Optimizing Neuronal Calcium Flux Analysis: A Python Framework for Alzheimer's and TBI Studies by Huiying Huang (490768)

“…The code checks overlap between dead and alive cells, detects the shockwave frame, and validates calcium intensity data. …”

Table 1_WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.xlsx by Yi-Ran Wang (5938265)

“…We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). …”

Image 1_WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.tif by Yi-Ran Wang (5938265)

“…We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). …”

Table 2_WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.docx by Yi-Ran Wang (5938265)

“…We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). …”

Table 2_Multiparametric-MRI habitat radiomics analysis for discriminating pathological types of brain metastases.xlsx by Jinling Zhu (15920747)

“…</p>Materials and methods<p>Pre-treatment MR images from 328 BMs patients at a single center were retrospectively collected and randomly divided into a training set (229 cases) and a test set (99 cases). Tumor regions were manually segmented on contrast-enhanced T1-weighted images (CE-T1WI), and the K-means clustering algorithm was employed to classify the tumor into four distinct sub-regions. …”

Table 1_Multiparametric-MRI habitat radiomics analysis for discriminating pathological types of brain metastases.docx by Jinling Zhu (15920747)

“…</p>Materials and methods<p>Pre-treatment MR images from 328 BMs patients at a single center were retrospectively collected and randomly divided into a training set (229 cases) and a test set (99 cases). Tumor regions were manually segmented on contrast-enhanced T1-weighted images (CE-T1WI), and the K-means clustering algorithm was employed to classify the tumor into four distinct sub-regions. …”

Data Sheet 1_Use of machine learning models to predict mortality in dialysis patients.pdf by Junmin Huang (2431492)

“…</p>Methods<p>This retrospective study included data from 538 maintenance hemodialysis patients (2018.1–2023.12), with 70% used for training and 30% for testing. Each model underwent hyperparameter optimization based on three performance metrics (accuracy, F1-score, and ROC Area Under the Curve [AUC]) to evaluate the impact of different clinical priorities.…”

Optical Tactile (TacTip) Dataset for texture classification by Dexter Shepherd (13238508)

“…We have two parts of this dataset "X_data_15" and "X_data_gel_15". The first one is a sensor that uses clear silicone, and the second makes use of a clear gel. …”

Data Sheet 1_Better performance of cerebral blood volume images synthesized from arterial spin labeling and standard MRI in separating glioblastoma recurrence from treatment respon... by Danyang Wu (6751385)

“…In 96 patients suspected of glioblastoma recurrence vs. treatment response as the external test set from a hospital-based cohort, the difference in the additive value between synthetic CBV maps and ASL to standard MRIs was examined using the Z test. …”

Table 1_Composition-centered prediction of kenaf core saccharification for next-generation bioethanol via machine learning.docx by Yitong Niu (22658927)

“…The curated dataset (n = 35) was used to train Random-Forest regressors tuned by six hyperparameter optimizers (grid search, random search, Bayesian optimization, genetic algorithm, particle swarm optimization, and simulated annealing). …”

Machine vision system for quantification of aortic and pulmonic valvuloplasty catheter compliance by Jiazhe Tang (17596080)

“…Upon ballon inflation, the defocused image is then refocused though passive focusing algorithms used to identify the best focal position. …”

Data Sheet 1_Mapping soil salinity using machine learning and remote sensing data in semi-arid croplands.docx by Abdelwahed Chaaou (22686404)

“…Four ML algorithms, Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Regressor (SVR), and Multi-Layer Perceptron (MLP) were tested. …”

PEG neurons encoded more complex features than A1 neurons. by Shoutik Mukherjee (18626028)

“…GMMs were fit using a boosting algorithm with large-covariance weak learners. The energy in CortSTRFs and STRFs of PEG neurons was more dispersed than those of A1 neurons (CortSTRF: <i>p</i> < 0.001, STRF: <i>p</i> = 0.002; Wilcoxon rank sum test). …”

Supplementary file 1_Healthcare deprivation matters: a novel framework to unveil the influencing mechanisms of aging anxiety and healthcare utilization.docx by Zhiyi Luo (22424863)

“…The random forest algorithm is used to estimate the marginal effect of aging anxiety on healthcare utilization. …”

Data Sheet 1_Latent class analysis and machine learning for clinical subtyping prediction and differentiation in suspected neurosyphilis patients.pdf by Sirui Wu (22559208)

“…Key predictive variables were selected using LASSO regression and Boruta algorithm. Six machine learning algorithms were employed to build LCA subtype prediction models. …”

Data Sheet 1_Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.csv by Zohreh Javanmard (20270430)

“…</p>Conclusion<p>The findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions. …”

Table 1_Predicting emotional responses in interactive art using Random Forests: a model grounded in enactive aesthetics.xlsx by Xiaowei Chen (200607)

“…Model evaluation was conducted using cross-validation and held-out test sets, applying classification and regression metrics to assess performance.…”

Table 1_Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.docx by Zohreh Javanmard (20270430)

“…</p>Conclusion<p>The findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions. …”

Table 2_Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.docx by Zohreh Javanmard (20270430)

“…</p>Conclusion<p>The findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions. …”

<b>Supporting data for "CT Radiomics and Deep Learning Auto-segmentation in Epithelial Ovarian Carcinoma Treatment Response and Prognosis Evaluation"</b> by Mengge He (11085414)

“…</p><p dir="ltr">Second study aimed to develop a DL algorithm in segmentation of omental metastases(OM) of EOC based on staging contrast-enhanced CT (ceCT) scans of EOC patients with OM from 6 institutions and to test its utility in recurrence detection. …”