and LC9 with frailty, and conducted trend tests to assess the stability of this association. …”

An iterative plot of the Cubic chaotic map. by Wan-Hua Zhang (21601139)

“…This improvement aims to improve the network performance and accuracy in diabetes detection. In the study, the IRSA-BP hybrid algorithm and many other machine learning algorithms were used for diabetes prediction, and the algorithm performance was comprehensively evaluated using multiple classification metrics. …”

Description of the dataset features. by Wan-Hua Zhang (21601139)

“…This improvement aims to improve the network performance and accuracy in diabetes detection. In the study, the IRSA-BP hybrid algorithm and many other machine learning algorithms were used for diabetes prediction, and the algorithm performance was comprehensively evaluated using multiple classification metrics. …”

Number of each target category. by Wan-Hua Zhang (21601139)

“…This improvement aims to improve the network performance and accuracy in diabetes detection. In the study, the IRSA-BP hybrid algorithm and many other machine learning algorithms were used for diabetes prediction, and the algorithm performance was comprehensively evaluated using multiple classification metrics. …”

Correlation matrix. by Wan-Hua Zhang (21601139)

“…This improvement aims to improve the network performance and accuracy in diabetes detection. In the study, the IRSA-BP hybrid algorithm and many other machine learning algorithms were used for diabetes prediction, and the algorithm performance was comprehensively evaluated using multiple classification metrics. …”

BP neural network architecture diagram. by Wan-Hua Zhang (21601139)

“…This improvement aims to improve the network performance and accuracy in diabetes detection. In the study, the IRSA-BP hybrid algorithm and many other machine learning algorithms were used for diabetes prediction, and the algorithm performance was comprehensively evaluated using multiple classification metrics. …”

Data processing flow diagram. by Carl D. Snyder (20868650)

Ig-domains templates of Table 1 in Excel format. by Caesar Tawfeeq (21087272)

“…<div><p>The Immunoglobulin fold (Ig-fold) is found in proteins from all domains of life and represents the most populous fold in the human genome, with current estimates ranging from 2 to 3% of protein coding regions. …”

2025-01-14 - PAG Talk - Kmerseek - Olga Botvinnik - Seanome by Olga Botvinnik (20571431)

Table 1_Identification of crosstalk genes and diagnostic biomarkers in systemic sclerosis associated sarcopenia through integrative analysis and machine learning.docx by Yanfang Wu (206076)

“…Immune infiltration analysis revealed significant correlations between CGs and multiple immune cell populations.</p>Conclusion<p>This study proposes NOX4 and NEK6 as novel biomarkers, offering a non-invasive strategy for the early detection of SSc-associated sarcopenia. …”

Image 2_Development and application of machine learning models for hematological disease diagnosis using routine laboratory parameters: a user-friendly diagnostic platform.jpeg by Jingya Liu (13338460)

“…Early diagnosis and detection of hematological diseases are very important to improve the quality of life and prognosis of patients.…”

Data Sheet 1_Development and application of machine learning models for hematological disease diagnosis using routine laboratory parameters: a user-friendly diagnostic platform.doc... by Jingya Liu (13338460)

“…Early diagnosis and detection of hematological diseases are very important to improve the quality of life and prognosis of patients.…”

Image 1_Development and application of machine learning models for hematological disease diagnosis using routine laboratory parameters: a user-friendly diagnostic platform.jpeg by Jingya Liu (13338460)

“…Early diagnosis and detection of hematological diseases are very important to improve the quality of life and prognosis of patients.…”

DataSheet2_Identification and validation of diagnostic biomarkers and immune cell abundance characteristics in Staphylococcus aureus bloodstream infection by integrative bioinforma... by Junhong Shi (14378839)

“…First, after overlapping the differentially expressed genes (DEGs) in S. aureus infection samples from GSE33341-human and GSE33341-mice samples, we detected 63 overlapping genes. Subsequently, the hub genes including DRAM1, PSTPIP2, and UPP1 were identified via three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. …”

Presentation1_Identification and validation of diagnostic biomarkers and immune cell abundance characteristics in Staphylococcus aureus bloodstream infection by integrative bioinfo... by Junhong Shi (14378839)

“…First, after overlapping the differentially expressed genes (DEGs) in S. aureus infection samples from GSE33341-human and GSE33341-mice samples, we detected 63 overlapping genes. Subsequently, the hub genes including DRAM1, PSTPIP2, and UPP1 were identified via three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. …”

DataSheet1_Identification and validation of diagnostic biomarkers and immune cell abundance characteristics in Staphylococcus aureus bloodstream infection by integrative bioinforma... by Junhong Shi (14378839)

“…First, after overlapping the differentially expressed genes (DEGs) in S. aureus infection samples from GSE33341-human and GSE33341-mice samples, we detected 63 overlapping genes. Subsequently, the hub genes including DRAM1, PSTPIP2, and UPP1 were identified via three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. …”