Image 6_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg by Xingbiao Chen (22481431)

“…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …”

Image 9_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg by Xingbiao Chen (22481431)

“…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …”

DATASET AI by Elena Stamate (18836305)

“…Performance metrics include accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC).</p><p dir="ltr">All data have been de-identified and processed in accordance with institutional ethical standards.…”

Image 5_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg by Xingbiao Chen (22481431)

“…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …”

Image 4_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg by Xingbiao Chen (22481431)

“…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …”

Table 1_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.xls by Xingbiao Chen (22481431)

“…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …”

Image 2_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg by Xingbiao Chen (22481431)

“…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …”

An open-pit mine segmentation dataset for deep learning by Lin Gang (18600526)

“…It was developed through a systematic process. Firstly, by conducting comprehensive literature research, the Point of Interest (POI) data of open-pit mines was summarized. …”

Image 1_Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning.tif by Chenbo Yang (10004575)

“…In conclusion, based on the winter wheat ChD data set and the corresponding canopy hyperspectral data set, combined with 3 FOD calculation methods, 1 band screening method, and 8 modeling algorithms, this study constructed hyperspectral monitoring models for winter wheat ChD. …”

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

“…</p>Methods<p>Multi-omics data from the TCGA and GEO databases were integrated, and strict data preprocessing was carried out. …”

Image1_DeepO-GlcNAc: a web server for prediction of protein O-GlcNAcylation sites using deep learning combined with attention mechanism.pdf 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.…”

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. …”