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Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology

Thomas Hu, Mayar Allam, Shuangyi Cai, Walter Henderson et al. • Nature Communications (2023)

Abstract Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite pr...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41467-023-43917-5

Physics-informed generative neural network: an application to troposphere temperature prediction

Zhihao Chen, Jie Gao, Weikai Wang, Zheng Yan • Environmental Research Letters (2021)

The troposphere is one of the atmospheric layers where most weather phenomena occur. Temperature variations in the troposphere, especially at 500 hPa, a typical level of the middle troposphere, are significant indicators of future weather c...

DOAJ Open Access (DOAJ) DOI: 10.1088/1748-9326/abfde9

Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks

Xiaozheng Li, Huazhen Wang, Huixin He, Jixiang Du et al. • BMC Bioinformatics (2019)

Abstract Background Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural n...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12859-019-2617-8

Prediction of lymph node metastasis in papillary thyroid carcinoma using non-contrast CT-based radiomics and deep learning with thyroid lobe segmentation: A dual-center study

Hao Wang, Xuan Wang, Yusheng Du, You Wang et al. • European Journal of Radiology Open (2025)

Objectives: This study aimed to develop a predictive model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients by deep learning radiomic (DLRad) and clinical features. Methods: This study included 271 thyroid lobes...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.ejro.2025.100639

Deep learning for cardiovascular medicine: a practical primer

Chayakrit Krittanawong, Kipp W. Johnson, Robert S. Rosenson, Zhen Wang et al. • European Heart Journal (2019)

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via...

OPENALEX Open Access DOI: https://doi.org/10.1093/eurheartj/ehz056 PMC: 6600129

Medical image segmentation using deep learning: A survey

Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang et al. • IET Image Processing (2022)

Abstract Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation us...

OPENALEX cc-by DOI: https://doi.org/10.1049/ipr2.12419

Deep Learning for Massive MIMO CSI Feedback

Chao-Kai Wen, Wan-Ting Shih, Shi Jin • IEEE Wireless Communications Letters (2018)

In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, suc...

OPENALEX Open Access DOI: https://doi.org/10.1109/lwc.2018.2818160

Deep Learning in Medical Imaging: General Overview

June‐Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee et al. • Korean Journal of Radiology (2017)

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishin...

OPENALEX cc-by-nc DOI: https://doi.org/10.3348/kjr.2017.18.4.570 PMC: 5447633