Table 1_An immunotherapy guide constructed by cGAS-STING signature for breast cancer and the biofunction validation of the pivotal gene HOXC13 via in vitro experiments.docx by Zhenling Dai (22011134)

Image 2_An immunotherapy guide constructed by cGAS-STING signature for breast cancer and the biofunction validation of the pivotal gene HOXC13 via in vitro experiments.tif by Zhenling Dai (22011134)

Image 1_An immunotherapy guide constructed by cGAS-STING signature for breast cancer and the biofunction validation of the pivotal gene HOXC13 via in vitro experiments.tif by Zhenling Dai (22011134)

Comparative Performance Metrics of AESs. by Tianhui Chen (3044838)

“…Results revealed that two systems demonstrated strong agreement with human ratings, while the third exhibited systematic score inflation, likely due to algorithmic discrepancies and limited consideration of nuanced language features. …”

Score Distributions by Rater. by Tianhui Chen (3044838)

“…Results revealed that two systems demonstrated strong agreement with human ratings, while the third exhibited systematic score inflation, likely due to algorithmic discrepancies and limited consideration of nuanced language features. …”

Descriptive Statistics for Human Raters and AESs. by Tianhui Chen (3044838)

“…Results revealed that two systems demonstrated strong agreement with human ratings, while the third exhibited systematic score inflation, likely due to algorithmic discrepancies and limited consideration of nuanced language features. …”

Source code for data mining from Apache JIRA project. by Susmita Haldar (17707125)

Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone by Michael J. Pasterski (11726741)

“…We use unsupervised ML on scanning electron microscopy–electron dispersive spectroscopy (SEM-EDS) measurements to define compositional categories based on differences in elemental abundances. We then test the ability of four ML algorithmsk-nearest neighbors (KNN), recursive partitioning and regressive trees (RPART), eXtreme gradient boost (XGBoost), and random forest (RF)to classify the ToF-SIM spectra using (1) the categories assigned via SEM-EDS, (2) organic and inorganic labels assigned via SEM-EDS, and (3) the presence or absence of detectable steranes in ToF-SIMS spectra. …”

TreeMap 2016 Stand Size Code Field (Image Service) by U.S. Forest Service (17476914)

“…<div>TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.…”

G4SNVHunter workflow for identifying variants that affect G4 formation. by Rongxin Zhang (1618159)

Enhancing the Robustness of Vehicle Re-Identification in Intelligent Transportation Systems by Mei Qiu (21081170)

Code and data for evaluating oil spill amount from text-form incident information by Yiming Liu (18823387)

“…<h2>Dataset and Code Description</h2><p dir="ltr">This repository includes the code and data for evaluating oil spill amounts from incident textual information. …”

Supporting data for "Interpreting complex ecological patterns and processes across differentscales using Artificial Intelligence" by Yifei Gu (9507104)

“…<p dir="ltr">This thesis bridges the gap between field observations and the empirical understanding of ecological systems by applying AI models at different spatial scales and hierarchical levels to study ecological complexity. The detailed implementation, source code and demo dataset are included in dedicated folders for each chapter.…”

How do drones fit into multimodal sensing networks? by Jenna Kline (22316425)

Uniform Manifold Approximation and Projection (UMAP)-based sorting enhances detection of low-firing rate neurons and boosts mutual information. by Daniel Suárez-Barrera (22676654)

Description of data sources. by Gowtham A. Rao (8261928)

“…<div><p>Objective</p><p>This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.…”

Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. by Daniel Suárez-Barrera (22676654)

Data and codes for the paper "Machine-Learning-Enabled Spatial Pattern Mining: Evaluating the Impact of Imperfect Inputs". by Zhili Li (18836803)

High-Throughput Mass Spectral Library Searching of Small Molecules in R with NIST MSPepSearch by Andrey Samokhin (20282728)

“…Despite the availability of numerous library search algorithms, those developed by NIST and implemented in MS Search remain predominant, partly because commercial databases (e.g., NIST, Wiley) are distributed in proprietary formats inaccessible to custom code. …”

SpeLL: An Agent for Natural Language-Driven Intelligent Spectral Modeling by Jiashun Fu (20888176)

“…The core strength of SpeLL lies in its dual RAG pathways. The Code RAG provides specialized code knowledge for spectral data analysis, enabling the LLM to generate robust and domain-specific analytical scripts that address the implementation and optimization of algorithms. …”