DataSheet1_Predicting the solubility of CO2 and N2 in ionic liquids based on COSMO-RS and machine learning.docx by Hongling Qin (5557505)

“…To further improve the performance of COSMO-RS, two options were used, i.e., the polynomial expression to correct the COSMO-RS results and the combination of COSMO-RS and machine learning algorithms (eXtreme Gradient Boosting, XGBoost) to develop a hybrid model. …”

Supplementary file 1_Explainable machine learning model predicts response to adjuvant therapy after radical cystectomy in bladder cancer.docx by Jian Hou (93442)

“…Decision curve analysis showed favorable net benefit within a moderate-risk threshold.</p>Conclusions<p>A machine learning model integrating pathological, demographic, and molecular features demonstrates promising potential to predict response to adjuvant therapy post-RC in bladder cancer. …”

Female regression coefficients for regression #3 across development in set size = 2 and set size = 4. by Juliana Chase (20469427)

Strategy parameters across development for female mice in set size = 2 and set size = 4 from winning computational model. by Juliana Chase (20469427)

“…However, female mice had a significant decrease in parameter S1 “Inappropriate Lose-Shift” in set size = 4 (H: <i>p</i> = 0.04) with a trend in a similar direction in set size = 2 (C: <i>p</i> = 0.08).…”

Sentiment Trends in Antidepressant. by Min Ho An (7894658)

TENS inhibited inflammation of CFL injury rats by regulating the IL-1 by Min Miao (142697)

Groups of clustered topics and sentiment. by Min Ho An (7894658)

Gene expression omnibus datasets. by Xinyi Xia (7516694)

“…TCMR hub genes, guanylate-binding protein 1 (GBP1) and CD69, showed increased expression. Decreased survival rates were found in patients who had undergone KT and had high GBP1 and CD69 levels. …”

Effect of session on winning model parameters for set size = 2 and set size = 4 for both male and females. by Juliana Chase (20469427)

Antidepressant related posts and comments over time. by Min Ho An (7894658)

AI driven topic clustering and trend analysis workflow. by Min Ho An (7894658)

Strategy parameters across development for male mice in set size = 2 and set size = 4 from winning computational model. by Juliana Chase (20469427)

“…<p>In order to understand the relationship between age and the parameters from our winning model (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012667#pcbi.1012667.g006" target="_blank">Fig 6</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012667#sec009" target="_blank">Materials and methods</a> for model details), we looked at whether parameter weight (y-axis) would change over development (x-axis) for male mice in set size = 2 (A-E) and set size = 4 (F-J). …”

<b> </b> Spatial memory assessment in Morris water maze test. 5A: Effect of different treatments on time spent in the target zone. Alcl3 significantly decreased the time spent in t... by Atefeh Sadeghi (21253051)

A random forest model was trained to classify <i>Blastocystis</i> colonization status. by Sergio Castañeda (9531501)

Data Sheet 1_Unveiling spatiotemporal evolution and driving factors of ecosystem service value: interpretable HGB-SHAP machine learning model.docx by Xiangming Xu (420946)

“…The ESV exhibited a slight increase in two counties, while it demonstrated a decrease in the remaining 16 counties at the county scale. …”

Data Sheet 2_Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation.zip by Yimin Zhou (3857113)

“…Four candidate genes (SLC31A1, DBT, DLST, LIAS) were obtained from the machine learning models, with SLC31A1 performing best (AUC = 0.958). …”

Data Sheet 1_Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation.zip by Yimin Zhou (3857113)

“…Four candidate genes (SLC31A1, DBT, DLST, LIAS) were obtained from the machine learning models, with SLC31A1 performing best (AUC = 0.958). …”

Table 1_Analysis and validation of biomarkers and immune cell infiltration profiles in unstable coronary atherosclerotic plaques using bioinformatics and machine learning.xlsx by Pengyue Jin (11657894)

“…</p>Methods<p>The datasets GSE163154 and GSE111782, obtained from the gene expression omnibus (GEO) database, were amalgamated for bioinformatics analysis, using the dataset GSE43292 as a test set. Sequentially, we performed principal component analysis (PCA), differential gene expression analysis, enrichment analysis, weighted gene co-expression network analysis (WGCNA), utilized a machine learning algorithm to screen key genes, conducted receiver operating characteristic (ROC) curve analysis and nomogram model to assess biomarker diagnostic efficacy, validated the biomarkers, and analyzed immune cell infiltration.…”

Data Sheet 1_Diagnostic classification of mild cognitive impairment in Parkinson's disease using subject-level stratified machine-learning analysis.pdf by Jing Wang (6206297)

“…Background<p>The timely identification of mild cognitive impairment (MCI) in Parkinson's disease (PD) is essential for early intervention and clinical management, yet it remains a challenge in practice.</p>Methods<p>We conducted an analysis of 3,154 clinical visits from 896 participants in the Parkinson's Progression Markers Initiative (PPMI) cohort. …”

Data Sheet 1_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv by Xiuping Xuan (8686266)