A three stage unsupervised learning pipeline for data analysis. by Alberto García-Rodríguez (18019079)

“…<p>The first stage involves data preprocessing, where the dataset undergoes standardization, normalization, and cleaning procedures to address missing values and noise. …”

Architecture of Swin-T model. by Wen-Qing Huang (5258126)

“…This enhancement strategy involves integrating feature map labels at each layer of the model to reduce computational complexity and enhance model lightweightness. …”

Model the experimental results curve. by Wen-Qing Huang (5258126)

“…This enhancement strategy involves integrating feature map labels at each layer of the model to reduce computational complexity and enhance model lightweightness. …”

Results of comparison experiments. by Wen-Qing Huang (5258126)

“…This enhancement strategy involves integrating feature map labels at each layer of the model to reduce computational complexity and enhance model lightweightness. …”

Architecture of Swin Transformer Block. by Wen-Qing Huang (5258126)

“…This enhancement strategy involves integrating feature map labels at each layer of the model to reduce computational complexity and enhance model lightweightness. …”

Disease distribution map of the GZDL-BD. by Wen-Qing Huang (5258126)

“…This enhancement strategy involves integrating feature map labels at each layer of the model to reduce computational complexity and enhance model lightweightness. …”

Token merging module. by Wen-Qing Huang (5258126)

“…This enhancement strategy involves integrating feature map labels at each layer of the model to reduce computational complexity and enhance model lightweightness. …”

Comparative results of the ablation experiments. by Wen-Qing Huang (5258126)

“…This enhancement strategy involves integrating feature map labels at each layer of the model to reduce computational complexity and enhance model lightweightness. …”

Image 1_A questionnaire for a conceptual framework and interdisciplinary public health research using the Delphi technique—development and validation.jpg by Anita Alaze (13378406)

“…</p>Conclusion<p>To address challenges in the development process, interdisciplinary researchers need to be involved. …”

Table 1_A questionnaire for a conceptual framework and interdisciplinary public health research using the Delphi technique—development and validation.docx by Anita Alaze (13378406)

“…</p>Conclusion<p>To address challenges in the development process, interdisciplinary researchers need to be involved. …”

Multisession fNIRS-EEG data of Post-Stroke Motor Recovery: Recordings During Intact and Paretic Hand Movements by Aleksandra Medvedeva (21221383)

“…</p><p dir="ltr">1)The subject identifier</p><p dir="ltr">2)The side of the paretic limb (R for right, L for left)</p><p dir="ltr">3)The session number (with session 1 representing the first day of rehabilitation and session 6 corresponding to the period immediately preceding patient withdrawal)</p><p dir="ltr">4)The target hand involved in the motor task.</p><p dir="ltr"><br></p><p dir="ltr">Each file contains channel position coordinates and metadata detailing the acquisition process and experimental events. …”

S1 File - by Constantino A. García (20347872)

“…Especially for large studies, this process is tedious and error-prone. This paper presents <i>RHRVEasy</i>, an R open-source package that automates all the steps of HRV analysis. …”

Experimental databases. by Constantino A. García (20347872)

“…Especially for large studies, this process is tedious and error-prone. This paper presents <i>RHRVEasy</i>, an R open-source package that automates all the steps of HRV analysis. …”

Prototype of the main RHRVEasy function. by Constantino A. García (20347872)

“…Especially for large studies, this process is tedious and error-prone. This paper presents <i>RHRVEasy</i>, an R open-source package that automates all the steps of HRV analysis. …”

Non-linear HRV indices included in <i>RHRVEasy</i>. by Constantino A. García (20347872)

“…Especially for large studies, this process is tedious and error-prone. This paper presents <i>RHRVEasy</i>, an R open-source package that automates all the steps of HRV analysis. …”

Development and characterization of a cell-type-specific gene expression reference panel from single-nucleus RNA sequencing (snRNAseq). by Brian Gural (21781982)

“…<p><b>(A)</b> Generation of the reference panel involved identifying five major cell types in snRNAseq data from pooled nuclei of left ventricles of untreated C57BL/6J mice. …”

Supplementary file 1_Exploring the molecular characteristics of inflammatory bowel disease from the perspective of hypoxia-related genes.docx by Lei Zheng (106363)

“…Hypoxia-related genes (HRGs) were extracted from the GeneCards database. Data preprocessing involved mitigating batch effects via the sva package and normalizing with the limma package. …”

Supplementary file 1_Comprehensive analysis of phagocytosis regulatory genes in bladder cancer: implications for prognosis and immunotherapy.xlsx by Xueming Ma (2119150)

“…Phagocytosis regulatory genes (PRGs) are involved in regulating the immune response against tumor cells, and in-depth research on them in bladder cancer is extremely urgent.…”

Data Sheet 7_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

“…</p>Methods<p>Data were collected from 38 Chinese institutions, including 4,244 patients visiting outpatient rehabilitation clinics. Data processing was conducted using Python software. The pandas library was used for data cleaning and preprocessing, involving 68 categorical and 12 continuous variables. …”

Data Sheet 2_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

“…</p>Methods<p>Data were collected from 38 Chinese institutions, including 4,244 patients visiting outpatient rehabilitation clinics. Data processing was conducted using Python software. The pandas library was used for data cleaning and preprocessing, involving 68 categorical and 12 continuous variables. …”