Data Sheet 1_Multi-omics derivation of a core gene signature for predicting therapeutic response and characterizing immune dysregulation in inflammatory bowel disease.csv by Mingming Wang (394106)

“…</p>Methods<p>We performed multi-omics analyses of GEO datasets using machine learning algorithms (LASSO/Random Forest) to derive a four-gene signature. …”

Image 1_Multi-omics derivation of a core gene signature for predicting therapeutic response and characterizing immune dysregulation in inflammatory bowel disease.jpeg by Mingming Wang (394106)

“…</p>Methods<p>We performed multi-omics analyses of GEO datasets using machine learning algorithms (LASSO/Random Forest) to derive a four-gene signature. …”

Table 1_The joint role of the immune microenvironment and N7-methylguanosine for prognosis prediction and targeted therapy in acute myeloid leukemia.xlsx by Zhixiang Chen (23839)

“…</p>Methods<p>Immune score for the Cancer Genome Atlas acute myeloid leukemia (AML) patients were calculated using the ESTIMATE algorithm, followed by identification of immune score-associated differentially expressed genes Non-negative matrix factorization (NMF) clustering was performed to stratify AML subtypes based on immune microenvironment (immune microenvironment)-related DEGs and 29 m7G regulatory genes. …”

Image 3_The joint role of the immune microenvironment and N7-methylguanosine for prognosis prediction and targeted therapy in acute myeloid leukemia.tif by Zhixiang Chen (23839)

“…</p>Methods<p>Immune score for the Cancer Genome Atlas acute myeloid leukemia (AML) patients were calculated using the ESTIMATE algorithm, followed by identification of immune score-associated differentially expressed genes Non-negative matrix factorization (NMF) clustering was performed to stratify AML subtypes based on immune microenvironment (immune microenvironment)-related DEGs and 29 m7G regulatory genes. …”

Image 2_The joint role of the immune microenvironment and N7-methylguanosine for prognosis prediction and targeted therapy in acute myeloid leukemia.tif by Zhixiang Chen (23839)

“…</p>Methods<p>Immune score for the Cancer Genome Atlas acute myeloid leukemia (AML) patients were calculated using the ESTIMATE algorithm, followed by identification of immune score-associated differentially expressed genes Non-negative matrix factorization (NMF) clustering was performed to stratify AML subtypes based on immune microenvironment (immune microenvironment)-related DEGs and 29 m7G regulatory genes. …”

Image 1_The joint role of the immune microenvironment and N7-methylguanosine for prognosis prediction and targeted therapy in acute myeloid leukemia.tif by Zhixiang Chen (23839)

“…</p>Methods<p>Immune score for the Cancer Genome Atlas acute myeloid leukemia (AML) patients were calculated using the ESTIMATE algorithm, followed by identification of immune score-associated differentially expressed genes Non-negative matrix factorization (NMF) clustering was performed to stratify AML subtypes based on immune microenvironment (immune microenvironment)-related DEGs and 29 m7G regulatory genes. …”

Data used to drive the Double Layer Carbon Model in the Qinling Mountains. by Huiwen Li (17705280)

“…The DLCM defines four soil carbon pools, categorized based on their location within the soil profile and their decomposition rates. …”

Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) by peng xin (21382394)

“…Using 80% of the data for training and 20% for testing, we employed 5-fold cross-validation to determine the feature subset that maximized the average R², ensuring optimal model performance. Additionally, a Genetic Algorithm (GA) was applied in each iteration to optimize the hyperparameters of the XGBoost model, which is crucial for enhancing both the efficiency and robustness of the model (Zhong and Liu, 2024; Zou et al., 2024). …”

Table_1_Development and validation of a multivariable nomogram predictive of hepatitis B e antigen seroconversion after pregnancy in hepatitis B virus-infected mothers.DOCX by Wenting Zhong (11460046)

“…</p>Conclusion<p>We developed a nomogram based on prenatal and pregnant factors to estimate HBeAg seroconversion after delivery in women. …”

SBDI Sativa curated 16S GTDB database by Daniel Lundin (11036598)

“…Subsequently, branch lengths for the tree are optimized based on the original alignment of 16S sequences using IQTREE [Nguyen et al. 2015] with a GTR+F+I+G4 model. …”

Data Sheet 1_A machine learning model for predicting the risk of diabetic nephropathy in individuals with type 2 diabetes mellitus.docx by Tingting Li (10553)

“…</p>Conclusions<p>The developed XGBoost model demonstrated optimal predictive accuracy for the occurrence of DKD in patients with T2DM. …”

Compack3D: Accelerating High-Order Compact Scheme Simulations by Sanjiva Lele (20503766)

“…Kernel performance and communication between distributed memory partitions are optimized based on the improved code implementation and design of parallel algorithms enabled by the mathematical properties of the linear system solution approach. …”

Table 1_Risk prediction for gastrointestinal bleeding in pediatric Henoch-Schönlein purpura using an interpretable transformer model.doc by Gahao Chen (21688843)

“…GI complications were stratified into three severity tiers: 1) no complications, 2) abdominal pain without bleeding), and 3) documented rectal bleeding or hemorrhage, based on standardized diagnostic criteria. Five machine learning algorithms (Random Forest, XGBoost, LightGBM, CatBoost, and TabPFN-V2) were optimized through nested cross-validation. …”

Data Sheet 1_Migraine triggers, phases, and classification using machine learning models.csv by Anusha Reddy (21297878)

“…This study will only consider using these methods for diagnostic purposes. Models based on these algorithms are then trained using the dataset, which includes a compilation of the types of migraine experienced by various patients. …”

Data Sheet 1_Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy.pdf by Xiaote Zhang (21570542)

“…</p>Objective<p>This study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.…”

CSSI Elements: Multi-GPU and Network Modeling and Simulation in SST by Kishwar Ahmed (21792083)

“…This AI-based model will leverage machine learning algorithms to optimize the performance of the GPU interconnection network, thereby improving the overall efficiency and speed of the simulation framework.…”

An explainable machine learning model in predicting vaginal birth after cesarean section by Ming Yang (109148)

“…Cervical Bishop score and interpregnancy interval showed the greatest impact on successful vaginal birth, according to SHAP results.</p> <p>Models based on ML algorithms can be used to predict VBAC. …”

Table 1_Machine learning prediction of post-CABG atrial fibrillation using clinical and pharmacogenomic biomarkers.docx by Lei Hua (465535)

“…Eight machine learning algorithms were trained using clinical variables and genetic variants. …”

Figures and Tables by Divya C D (22799186)

“…Robots Comput. Vision XXXI: Algorithms and Techniques, Burlingame, CA, USA, Jan. 23–24, 2012.…”

Data Sheet 1_Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients—Machine learning models for clinical decision support.do... by Yuanguo Xiong (20135991)

“…Thirteen distinct machine learning (ML) algorithms were trained and benchmarked to construct an optimal risk stratification framework. …”