Human symptoms–disease network

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Transfer learning for biomedical named entity recognition with neural networks (2018, background): “Text-mining has already proven useful for many types of large-scale biomedical data analysis, such as network biology (Zhou et al., 2014; Al-Aamri et al., 2017), gene prioritization (Aerts et al.”
Deep learning with word embeddings improves biomedical named entity recognition (2017, methodology): “Text mining is an important tool for many types of large-scale biomedical data analysis, such as network biology (Zhou et al., 2014), gene prioritization (Aerts et al., 2006), drug repositioning (Wang and Zhang, 2013) or creation of curated databases (Li et al., 2015).”
Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine (2023, background): “From a macro perspective,by analyzing the network topology similarity between disease-related phenotypes and syndrome-related clinical features,the relationships between diseases and syndromes can be revealed [60, 90].”
Systematic integration of biomedical knowledge prioritizes drugs for repurposing (2017, background): “, 2016), disease symptoms (Zhou et al., 2014), and molecular pathways (Pratanwanich and Lió, 2014).”