Construction of a novel prognostic model based on lncRNAs-related to DNA damage repair for predicting the prognosis of clear cell renal cell carcinoma by Peng Chen (6514)

“…The purpose of this study was to explore the potential value of lncRNAs-related to DNA damage repair (DDR) in predicting the prognosis of ccRCC by construction and verification a novel prognostic model.</p> <p>RNA-seq data and clinical data of ccRCC were downloaded from public databases. …”

Table1_DeepO-GlcNAc: a web server for prediction of protein O-GlcNAcylation sites using deep learning combined with attention mechanism.xlsx by Liyuan Zhang (220168)

“…Recent advancements in deep learning algorithms and the availability of O-GlcNAc proteomics data present an opportunity to improve O-GlcNAc site prediction.…”

Table 2_Unraveling the role of coagulation-related genes in esophageal squamous cell carcinoma: development of a prognostic model and exploration of potential clinical significance... by Langlang Deng (22381786)

“…</p>Methods<p>To investigate this, we integrated various multi-omics datasets, including mRNA expression data from TCGA and GEO, single-cell RNA sequencing data, as well as DNA mutation and methylation profiles. …”

Table 1_Unraveling the role of coagulation-related genes in esophageal squamous cell carcinoma: development of a prognostic model and exploration of potential clinical significance... by Langlang Deng (22381786)

“…</p>Methods<p>To investigate this, we integrated various multi-omics datasets, including mRNA expression data from TCGA and GEO, single-cell RNA sequencing data, as well as DNA mutation and methylation profiles. …”

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

“…Through the computation of the indicator function, model identifiability and sample complexity are defined and their properties are described for different data-generating processes, ranging from deterministic to ergodic stationary stochastic processes. …”

Context tree classification and clustering by Adriano Zanin Zambom (20448557)

“…<p>In this manuscript, we develop clustering and classification algorithms for Context trees arising from Variable Length Markov Chains (VLMC). …”

Table3_A real-world pharmacovigilance analysis of eslicarbazepine acetate using the FDA adverse events reporting system (FAERS) database from 2013 (Q4) to 2024 (Q1).docx by Huafei Tang (19710232)

“…</p>Methods<p>By extracting all available data since the FDA approval of ESL (2013Q4-2024Q1), disproportionality analysis was performed using reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN) and multi-item gamma Poisson shrinker (MGPS) algorithms. …”

Table1_A real-world pharmacovigilance analysis of eslicarbazepine acetate using the FDA adverse events reporting system (FAERS) database from 2013 (Q4) to 2024 (Q1).XLSX by Huafei Tang (19710232)

“…</p>Methods<p>By extracting all available data since the FDA approval of ESL (2013Q4-2024Q1), disproportionality analysis was performed using reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN) and multi-item gamma Poisson shrinker (MGPS) algorithms. …”

Table2_A real-world pharmacovigilance analysis of eslicarbazepine acetate using the FDA adverse events reporting system (FAERS) database from 2013 (Q4) to 2024 (Q1).XLSX by Huafei Tang (19710232)

“…</p>Methods<p>By extracting all available data since the FDA approval of ESL (2013Q4-2024Q1), disproportionality analysis was performed using reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN) and multi-item gamma Poisson shrinker (MGPS) algorithms. …”

Polyanion sodium cathode materials dataset by Martin Hoffmann Petersen (13626778)

“…An example of how to extract data and plot the physical properties is provided in https://github.com/dtu-energy/cathode-generation-workflow/tree/main/extract_data/read_data.py and https://github.com/dtu-energy/cathode-generation-workflow/tree/main/extract_data/utils.py contains two functions, one used to attached Bader charges to an ASE atom object an another to combine multiple XYZ data files.…”

Methodology block diagram. by Gahao Chen (21688843)

“…Current machine learning (ML) models demonstrate suboptimal predictive performance in KD treatment response prediction, primarily due to their limited ability to effectively process categorical variables and interpret tabular clinical data. …”

Figures and Tables by Divya C D (22799186)

“…Robots Comput. Vision XXXI: Algorithms and Techniques, Burlingame, CA, USA, Jan. 23–24, 2012.…”

CANDID-II Dataset by Sijing Feng (10187216)

“…This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.</p><p dir="ltr">Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID II dataset is to be accessed by the external users. …”

Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Image 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Image 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Image 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”