Hospital (A) accesses the B-UMCS. by Albatoul Almohana (20370514)

“…Most existing works utilized blockchain technology within a specific healthcare entity’s services but not among several healthcare organizations. …”

Hospital (A) sends the CR to Hospital (C). by Albatoul Almohana (20370514)

“…Most existing works utilized blockchain technology within a specific healthcare entity’s services but not among several healthcare organizations. …”

MOH reviews all hospital requests. by Albatoul Almohana (20370514)

“…Most existing works utilized blockchain technology within a specific healthcare entity’s services but not among several healthcare organizations. …”

Hospital (A) views the received messages. by Albatoul Almohana (20370514)

“…Most existing works utilized blockchain technology within a specific healthcare entity’s services but not among several healthcare organizations. …”

Forest plots for safety outcomes related to symptoms. by Reina Tonegawa-Kuji (20724254)

Forest plots for safety outcomes. by Reina Tonegawa-Kuji (20724254)

Forest plots for change in Clinical Dementia Rating-Sum of Boxes (CDR-SB). by Reina Tonegawa-Kuji (20724254)

Forest plots for change in Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog). by Reina Tonegawa-Kuji (20724254)

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

A. Identification of six co-expressed gene modules with an average size of 318. by Nooshin Ghahramani (11538154)

“…Heat map of the TOM showing the degree of interconnectedness among genes within the modules identified by the dynamic tree cutting algorithm. …”

S1 Graphical abstract - by José M. Rivera-Arbeláez (12418512)

“…<div><p>Engineered heart tissues (EHTs) have shown great potential in recapitulating tissue organization, functions, and cell-cell interactions of the human heart <i>in vitro</i>. …”

Data. by Naiara Virto (20490408)

“…</p><p>Results</p><p>The predictive models used in this study exhibited AUC scores of 0.7671 for MQI and 0.7649 for sarcopenia, with the most successful algorithms being SVM and MLP. Key factors in predicting both conditions have been shown to be relative power, age, weight, and the 5STS. …”

PhAMEs group into clusters of structurally related proteins. by Federico G. Mirkin (22615287)

Gene expression prognosis prediction Comparison. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

LGWO-BP research flowchart. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

miRNA expression prognosis prediction Comparison. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

Pearson correlation heatmap of Gene expression. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

Grey wolf optimizer schematic diagram. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

DNAmethylation prognosis prediction Comparison. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

Outcomes of unimodal benchmarking assessments. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”