Data Sheet 1_Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer.zip by Lei Shen (76320)

“…Additionally, various computational algorithms were employed to evaluate the immunotherapeutic responses of different risk groups. …”

Image 4_Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer.tif by Lei Shen (76320)

“…Additionally, various computational algorithms were employed to evaluate the immunotherapeutic responses of different risk groups. …”

Data Sheet 1_CU Cilia – an application for image analysis by machine learning – reveals significance of cysteine cathepsin K activity for primary cilia of human thyroid epithelial... by Maren Rehders (2879318)

“…</p>Results<p>Therefore, we developed CU Cilia, i.e., a method for the detection of primary cilia and for segmentation of nuclei by machine learning-based image analysis algorithms. …”

Image 3_Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer.tif by Lei Shen (76320)

“…Additionally, various computational algorithms were employed to evaluate the immunotherapeutic responses of different risk groups. …”

Table 1_Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer.xlsx by Lei Shen (76320)

“…Additionally, various computational algorithms were employed to evaluate the immunotherapeutic responses of different risk groups. …”

Image 1_Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer.tif by Lei Shen (76320)

“…Additionally, various computational algorithms were employed to evaluate the immunotherapeutic responses of different risk groups. …”

Machine Learning-Driven Methods for Nanobody Affinity Prediction by Hua Feng (234718)

“…In the current study, 12 machine learning algorithms were compared in parallel to explore the potential patterns between Nb–ligand affinity and eight noncovalent interactions. …”

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. …”

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.…”

Image 1_Integrated transcriptomics and machine learning reveal REN as a dual regulator of tumor stemness and NK cell evasion in Wilms tumor progression.tif by Qingfei Cao (12062217)

“…A novel Cancer Stemness Prognostic Index (CSPI) was developed using machine learning algorithms to stratify WT patients by risk and histological subtype. …”

Data Sheet 2_Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.pdf by Yi Zhang (9093)

“…Background<p>Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.…”

Data Sheet 1_Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.pdf by Yi Zhang (9093)

“…Background<p>Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.…”

Table 1_Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.xlsx by Yi Zhang (9093)

“…Background<p>Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.…”

Abbreviations used in the text. by Lorenzo Ruinelli (13014138)

“…Machine Learning (ML) algorithms were developed with 10-fold cross-validation, and diagnostic accuracy was evaluated.…”

DataSheet1_Assessing sepsis-induced immunosuppression to predict positive blood cultures.pdf by Enrique Hernández-Jiménez (559505)

“…Although not widely accepted, several clinical and artificial intelligence-based algorithms have been recently developed to predict bacteremia. …”

CSPP instance by peixiang wang (19499344)

“…</b></p><p dir="ltr">Its primary function is to create structured datasets that simulate container terminal operations, which can then be used for developing, testing, and benchmarking optimization algorithms (e.g., for yard stacking strategies, vessel stowage planning).…”

Table 1_Integrated transcriptomics and machine learning reveal REN as a dual regulator of tumor stemness and NK cell evasion in Wilms tumor progression.xlsx by Qingfei Cao (12062217)

“…A novel Cancer Stemness Prognostic Index (CSPI) was developed using machine learning algorithms to stratify WT patients by risk and histological subtype. …”