5 research outputs found
Trustworthy Representation Learning Across Domains
As AI systems have obtained significant performance to be deployed widely in
our daily live and human society, people both enjoy the benefits brought by
these technologies and suffer many social issues induced by these systems. To
make AI systems good enough and trustworthy, plenty of researches have been
done to build guidelines for trustworthy AI systems. Machine learning is one of
the most important parts for AI systems and representation learning is the
fundamental technology in machine learning. How to make the representation
learning trustworthy in real-world application, e.g., cross domain scenarios,
is very valuable and necessary for both machine learning and AI system fields.
Inspired by the concepts in trustworthy AI, we proposed the first trustworthy
representation learning across domains framework which includes four concepts,
i.e, robustness, privacy, fairness, and explainability, to give a comprehensive
literature review on this research direction. Specifically, we first introduce
the details of the proposed trustworthy framework for representation learning
across domains. Second, we provide basic notions and comprehensively summarize
existing methods for the trustworthy framework from four concepts. Finally, we
conclude this survey with insights and discussions on future research
directions.Comment: 38 pages, 15 figure
Study of Osteoarthritis-Related Hub Genes Based on Bioinformatics Analysis
Osteoarthritis (OA) is a common cause of morbidity and disability worldwide. However, the pathogenesis of OA is unclear. Therefore, this study was conducted to characterize the pathogenesis and implicated genes of OA. The gene expression profiles of GSE82107 and GSE55235 were downloaded from the Gene Expression Omnibus database. Altogether, 173 differentially expressed genes including 68 upregulated genes and 105 downregulated genes in patients with OA were selected based on the criteria of ∣log fold‐change∣>1 and an adjusted p value < 0.05. Protein-protein interaction network analysis showed that FN1, COL1A1, IGF1, SPP1, TIMP1, BGN, COL5A1, MMP13, CLU, and SDC1 are the top ten genes most closely related to OA. Quantitative reverse transcription-polymerase chain reaction showed that the expression levels of COL1A1, COL5A1, TIMP1, MMP13, and SDC1 were significantly increased in OA. This study provides clues for the molecular mechanism and specific biomarkers of OA