Image 13_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…Objective<p>This study explored the interactions between ferroptosis and lipid metabolism in colon cancer, established a prognostic model to elucidate immune microenvironment heterogeneity, and evaluated the prospects of immunotherapy.…”

Table 11_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…Objective<p>This study explored the interactions between ferroptosis and lipid metabolism in colon cancer, established a prognostic model to elucidate immune microenvironment heterogeneity, and evaluated the prospects of immunotherapy.…”

Image 12_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…Objective<p>This study explored the interactions between ferroptosis and lipid metabolism in colon cancer, established a prognostic model to elucidate immune microenvironment heterogeneity, and evaluated the prospects of immunotherapy.…”

Machine learning approach to predict heat transfer and fluid flow characteristics of integrated pin fin-metal foam heat sink by Amitav Tikadar (8608923)

“…Upon testing the prediction and interpolation capability of all considered ML models, ANN outperforms other models, hence, ANN model has been coupled with genetic algorithm (GA) optimizer to identify three different optimized PF-MF heat sink layouts at three different <math>Re<mo>.…”

Coordination of electrical drilling machines in open-pit mines: a constraint programming approach by Mohamed Maftah (22428761)

“…This paper provides a comprehensive problem description and proposes a constraint programming model complemented by a heuristic algorithm. Experimental results demonstrate the model’s effectiveness in scheduling up to 300 tasks with 3 machines over 24 hours, achieving near-optimal solutions within 2 minutes. …”

Data Sheet 1_Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy.docx by Tobias Schraut (21488108)

“…A videoendoscopic model was developed by selecting a suitable classification algorithm and a minimal-optimal subset of glottal parameters. …”

Literature review on Benders cut selection and a multiple cut generation scheme by Timoleon Kaltis (22119860)

“…The proposed approach builds upon the work of Brandenberg and Stursberg (Mathematical Methods of Operations Research, 94:383–412, 2021), who developed a unifying framework for generating Benders cuts by identifying appropriate parametrizations for the cost vector of the objective function used to optimize over the alternative polyhedron. …”

Presentation 1_Development of an AI-driven digital assistance system for real-time safety evaluation and quality control in laparoscopic liver surgery.pptx by Zi-Yang Peng (9616239)

“…We have upgraded an Intelligent Surgical Assistant (ISA) through integrating a redesigned hierarchical recognition algorithm, an expanded surgical dataset, and an optimized real-time intraoperative feedback framework.…”

Image 1_Noninvasive estimation of oxygenation index in pediatric critical care: an independent retrospective observational validation.pdf by Thomas E. Bachman (19262317)

“…Objective<p>To independently validate an empirically optimized algorithm for calculating estimated Oxygenation Index (eOI) using noninvasive parameters from pediatric intensive care populations.…”

Data Sheet 1_Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning.csv by Weijun Zhou (2215300)

“…Additionally, the Shapley Additive Explanation (SHAP) algorithm was utilized to clarify the results of the optimal ML model.…”

Presentation 1_A combined model integrating deep learning, radiomics, and clinical ultrasound features for predicting BRAF V600E mutation in papillary thyroid carcinoma with Hashim... by Peng-Fei Zhu (523674)

“…Feature selection was performed using Pearson’s correlation coefficient, the Minimum Redundancy Maximum Relevance (mRMR) algorithm, and LASSO regression. The optimal algorithm was selected from nine machine learning algorithms for model construction, including the traditional radiomics model (RAD), the deep learning model (DL), and their fusion model (DL_RAD). …”

Data Sheet 1_Machine learning-based ultrasomics for predicting response to tyrosine kinase inhibitor in combination with anti-PD-1 antibody immunotherapy in hepatocellular carcinom... by Yiwen Hu (615619)

“…Objective<p>The objective of this study is to build and verify the performance of machine learning-based ultrasomics in predicting the objective response to combination therapy involving a tyrosine kinase inhibitor (TKI) and anti-PD-1 antibody for individuals with unresectable hepatocellular carcinoma (HCC). …”

Data Sheet 1_Precision dosing of voriconazole in immunocompromised children under 2 years: integrated machine learning and population pharmacokinetic modeling.docx by Li Shen (110858)

“…The models were evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²) to identify the optimal algorithm, which then underwent independent external validation. …”

Table 1_Developing a proactive coping theory-based conceptual framework for sarcopenia management in aging societies: a mixed-methods study from China.docx by Ruiqi Dai (22651169)

“…</p>Discussion<p>This interdisciplinary model synergistically addresses three critical objectives: healthcare resource optimization, social participation longevity enhancement, and disability trajectory modulation. …”

Table 1_Research trends and hotspots evolution of artificial intelligence for cholangiocarcinoma over the past 10 years: a bibliometric analysis.docx by Ke-xie Wang (20718248)

“…Early research primarily focused on traditional treatment methods and care strategies for CCA, but since 2019, there has been a significant shift towards the development and optimization of AI algorithms and their application in early cancer diagnosis and treatment decision-making. …”

Data Sheet 1_A risk prediction model for poor joint function recovery after ankle fracture surgery based on interpretable machine learning.pdf by Congyang Li (10012877)

“…Objective<p>Currently, there is no individualized prediction model for joint function recovery after ankle fracture surgery. …”

Image 1_Recognition of parasitic helminth eggs via a deep learning-based platform.png by Wei He (131453)

“…</p>Discussion<p>The results show that this AI-assisted platform significantly reduces reliance on professional expertise while maintaining real-time efficiency and high accuracy, offering a powerful tool for the diagnosis and treatment of parasitosis. With further optimization, such as expanding training datasets and refining recognition algorithms, this AI system could become a key resource in both clinical and public health efforts to combat parasitic infections.…”

Data Sheet 1_Development and validation of a machine learning model to predict postoperative complications following radical gastrectomy for gastric cancer.doc by Zhenmeng Lin (21564404)

“…</p>Conclusion<p>While the RF model provided optimal predictive accuracy among ML algorithms, the interpretable nomogram offers comparable discrimination and clinical accessibility. …”

Data Sheet 1_Leveraging automated time-lapse microscopy coupled with deep learning to automate colony forming assay.docx by Anusha Klett (20748443)

“…Our automated platform utilizes a deep learning and multi-object tracking approach for colony counting. Brightfield images were used to train a YOLOv8 object detection network, achieving a mAP50 score of 86% for identifying single cells, clusters, and colonies, and 97% accuracy for Z-stack colony identification with a multi-object tracking algorithm. …”

Image 2_Interpretable machine learning models based on multi-dimensional fusion data for predicting positive surgical margins in robot-assisted radical prostatectomy: a retrospecti... by Zhangcheng Liu (21188233)

“…Feature selection was performed using intraobserver and interobserver correlation coefficients (ICCs), low-variance filtering, univariable logistic regression, Spearman’s correlation analysis, the least absolute shrinkage and selection operator (LASSO) algorithm, and the Boruta algorithm. Six ML models were constructed, with performance evaluated using area under the curve (AUC), calibration curves, and decision curve analyses (DCA) to identify the optimal model. …”