Table 2_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... by Yun Zhao (48978)

“…Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …”

Image 3_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... by Yun Zhao (48978)

“…Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …”

Table 1_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... by Yun Zhao (48978)

“…Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …”

Image 1_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... by Yun Zhao (48978)

“…Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …”

Data Sheet 1_Integrating groundwater pumping data with regression-enhanced random forest models to improve groundwater monitoring and management in a coastal region.pdf by Jamie Kim (14893220)

Data Sheet 1_Quantifying the spatial patterns of retinal ganglion cell loss and progression in optic neuropathy by applying a deep learning variational autoencoder approach to opti... by Jui-Kai Wang (15426571)

Foot posture examples from personal collections – a) Barbie Made to Move (equinus), b) Barbie Fashionista #210 (flat), c) Fashionistas Barbie heart DownSyndrome (Assistive technolo... by Cylie Williams (3446018)

Data Sheet 1_Landscape analysis of m6A modification regulators reveals LRPPRC as a key modulator in tubule cells for DKD: a multi-omics study.docx by Li Jiang (120930)

“…</p>Aim<p>This study aimed to investigate the specific expression patterns of the m6A geneset in the pathogenesis of DKD.</p>Method<p>Bulk RNA, single-cell and spatial transcriptome were utilized to clarify the hub gene. 3 types of machine learning algorithms were applied. …”

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by Ayush Garg (21090944)

“…But to the best of our knowledge, an in-depth analysis of the performance of these techniques against the class ratio is not available in the literature. We have addressed these shortcomings in this study and have performed a detailed analysis of the performance of four different techniques to address imbalanced class distribution using machine learning (ML) methods and AutoML tools. …”

Graphical representation of the optimization process in machine learning, focusing on the adjustment of weights during gradient descent. by Liu Liu (512237)

Raw data. by Changzhi Liu (518454)

“…The participants were divided into low and high groups for RC, and the RC-to-cholesterol ratio was based on the median values. Vertebral fractures were assessed via the Genant semiquantitative classification system on CT-reconstructed sagittal images.…”

Baseline characteristics of the subjects. by Changzhi Liu (518454)

“…The participants were divided into low and high groups for RC, and the RC-to-cholesterol ratio was based on the median values. Vertebral fractures were assessed via the Genant semiquantitative classification system on CT-reconstructed sagittal images.…”

Data Sheet 1_Application of machine learning based on habitat imaging and vision transformer to predict treatment response of locally advanced esophageal squamous cell carcinoma fo... by Shu-Han Xie (17902661)

“…This study aimed to develop a machine learning model integrating habitat imaging and deep learning (DL) to predict the treatment response of ESCC patients to nICT.…”

Supplementary Material for: First European Experience of Shape-Sensing Robotic-assisted bronchoscopy: Learning Curve Analysis by figshare admin karger (2628495)

“…Conclusion: Competence in ssRAB can be achieved quickly, and procedure times decrease after a few cases. However, as learning curves vary between proceduralists, a sufficient number of cases should be considered to achieve ssRAB proficiency. …”

Data Sheet 1_Identification of ALDH2 as a novel target for the treatment of acute kidney injury in kidney transplantation based on WGCNA and machine learning algorithms and explora... by Jinpu Peng (748597)

“…Next, we intersected the key genes identified by three types of machine learning, namely, Random Forest, LASSO regression analysis and SVM, and obtained a total of 1 intersected gene as ALDH2, which we used as a key gene in kidney transplantation AKI. …”

Infrared Spectroscopy Coupled with Machine Learning Algorithms to Investigate Vascular Dysfunction in Ovariectomy: An Animal Model Study by Tháfanys S. Travezani (20609398)

Prospective contingency explains behavior and dopamine signals during associative learning, Qian et al., 2025 by Mark Burrell (20566502)

“…We show that both anticipatory licking and dopamine responses to a conditioned stimulus decreased when additional rewards were delivered uncued, but remained unchanged if additional rewards were cued. …”

Table 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 4_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 5_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”