The flowchart of GWO-VMD method. by Zhenjing Yao (22189970)

“…Then, the decomposed effective intrinsic mode functions (IMFs) are extracted to separate and suppress random noises. …”

The 147th single trace. by Zhenjing Yao (22189970)

“…Then, the decomposed effective intrinsic mode functions (IMFs) are extracted to separate and suppress random noises. …”

Supplementary file 1_Unraveling the bacterial composition of a coral and bioeroding sponge competing in a marginal coral environment.docx by Sambhaji Mote (6311663)

“…This study focuses on the coral Turbinaria mesenterina and sponge C. thomasi, both known for their distinct symbiotic associations with Symbiodiniaceae. …”

Data Sheet 1_Mycobiome analysis of leaf, root, and soil of symptomatic oil palm trees (Elaeis guineensis Jacq.) affected by leaf spot disease.pdf by Abiodun Abeeb Azeez (20393835)

“…Additionally, a large proportion of the identified keystone species consisted of rare taxa, suggesting potential role in ecosystem functions. Surprisingly both AS and SS leaf samples shared taxa previously associated with oil palm leaf spot disease. …”

Image 1_Mitochondrial insights: key biomarkers and potential treatments for diabetic nephropathy and sarcopenia.tif by Yi Wei Chen (21684728)

“…Utilizing four machine learning algorithms (LASSO, SVM, XGBoost, RF), we pinpointed three mitochondrial hub genes. …”

DataSheet2_Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis... by Chenghui Cao (12472029)

“…To screen for biomarkers for diagnosis, we applied machine learning algorithm to identify hub genes and constructed a clinical predictive model. …”

Table 1_Integrated transcriptomic and network analysis reveals candidate immune–metabolic biomarkers in children with the inattentive type of ADHD.xlsx by Qiaoyan Shao (22357861)

“…The ADHD-I molecular landscape was explored through functional enrichment, immune cell profiling, pharmacological screening, and ligand-receptor interaction modeling.…”

Image 1_Integrated transcriptomic and network analysis reveals candidate immune–metabolic biomarkers in children with the inattentive type of ADHD.tif by Qiaoyan Shao (22357861)

“…The ADHD-I molecular landscape was explored through functional enrichment, immune cell profiling, pharmacological screening, and ligand-receptor interaction modeling.…”

Table 2_Integrated transcriptomic and network analysis reveals candidate immune–metabolic biomarkers in children with the inattentive type of ADHD.xlsx by Qiaoyan Shao (22357861)

“…The ADHD-I molecular landscape was explored through functional enrichment, immune cell profiling, pharmacological screening, and ligand-receptor interaction modeling.…”

DataSheet1_Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis... by Chenghui Cao (12472029)

“…To screen for biomarkers for diagnosis, we applied machine learning algorithm to identify hub genes and constructed a clinical predictive model. …”

A New Control Approach for a Multi-Controlled Wheelchair Utilizing Deep Learning Framework and Raspberry Pi by Sudipta Chatterjee (536333)

“…Employing a deep learning algorithm, it performs real-time object detection and tracks its current position in various environmental conditions. …”

Supplementary file 1_OncoPSM: an interactive tool for cost-effectiveness analysis using partitioned survival models in oncology trial.xlsx by Xulong Qiu (22123102)

“…</p>Methods<p>We extracted data from Kaplan-Meier (KM) curves, reconstructed individual patient data (IPD) using an iterative KM algorithm, and fitted parametric survival functions to the IPD data. …”

Table 2_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.xls by Haoxue Zhang (12208580)

“…</p>Conclusion<p>WDR77 serves as both a prognostic biomarker and functional regulator in melanoma, highlighting its potential as a therapeutic target.…”

Table 3_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.xlsx by Haoxue Zhang (12208580)

“…</p>Conclusion<p>WDR77 serves as both a prognostic biomarker and functional regulator in melanoma, highlighting its potential as a therapeutic target.…”

Table 1_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.xlsx by Haoxue Zhang (12208580)

“…</p>Conclusion<p>WDR77 serves as both a prognostic biomarker and functional regulator in melanoma, highlighting its potential as a therapeutic target.…”

Data Sheet 2_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.zip by Haoxue Zhang (12208580)

“…</p>Conclusion<p>WDR77 serves as both a prognostic biomarker and functional regulator in melanoma, highlighting its potential as a therapeutic target.…”

Data Sheet 1_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.zip by Haoxue Zhang (12208580)

“…</p>Conclusion<p>WDR77 serves as both a prognostic biomarker and functional regulator in melanoma, highlighting its potential as a therapeutic target.…”

Uncertainty and Novelty in Machine Learning by Derek Scott Prijatelj (20364288)

“…</p> <p>This work answers these questions through both theory and application. We provide a Bayesian evaluation framework for subjective tasks where different sources of uncertainty are considered and the truth itself is uncertain. …”

Particle swarm optimization method for energy management of the hybrid system of an electric vehicle charging station by Olukorede Adenuga (20139099)

“…The PSO provide the best value for uncertainty cost functions for both RESs and electric vehicle charging stations considering active power loss, reactive <a href="" target="_blank">power loss </a>operation cost, power flow, and voltage deviation in the thesis. …”

Supporting data for "Machine learning based prognosis prediction of intracerebral hemorrhage outcome". by Eunhye Yang (17853260)

“…To support timely clinical decision-making, numerous prognosis scoring systems have been established, yet no scores have been found to be accurate in predicting both mortality and morbidity. Therefore, using 957 spontaneous ICH patient samples provided by the University of Hong Kong stroke registry, this study aims to utilize machine learning (ML) tools to develop a prognosis system that effectively predicts six-month outcomes of both mortality and morbidity in ICH patients. …”