The diagnosis and treatment of traditional Chinese medicine (TCM) are formed based on the differentiation of syndromes and symptoms. Symptom management is always the core task of nursing science. Connotation between TCM symptoms and Modern medicine (MM) symptoms are obvious different, especially tongue and pulse symptoms of TCM. However, the underlying molecular mechanisms of most TCM symptoms remain unclear. Here, we developed a network-based framework to predict candidate genes of TCM symptoms (called PTsGene) and construction a high-quality set of TCM symptom-gene associations. Experimental results indicated that PTsGene performed significantly better than the baseline algorithms. The reliability of the candidate genes of symptoms (containing one of typical symptoms of COVID-19, fever) were validated by the analysis of functional homogeneity, molecular co-expression, and recently published literatures. Finally, a high-quality set of TCM symptom-gene associations is constructed to promote the mechanism developments of TCM symptoms. Prediction and construction for reliable TCM symptom-gene associations are valuable for uncovering the underlying molecular mechanisms of TCM symptoms. Our TCM symptom-gene associations deliver a highly insightful data sources for researchers both from basic and clinical settings of precision healthcare.
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Mt Sinai Sch Med, Dept Med, Div Liver Dis, Mt Sinai Liver Canc Program, New York, NY 10029 USA
Hosp Valle De Hebron, Dept Internal Med, Liver Unit, Barcelona, Spain
Inst Carlos III, CIBEREHD, Madrid, SpainMt Sinai Sch Med, Dept Med, Div Liver Dis, Mt Sinai Liver Canc Program, New York, NY 10029 USA
Minguez, Beatriz
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Sia, Daniela
Llovet, Josep Maria
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机构:
Mt Sinai Sch Med, Dept Med, Div Liver Dis, Mt Sinai Liver Canc Program, New York, NY 10029 USA
Inst Carlos III, CIBEREHD, Madrid, Spain
Hosp Clin Barcelona, Barcelona Clin Liver Canc Grp, HCC Translat Res Lab, IDIBAPS,Liver Unit, Barcelona, Spain
ICREA, Barcelona, SpainMt Sinai Sch Med, Dept Med, Div Liver Dis, Mt Sinai Liver Canc Program, New York, NY 10029 USA