Image 4_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.tif by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Table 3_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.xlsx by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Image 6_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.tif by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Image 1_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.tif by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Image 2_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.tif by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Image 5_Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer.tif by Ye Tian (220278)

“…A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.…”

Data Sheet 1_Multi-omics analysis and metastasis risk factor prediction in N1b stage PTMC: insights into immune infiltration and therapeutic implications.docx by Hao Dai (4277254)

The research framework. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”

Optimization outcome for the elongation problem. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”

IEEE Big Data 2024 Presentation: Norma: A Framework for Finding Threshold Associations Between Continuous Variables Using Point-wise Function by Md Mahin (21434966)

“…It employs a novel interestingness function, which measures agreement with respect to hotspots where variable pointwise functions exceed associated thresholds. …”

Enhancements to SarcGraph’s z-disc segmentation pipeline. by Saeed Mohammadzadeh (13492285)

The modified SarcGraph pipeline. by Saeed Mohammadzadeh (13492285)

This figure presents the performance of a Support Vector Regression (SVR) model on predicting cellular structural organization scores. by Saeed Mohammadzadeh (13492285)

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 by Robert Zomer (12796235)

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). …”

Data generation process. by Nicola Dietler (12551766)

Noninvasive identification of metabolic dysfunction–associated steatohepatitis (INFORM MASH): a retrospective cohort and disease modeling study by G. Craig Wood (7522025)

“…</p> <p>Patients aged ≥18 with electronic health record (EHR) documented liver function tests and liver biopsies between 2016 and 2021 were retrospectively identified from the Geisinger Health System Research Liver Registry. …”

Recursive-Expansive Dynamics Frameworks Formalization with Negation Isolation and Inverse Zero Operators by Del Bel, Julian (21403013)

From timetable spaces to networks. by Fabio Lamanna (4961875)