Data Sheet 4_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv by Xiuping Xuan (8686266)

Data Sheet 6_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv by Xiuping Xuan (8686266)

Data Sheet 3_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv by Xiuping Xuan (8686266)

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

Data Sheet 5_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv by Xiuping Xuan (8686266)

Data Sheet 1_Grammar-constrained decoding for structured information extraction with fine-tuned generative models applied to clinical trial abstracts.pdf by David M. Schmidt (20517821)

<b>Ensemble learning model identifies </b><b>adaptation classification and turning points</b><b> of river microbial communities in response to heatwaves</b> by Qian Qu (14198684)

“…<a href="" target="_blank">However, how river microbial communities respond to</a> heatwaves and whether and how high temperatures exceed microbial adaptation remain unclear. In this study, we proposed four types of pulse temperature-induced <a href="" target="_blank">microbial responses</a> and predicted the possibility of microbial adaptation to high temperature in global rivers using ensemble machine learning models. …”

Terrestrial Condition Assessment (TCA) Grassland Productivity by U.S. Forest Service (17476914)

Data Sheet 2_Integration of multi-omics and machine learning strategies identifies immune related candidate biomarkers in inflammation-associated hypertrophic cardiomyopathy.csv by Qingzhu Liang (22315633)

“…MR analysis was performed on 19,942 expression quantitative trait loci (eQTLs) and HCM cases using the TwoSampleMR package. …”

Data Sheet 5_Integration of multi-omics and machine learning strategies identifies immune related candidate biomarkers in inflammation-associated hypertrophic cardiomyopathy.csv by Qingzhu Liang (22315633)

“…MR analysis was performed on 19,942 expression quantitative trait loci (eQTLs) and HCM cases using the TwoSampleMR package. …”

Data Sheet 4_Integration of multi-omics and machine learning strategies identifies immune related candidate biomarkers in inflammation-associated hypertrophic cardiomyopathy.csv by Qingzhu Liang (22315633)

“…MR analysis was performed on 19,942 expression quantitative trait loci (eQTLs) and HCM cases using the TwoSampleMR package. …”

Data Sheet 1_Integration of multi-omics and machine learning strategies identifies immune related candidate biomarkers in inflammation-associated hypertrophic cardiomyopathy.csv by Qingzhu Liang (22315633)

“…MR analysis was performed on 19,942 expression quantitative trait loci (eQTLs) and HCM cases using the TwoSampleMR package. …”

Data Sheet 3_Integration of multi-omics and machine learning strategies identifies immune related candidate biomarkers in inflammation-associated hypertrophic cardiomyopathy.csv by Qingzhu Liang (22315633)

“…MR analysis was performed on 19,942 expression quantitative trait loci (eQTLs) and HCM cases using the TwoSampleMR package. …”

Baseline characteristics of the participants. by Junichi Kushioka (12236447)

“…Although diagnosing LS using standardized charts is straightforward, the labor-intensive and time-consuming nature of the process limits its widespread implementation. To address this, we introduced a Deep Learning (DL)-based computer vision model that employs OpenPose for pose estimation and MS-G3D for spatial-temporal graph analysis. …”

Internal validation by cross-validation. by Junichi Kushioka (12236447)

“…Although diagnosing LS using standardized charts is straightforward, the labor-intensive and time-consuming nature of the process limits its widespread implementation. To address this, we introduced a Deep Learning (DL)-based computer vision model that employs OpenPose for pose estimation and MS-G3D for spatial-temporal graph analysis. …”

Data Sheet 1_Investigating neural markers of Alzheimer's disease in posttraumatic stress disorder using machine learning algorithms and magnetic resonance imaging.pdf by Gabriella Yakemow (20137758)

“…Mean cerebral blood flow (CBF) and gray matter (GM) volume were compared between groups. Additionally, we utilized two previously established machine learning-based algorithms, one representing AD-like brain activity (Machine learning-based AD Designation [MAD]) and the other focused on AD-like brain structural changes (AD-like Brain Structure [ABS]). …”

Comparison between AL and randomly selected data. by Jia Li (160557)

“…This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Framework of MsHop. by Jia Li (160557)

“…This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Results of ablation study. by Jia Li (160557)

“…This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Kappa consistency ranges. by Jia Li (160557)

“…This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”