About Xi Peng Xi Peng Ph.D. Student (former), Computer Science machine learning Knowledge representation and reasoning Xi Peng likes pop music and doing sports. He graduated from SUSTech in 2021 and planning to continue his academic career at KAUST under the supervision of Professor Robert Hoehndorf. "He says KAUST has the best environment to do research. Life in KAUST is also comfortable. I believe I can achieve a lot in KAUST". Research Interest applications of machine learning and logic integration in biology Education Profile B.Sc, Computer Science, Southern University of Science and Technology, Shenzhen, China, 2017-2021 M.Sc, Computer Science, King Abdullah University of Science and Technology, Thuwal Projects Related Projects 2021 IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Fri, Jan 1 2021 - Sun, Dec 31 2023 Applied Ontology Neuro-Symbolic AI Rare disease Biological measurements are inherently high-dimensional and heterogeneous: omics platforms produce thousands to millions of features per individual, and they coexist with qualitative information such as diagnoses, phenotype calls, and prescriptions. Biomedical ontologies and knowledge graphs encode rich qualitative background knowledge, but they are largely disconnected from the quantitative measurements that gave rise to the categorical phenotypes in the first place. Conversely, graph neural networks and other methods that handle quantitative data on graphs do not yet exploit the formal
IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Fri, Jan 1 2021 - Sun, Dec 31 2023 Applied Ontology Neuro-Symbolic AI Rare disease Biological measurements are inherently high-dimensional and heterogeneous: omics platforms produce thousands to millions of features per individual, and they coexist with qualitative information such as diagnoses, phenotype calls, and prescriptions. Biomedical ontologies and knowledge graphs encode rich qualitative background knowledge, but they are largely disconnected from the quantitative measurements that gave rise to the categorical phenotypes in the first place. Conversely, graph neural networks and other methods that handle quantitative data on graphs do not yet exploit the formal
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